Academic literature on the topic 'Drugs Prescribing Data processing'
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Journal articles on the topic "Drugs Prescribing Data processing"
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Colling, Craig, Christoph Mueller, Gayan Perera, Nicola Funnell, Justin Sauer, Daniel Harwood, Robert Stewart, and Delia Bishara. "‘Real time’ monitoring of antipsychotic prescribing in patients with dementia: a study using the Clinical Record Interactive Search (CRIS) platform to enhance safer prescribing." BMJ Open Quality 9, no. 1 (March 2020): e000778. http://dx.doi.org/10.1136/bmjoq-2019-000778.
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BackgroundThe use of antipsychotic drugs in dementia has been reported to be associated with increased risk of cerebrovascular events and mortality. There is an international drive to reduce the use of these agents in patients with dementia and to improve the safety of prescribing and monitoring in this area.ObjectivesThe aim of this project was to use enhanced automated regular feedback of information from electronic health records to improve the quality of antipsychotic prescribing and monitoring in people with dementia.MethodsThe South London and Maudsley NHS Foundation Trust (SLaM) incorporated antipsychotic monitoring forms into its electronic health records. The SLaM Clinical Record Interactive Search (CRIS) platform provides researcher access to de-identified health records, and natural language processing is used in CRIS to derive structured data from unstructured free text, including recorded diagnoses and medication. Algorithms were thus developed to ascertain patients with dementia receiving antipsychotic treatment and to determine whether monitoring forms had been completed. We used two improvement plan-do-study-act cycles to improve the accuracy of the algorithm for automated evaluation and provided monthly feedback on team performance.ResultsA steady increase in antipsychotic monitoring form completion was observed across the study period. The percentage of our sample with a completed antipsychotic monitoring form more than doubled from October 2017 (22%) to January 2019 (58%).Conclusion‘Real time’ monitoring and regular feedback to teams offer a time-effective approach, complementary to standard audit methods, to enhance the safer prescribing of high risk drugs.
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Walker, Lauren E., Aseel S. Abuzour, Danushka Bollegala, Andrew Clegg, Mark Gabbay, Alan Griffiths, Cecil Kullu, et al. "The DynAIRx Project Protocol: Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity." Journal of Multimorbidity and Comorbidity 12 (January 2022): 263355652211454. http://dx.doi.org/10.1177/26335565221145493.
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Background Structured Medication Reviews (SMRs) are intended to help deliver the NHS Long Term Plan for medicines optimisation in people living with multiple long-term conditions and polypharmacy. It is challenging to gather the information needed for these reviews due to poor integration of health records across providers and there is little guidance on how to identify those patients most urgently requiring review. Objective To extract information from scattered clinical records on how health and medications change over time, apply interpretable artificial intelligence (AI) approaches to predict risks of poor outcomes and overlay this information on care records to inform SMRs. We will pilot this approach in primary care prescribing audit and feedback systems, and co-design future medicines optimisation decision support systems. Design DynAIRx will target potentially problematic polypharmacy in three key multimorbidity groups, namely, people with (a) mental and physical health problems, (b) four or more long-term conditions taking ten or more drugs and (c) older age and frailty. Structured clinical data will be drawn from integrated care records (general practice, hospital, and social care) covering an ∼11m population supplemented with Natural Language Processing (NLP) of unstructured clinical text. AI systems will be trained to identify patterns of conditions, medications, tests, and clinical contacts preceding adverse events in order to identify individuals who might benefit most from an SMR. Discussion By implementing and evaluating an AI-augmented visualisation of care records in an existing prescribing audit and feedback system we will create a learning system for medicines optimisation, co-designed throughout with end-users and patients.
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Bizuneh, Gizachew Kassahun, Betelhem Anteneh Adamu, Getenet Tadege Bizuayehu, and Solomon Debebe Adane. "A Prospective Observational Study of Drug Therapy Problems in Pediatric Ward of a Referral Hospital, Northeastern Ethiopia." International Journal of Pediatrics 2020 (March 21, 2020): 1–6. http://dx.doi.org/10.1155/2020/4323189.
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Background. A drug therapy problem is any undesirable event experienced by a patient during drug therapy that interferes with achieving the desired goals of therapy. It has been pointed out that hospitalized pediatric patients are particularly prone to drug-related problems. Identifying drug therapy problems enables risk quantification and determination of the potential impact of prevention strategies. The purpose of this study was to assess the drug therapy problems in a pediatric ward of Dessie Referral Hospital, northeast of Ethiopia, and to identify associated factors for drug therapy problems. Methods. A prospective observational study design was carried out to assess drug therapy problems in a pediatric ward of Dessie Referral Hospital from February 1, 2018, to May 30, 2018. Ethical approval was obtained, and informed consent was signed by each study participant’s parent before the commencement of the study. All patients admitted to the ward during the study period were included in the study. Data was collected by trained pharmacy staffs through medical record reviews of patients using a prepared standard checklist and semistructured questionnaire. The collected data were cleared and checked every day for completeness and consistency before processing. Data were entered, and descriptive statistical analysis was done using SPSS Version 20 Software. A P value of less than 0.05 was considered significant. Results. The participants’ mean age was 2.32 years with the standard deviation (SD) of 0.76 years. Among 81 patients, 71 (87.7%) of them had at least one drug therapy problem per patient which indicates that the prevalence of the drug therapy problem was substantially high. Needs additional drug was the most predominantly encountered drug therapy problem accounted (30 (25.2%)). On the other hand, ineffective drug was the least (3 (2.5%)) drug therapy problem. Antibiotics (47 (39.5%)) followed by fluid and electrolyte (25 (21%)) were classes of drugs mostly involved in the drug therapy problem. The main risk factors reported to the occurrence of the drug therapy problems were prescribing and dose calculation errors. Conclusion. The present study revealed that majority of the patients had at least one DTP per patient; this indicates that prevalence of DTP was very high in the study area. Needs additional drug therapy followed by noncompliance was the major cause of the occurrence of DTP. Antibiotics were the main class of drugs involved in the drug therapy problem, and among the risk factors assessed, prescribing and dose calculation errors showed statistical significance.
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Herigon, Joshua C., Amir Kimia, and Marvin Harper. "1358. Using natural language processing to optimize case ascertainment of acute otitis media in a large, state-wide pediatric practice network." Open Forum Infectious Diseases 7, Supplement_1 (October 1, 2020): S690—S691. http://dx.doi.org/10.1093/ofid/ofaa439.1540.
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Abstract Background Antibiotics are the most commonly prescribed drugs for children and frequently inappropriately prescribed. Outpatient antimicrobial stewardship interventions aim to reduce inappropriate antibiotic use. Previous work has relied on diagnosis coding for case identification which may be inaccurate. In this study, we sought to develop automated methods for analyzing note text to identify cases of acute otitis media (AOM) based on clinical documentation. Methods We conducted a cross-sectional retrospective chart review and sampled encounters from 7/1/2018 – 6/30/2019 for patients < 5 years old presenting for a problem-focused visit. Complete note text and limited structured data were extracted for 12 randomly selected weekdays (one from each month during the study period). An additional weekday was randomly selected for validation. The primary outcome was correctly identifying encounters where AOM was present. Human review was considered the “gold standard” and was compared to ICD codes, a natural language processing (NLP) model, and a recursive partitioning (RP) model. Results A total of 2,724 encounters were included in the training cohort and 793 in the validation cohort. ICD codes and NLP had good performance overall with sensitivity 91.2% and 93.1% respectively in the training cohort. However, NLP had a significant drop-off in performance in the validation cohort (sensitivity: 83.4%). The RP model had the highest sensitivity (97.2% training cohort; 94.1% validation cohort) out of the 3 methods. Figure 1. Details of encounters included in the training and validation cohorts. Table 1. Performance of ICD coding, a natural language processing (NLP) model, and a recursive partitioning (RP) model for identifying cases of acute otitis media (AOM) Conclusion Natural language processing of outpatient pediatric visit documentation can be used successfully to create models accurately identifying cases of AOM based on clinical documentation. Combining NLP and structured data can improve automated case detection, leading to more accurate assessment of antibiotic prescribing practices. These techniques may be valuable in optimizing outpatient antimicrobial stewardship efforts. Disclosures All Authors: No reported disclosures
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Gumułka, Paweł, Joanna Żandarek, Monika Dąbrowska, and Małgorzata Starek. "UPLC Technique in Pharmacy—An Important Tool of the Modern Analyst." Processes 10, no. 12 (November 24, 2022): 2498. http://dx.doi.org/10.3390/pr10122498.
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In recent years, ultra-efficient liquid chromatography (UPLC) has gained particular popularity due to the possibility of faster separation of small molecules. This technique, used to separate the ingredients present in multi-component mixtures, has found application in many fields, such as chemistry, pharmacy, food, and biochemistry. It is an important tool in both research and production. UPLC created new possibilities for analytical separation without reducing the quality of the obtained results. This technique is therefore a milestone in liquid chromatography. Thanks to the increased resolution, new analytical procedures, in many cases, based on existing methods, are being developed, eliminating the need for re-analysis. Researchers are trying to modify and transfer the analytical conditions from the commonly used HPLC method to UPLC. This topic may be of strategic importance in the analysis of medicinal substances. The information contained in this manuscript indicates the importance of the UPLC technique in drug analysis. The information gathered highlights the importance of selecting the appropriate drug control tools. We focused on drugs commonly used in medicine that belong to various pharmacological groups. Rational prescribing based on clinical pharmacology is essential if the right drug is to be administered to the right patient at the right time. The presented data is to assist the analyst in the field of broadly understood quality control, which is very important, especially for human health and treatment. This manuscript shows that the UPLC technique is now an increasingly used tool for assessing the quality of drugs and determining the identity and content of active substances. It also allows the monitoring of active substances and finished products during their processing and storage.
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Kinderbaeva, N. K., K. Bazira, N. M. Karabekova, R. M. Mamatova, Zh Asel, A. K. Nartaeva, and S. M. Mamatov. "Analysis of antithrombotic therapy in elderly patients with nonvalvular atrial fibrillation in the Kyrgyz Republic and ways to increase treatment adherencе." Kazan medical journal 102, no. 4 (August 8, 2021): 439–45. http://dx.doi.org/10.17816/kmj2021-439.
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Aim. To analyze anticoagulant therapy in elderly patients with non-valvular atrial fibrillation and ways to increase adherence in the work of a specialized team.
Methods. The study followed 250 patients with non-valvular atrial fibrillation aged 65 to 74 years (mean age 70.74.39 years). The patients were divided into three groups: the first group included 105 people, who were prescribed warfarin in a retrospective study; the second group 57 people treated with rivaroxaban, and the third group 88 people treated with warfarin. The second and third groups were prospective study groups which were supervised by a specialized team of physicians. The groups were matched on sex and age, comorbidities. Statistical data analysis and mathematical processing were performed by using the methods of descriptive and variational statistics. Most parameters reported as absolute values and percentages, while quantitative data the 25th and 75th percentiles.
Results. All patients included in the study had a high risk of developing thromboembolic complications by their CHA2DS2-VASc score (2) and a low risk of developing hemorrhagic complications on the HAS-BLED scale (average score 1.490.04). They were prescribed anticoagulant therapy. By the end of the year follow-up from the start of anticoagulant therapy, only 9.5% of patients were treatment adherent, in the second group 43.8%, in the third group 70.5% of patients. The reason for refusing to take warfarin in the vast majority of cases was the inability to control the international normalized ratio, medical contraindications, and the high cost of the drug in prescribing rivaroxaban. The results showed that the majority of patients with atrial fibrillation (90.5%) receive inadequate antithrombotic therapy in routine outpatient clinical practice. At the same time, in a very small number of patients (9.5%) receiving warfarin, this type of therapy can be considered adequate (60% or more of the stay time in the therapeutic range of international normalized ratio of 2.0 to 3.0).
Conclusion. Anticoagulant therapy prescription under the supervision of a specialized team contributes to a significant improvement in treatment adherence (from 43.8 to 70.5%); promising in the future is the use of drugs from the group of new oral anticoagulants that do not require routine monitoring of coagulogram.
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Лысенко, О. В. "Comparative Efficacy of Various Forms of Estrogen-Containing Drugs for Endometrial Preparation During Frozen Embryo Transfer in Protocols with Hormone Replacement Therapy." Репродуктивное здоровье. Восточная Европа, no. 3 (August 18, 2022): 356–63. http://dx.doi.org/10.34883/pi.2022.12.3.007.
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Цель. Сравнить эффективность, удобность в использовании, финансовые затраты при применении эстрогенсодержащих лекарственных средств в протоколах переноса размороженных эмбрионов.Материалы и методы. Под нашим наблюдением находилось 114 женщин репродуктивного возраста в протоколах переноса размороженных эмбрионов с заместительной гормонотерапией. Первую группу составили 29 женщин, которым назначено эстрогенсодержащее лекарственное средство в таблетках, вторую – 25 пациенток, которые использовали гель трансдермальный в тубах, третью – 31 женщина, использовавшая гель для наружного применения в саше, в четвертую группу вошли 30 пациенток, которым был назначен спрей во флаконах с насосом.Всем женщинам было произведено трансвагинальное ультразвуковое исследование в 2D-режиме при старте протокола и через 10 дней от начала применения эстрогенов.Статистическая обработка данных осуществлялась с применением программного пакета Statistica 6.0. Во всех случаях критическое значение уровня значимости принималось р<0,05 (5%).Результаты. Стартовый ультразвуковой мониторинг проводился со 2-го по 5-й день менструального цикла для исключения патологии органов малого таза и беременности. Следующий ультразвуковой мониторинг проводили на 10-й день стандартной гормонотерапии эстрогенами. Терапию считали достаточной, если толщина эндометрия достигла не менее 8 мм, но не более 14 мм. Установлено, что эффективность эстрогенсодержащих лекарственных средств с точки зрения толщины эндометрия сопоставима. Но наиболее приемлемо с точки зрения цены применение спрея во флаконах с насосом. При наименьшей стоимости одного флакона спрея при назначении эквивалентной дозы его хватает на 2 недели в сравнении с 7-8-9 днями при использовании таблеток, геля трансдермального в тубах, геля в саше.Заключение. При одинаковой эффективности с другими лекарственными средствами предпочтительно применение спрея с насосом, учитывая удобство в использовании и наименьшую цену лекарственного средства при наибольшей длительности применения. Purpose. To compare the efficiency, ease of use, and financial costs of using estrogen- containing drugs in frozen embryo transfer protocols.Materials and methods. We examined a total of 114 cases – women of reproductive age in the frozen embryo transfer protocols with hormone replacement therapy. The first group consisted of 29 women who used the estrogen-containing drug in tablets, the second – 25 patients who used the transdermal gel in tubes, the third – 31 women who used the gel for external use in sachets, the fourth group included 30 patients who used a spray in pump bottles.All women underwent 2D-transvaginal ultrasound at the start of the protocol and 10 days after the start of estrogen use.Statistical data processing was carried out using the Statistica 6.0 software package. In all cases, the critical significance level was taken as p<0.05 (5%).Results. To exclude the pathology of the pelvic organs and pregnancy starting ultrasound monitoring was carried out from the 2nd to the 5th day of the menstrual cycle. The next ultrasound monitoring was performed on the 10th day of standard estrogen hormone therapy. Therapy was considered sufficient if the thickness of the endometrium reached at least 8 mm, but not more than 14 mm. We have established that the effectiveness of estrogen-containing drugs in terms of endometrial thickness is comparable. But the most acceptable in terms of price is the use of a spray in bottles with a pump. At the lowest cost of one bottle of spray when prescribing an equivalent dose, it lasts for 2 weeks compared to 7-8-9 days when using tablets, transdermal gel in tubes, gel in a sachet.Conclusion. With the same efficiency as other drugs, it is preferable to use a spray with a pump, given the ease of use and the lowest price of the drug for the longest duration of use.
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Qaiser, Aimen, Zahra Hassan Kiani, Farina Abid, Tania Pervaiz, and Zafar Iqbal. "Evaluation of Prescribing Pattern in Mirpur Azad Kashmir using who Prescribing Indicators." Global Pharmaceutical Sciences Review V, no. I (December 30, 2020): 17–24. http://dx.doi.org/10.31703/gpsr.2020(v-i).03.
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Promotion of rational use of drugs in developing countries is necessary for improving the quality of life. Therefore, evaluation of drug use pattern using World Health Organization (WHO) indicators is necessary for assessment of rational use of drugs. 200 prescriptions were randomly collected from different pharmacies in Mirpur and evaluated to measure prescribing indicators. Data was analyzed using SPSS (version 25). Average count of drugs prescribed per prescription was 3.8 (S.D+ 2.01). Percentage of antibiotics prescribed, and injections prescribed per prescription was 42% (n=84) and 16% (n=32) respectively. The percentage of drugs prescribed from Essential Drugs List was 90.5%. It was concluded that prescribing pattern was far away from the standard WHO requirements. Greater number of drugs and overuse of antibiotics focused on close monitoring and regulation of prescribing pattern. Steps should be taken to encourage the rational use of drugs to improve the quality of life.
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Brodie, Martin J., and Ian Harrison. "Practical prescribing." Drug and Therapeutics Bulletin 25, no. 20 (October 5, 1987): 80. http://dx.doi.org/10.1136/dtb.25.20.80.
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This book is a practical manual for the prescriber rather than a text book. The first chapter usefully explains pharmacological terms which are used later in the book. This is followed by three sections concerned with choosing drugs. The first section gives a list of ‘best buys’ for common complaints, the second looks at treatment policies and the third gives basic pharmacological information to help in making choices. Side-effects and drug interactions are presented in the next two chapters in a readily accessible form. The final chapter, called ‘Cautions,’ has some useful information not readily found elsewhere including data on teratogenesis and shelf-life of formulations. It also suggests which drugs we should stop using, and discusses factors to consider before using a new drug.
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Bruck, P., C. A. Antao, and J. A. Henry. "Generic Prescribing of Antidepressants." Journal of the Royal Society of Medicine 85, no. 11 (November 1992): 682–85. http://dx.doi.org/10.1177/014107689208501109.
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Analysis of National Health Service prescription data for the antidepressants from 1980 to 1989 shows a consistent secular trend towards the increased use of generic names on prescriptions for this group of drugs. This apparently reflects national trends for all drugs, and was similar for most antidepressants. However, generic prescribing had by 1989 increased significantly more rapidly with fluvoxamine, which was introduced in 1987. The two drugs introduced in 1989, fluoxetine and amoxapine, also had a high generic prescribing rate in their year of introduction. Incrased generic prescribing may become a feature with further new drugs. However, the use of the generic name on the prescription has relatively little influence on what is dispensed to the patient. Pharmacists may dispense a brand name when given a generic prescription. Moreover, pressures on doctors to write generic names on prescriptions may have limited relevance for some drugs; generic alternatives were available for only four out of 22 antidepressants.
Dissertations / Theses on the topic "Drugs Prescribing Data processing"
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Sithole, Jabulani S. "Longitudinal data models for evaluating change in prescribing patterns." Thesis, Keele University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327702.
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Patterson, Nona L. "Providing behavioral data to physicians for enhancing medication treatment for chronically mentally disabled individuals." Scholarly Commons, 1987. https://scholarlycommons.pacific.edu/uop_etds/2140.
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Physicians are often unaware of mentally disabled outpatients' symptomatic behaviors that are relevant to their medication prescriptions. Such information is available to the clients' residential care providers . The present study trained clients' residential care providers in data collection and provided these data to physicians. It was predicted that these data would improve medication treatment for these clients and consequently lead to a decrease in client's symptomatic behaviors . The results did not confirm the predictions; the data provided to physicians on clients' between- visit behaviors had no measured effect on the physicians' treatment of these clients, although the physicians reported positive attitudes about the helpfulness and utility of ·the system .
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Hu, Lihong, and 胡麗紅. "Application of neural networks in the first principles calculations and computer-aided drug design." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B45014796.
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Tai, Hio Kuan. "Protein-ligand docking and virtual screening based on chaos-embedded particle swarm optimization algorithm." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3948431.
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"The applications of image processing in biology and relevant data analysis." 2007. http://library.cuhk.edu.hk/record=b5893361.
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Wang, Zexi.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.
Includes bibliographical references (leaves 63-64).
Abstract --- p.i
Acknowledgement --- p.iii
Chapter 0 --- Introduction --- p.1
Chapter 1 --- The Design of the Experiments --- p.4
Chapter 1.1 --- Flies and the Devices --- p.5
Chapter 1.2 --- Parameter Settings and Interested Information --- p.8
Chapter 2 --- Video Processing --- p.11
Chapter 2.1 --- "Videos, Computer Vision and Image Processing" --- p.11
Chapter 2.2 --- Details in Video Processing --- p.14
Chapter 3 --- Data Analysis --- p.20
Chapter 3.1 --- Background --- p.20
Chapter 3.2 --- Outline of Data Analysis in Our Project --- p.22
Chapter 4 --- Effect of the Medicine --- p.25
Chapter 4.1 --- Hypothesis Testing --- p.26
Chapter 4.2 --- Two-sample t Test --- p.28
Chapter 5 --- Significance of the Two Factors --- p.32
Chapter 5.1 --- Background of ANOVA --- p.33
Chapter 5.2 --- The Model of ANOVA --- p.35
Chapter 5.3 --- Two-way ANOVA in Our Data Analysis --- p.42
Chapter 6 --- Regression Model --- p.45
Chapter 6.1 --- Background of Regression Analysis --- p.47
Chapter 6.2 --- Polynomial Regression Models --- p.52
Chapter 6.2.1 --- Background --- p.52
Chapter 6.2.2 --- R2 and adjusted R2 --- p.53
Chapter 6.3 --- Model Verification --- p.58
Chapter 6.4 --- A Simpler Model As the Other Choice --- p.59
Chapter 6.5 --- Conclusions --- p.60
Chapter 7 --- Further Studies --- p.61
Bibliography --- p.62
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"Computer-aided drug discovery and protein-ligand docking." 2015. http://repository.lib.cuhk.edu.hk/en/item/cuhk-1290642.
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Developing a new drug costs up to US$2.6B and 13.5 years. To save money and time, we have developed a toolset for computer-aided drug discovery, and utilized our toolset to discover drugs for the treatment of cancers and influenza.We first implemented a fast protein-ligand docking tool called idock, and obtained a substantial speedup over a popular counterpart. To facilitate the large-scale use of idock, we designed a heterogeneous web platform called istar, and collected a huge database of more than 23 million small molecules. To elucidate molecular interactions in web, we developed an interactive visualizer called iview. To synthesize novel compounds, we developed a fragment-based drug design tool called iSyn. To improve the predictive accuracy of binding affinity, we exploited the machine learning technique random forest to re-score both crystal and docked poses. To identify structurally similar compounds, we ported the ultrafast shape recognition algorithms to istar. All these tools are free and open source.
We applied our novel toolset to real world drug discovery. We repurposed anti-acne drug adapalene for the treatment of human colon cancer, and identified potential inhibitors of influenza viral proteins. Such new findings could hopefully save human lives.
開發一種新藥需要多至26億美元和13年半的時間。為節省金錢和時間，我們開發了一套計算機輔助藥物研發工具集，並運用該工具集尋找藥物治療癌症和流感。
我們首先實現了一個快速的蛋白與配體對接工具idock，相比一個同類流行軟件獲得了顯著的速度提升。為輔助idock 的大規模使用，我們設計了一個異構網站平台istar，收集了多達兩千三百萬個小分子的大型數據庫。為在網頁展示分子間相互作用，我們開發了一個交互式可視化軟件iview。為生成全新的化合物，我們開發了一個基於分子片段的藥物設計工具iSyn。為改進結合強度預測的精度，我們利用了機器學習技術隨機森林去重新打分晶體及預測構象。為尋找結構相似的化合物，我們移植了超快形狀識別算法至istar。所有這些工俱全是免費和開源。
我們應用了此創新工具集至現實世界藥物尋找中。我們發現抗痤瘡藥阿達帕林可用於治療人類結腸癌，亦篩選出流感病毒蛋白的潛在抑制物。這些新發現可望拯救人類生命。
Li, Hongjian.
Thesis (Ph.D.)--Chinese University of Hong Kong, 2015.
Includes bibliographical references (leaves 340-394).
Abstracts also in Chinese.
Title from PDF title page (viewed on 15, September, 2016).
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
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"A computational-based drug development framework." 2011. http://library.cuhk.edu.hk/record=b5894618.
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Tse, Ching Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.
Includes bibliographical references (p. 188-200).
Abstracts in English and Chinese.
Abstract --- p.i
Acknowledgement --- p.vi
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Obtain information on drug targets --- p.3
Chapter 1.2 --- Drug Design --- p.5
Chapter 1.3 --- Interface for interaction --- p.9
Chapter 1.4 --- Summary --- p.10
Chapter 2 --- Background Study --- p.12
Chapter 2.1 --- Protein Function Prediction --- p.16
Chapter 2.2 --- Drug Design --- p.37
Chapter 2.3 --- Visualisation and Interaction in Biomedic --- p.44
Chapter 3 --- Overview --- p.48
Chapter 3.1 --- Protein prediction using secondary structure analysis --- p.52
Chapter 3.2 --- Knowledge-driven ligand design --- p.55
Chapter 3.3 --- Interactive interface in virtual reality --- p.57
Chapter 4 --- Protein Function Prediction --- p.60
Chapter 4.1 --- Introduction --- p.61
Chapter 4.1.1 --- Motivation --- p.61
Chapter 4.1.2 --- Objective --- p.62
Chapter 4.1.3 --- Overview --- p.63
Chapter 4.2 --- Methods and Design --- p.66
Chapter 4.2.1 --- Feature Cell --- p.68
Chapter 4.2.2 --- Heterogeneous Vector --- p.71
Chapter 4.2.3 --- Feature Cell Similarity --- p.75
Chapter 4.2.4 --- Heterogeneous Vector Similarity --- p.79
Chapter 4.3 --- Experiments --- p.85
Chapter 4.3.1 --- Data Preparation --- p.85
Chapter 4.3.2 --- Experimental Methods --- p.87
Chapter 4.4 --- Results --- p.97
Chapter 4.4.1 --- Scalability --- p.97
Chapter 4.4.2 --- Cluster Quality --- p.99
Chapter 4.4.3 --- Classification Quality --- p.102
Chapter 4.5 --- Discussion --- p.103
Chapter 4.6 --- Conclusion --- p.104
Chapter 5 --- Drug Design --- p.106
Chapter 5.1 --- Introduction --- p.107
Chapter 5.1.1 --- Motivation --- p.107
Chapter 5.1.2 --- Objective --- p.109
Chapter 5.1.3 --- Overview --- p.109
Chapter 5.2 --- Methods --- p.111
Chapter 5.2.1 --- Fragment Joining --- p.115
Chapter 5.2.2 --- Genetic Operators --- p.116
Chapter 5.2.3 --- Post-Processing --- p.124
Chapter 5.3 --- Experiments --- p.128
Chapter 5.3.1 --- Data Preparation --- p.129
Chapter 5.3.2 --- Experimental Methods --- p.132
Chapter 5.4 --- Results --- p.134
Chapter 5.4.1 --- Binding Pose --- p.134
Chapter 5.4.2 --- Free Energy and Molecular Weight --- p.137
Chapter 5.4.3 --- Execution Time --- p.138
Chapter 5.4.4 --- Handling Phosphorus --- p.138
Chapter 5.5 --- Discussions --- p.139
Chapter 5.6 --- Conclusion --- p.140
Chapter 6 --- Interface in Virtual Reality --- p.142
Chapter 6.1 --- Introduction --- p.143
Chapter 6.1.1 --- Motivation --- p.143
Chapter 6.1.2 --- Objective --- p.145
Chapter 6.1.3 --- Overview --- p.145
Chapter 6.2 --- Methods and Design --- p.146
Chapter 6.2.1 --- Hybrid Drug Synthesis --- p.147
Chapter 6.2.2 --- Interactive Interface in Virtual Reality --- p.154
Chapter 6.3 --- Experiments and Results --- p.171
Chapter 6.3.1 --- Data Preparation --- p.171
Chapter 6.3.2 --- Experimental Settings --- p.172
Chapter 6.3.3 --- Results --- p.173
Chapter 6.4 --- Discussions --- p.176
Chapter 6.5 --- Conclusions --- p.179
Chapter 7 --- Conclusion --- p.180
A Glossary --- p.184
Bibliography --- p.188
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Gadabu, Oliver Jintha. "Prescribing cotrimoxazole prophylactic therapy (CPT) before and after an electronic medical record system implementation in two selected hospitals in Malawi." Diss., 2013. http://hdl.handle.net/10500/14404.
Full textAbstract:
Opportunistic infections (OIs) have been identified as a leading cause of poor outcomes in the ARV therapy (ART) programme. In order to reduce OIs, the Malawi, MoH introduced routine prescription of cotrimoxazole preventive therapy (CPT) in 2005. The MoH also started scaling up a point-of-care electronic medical record (EMR) system in 2007 to improve monitoring and evaluation.
This study had the following objectives: i) to quantify prescription of CPT before and after implementing EMR; ii) to compare the difference in CPT prescription before and after implementing EMR.
A historically controlled study design was used to compare CPT prescriptions one year before, and one year after implementation of the EMR at two health facilities.
The data indicated that there was a significant (P <0.001) decrease in CPT prescribing at one health facility and a significant increase in CPT prescription at another.Health Studies
M.A. (Public Health)
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Fink, David S. "Effect modification by socioeconomic conditions on the effects of prescription opioid supply on drug poisoning deaths in the United States." Thesis, 2020. https://doi.org/10.7916/d8-4851-zb84.
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The rise in America’s drug poisoning rates has been described as a public health crisis and has long been attributed to the rapid rise in opioid supply due to increased volumes of medical prescribing in the United States that began in the mid-1990s and peaked in 2012. In 2016, the introduction of the “deaths of despair” hypothesis provided a more nuanced explanation for the rising rates of drug poisoning deaths: increasing income inequality and stagnation of middle-class worker wages, driven by long-term shifts in the labor market, reduced employment opportunities and overall life prospects for persons with a high school degree or less, driving increases in “deaths of despair” (i.e., deaths from suicide, cirrhosis of the liver, and drug poisonings). This focus on economic and social conditions as capable of shaping geospatial differences in drug demand and attendant drug-related harms (e.g., drug poisonings) provides a larger context to factors potentially underlying the heterogeneous distribution of prescription opioid supply across the United States. However, despite the likelihood that economic and social conditions may be important demand-side factors that also interact with supply-side factors to produce the rates of fatal drug poisonings, little information exists about the effect of area-level socioeconomic conditions on fatal drug poisoning rates, and no study has investigated whether socioeconomic conditions interact with prescription opioid supply to affect area-level rates of fatal drug poisonings. The overarching goal of this dissertation was to test the independent and joint effects of supply- and demand-side factors, operationalized as prescription opioid supply and socioeconomic conditions, on fatal drug poisoning in the U.S. First, a systematic review of the literature was conducted to critically evaluate the evidence on the ecological relationship of prescription opioid supply and socioeconomic conditions on rates of drug poisoning deaths. The systematic review provides robust evidence of the independent effect of each prescription opioid supply and socioeconomic conditions on rates of drug poisoning deaths. The gap in the literature on the joint effects of prescription opioid supply and socioeconomic conditions was clear, with no study examining the interaction between supply- and demand-side factors on rates of fatal drug poisonings. Moreover, although greater prescription opioid supply was associated with higher rates of fatal drug poisonings in most of the studies, two studies presented contradictory findings, with one study showing no effect of supply on drug poisoning deaths and the other showing locations with higher levels of prescription opioid supply were associated with fewer drug-related deaths. Three limitations were also identified in the reviewed studies that could partially explain the observed associations. First, although studies aggregated data on drug poisoning deaths to a range of administrative spatial levels, including census tract, 5-digit ZIP code, county, 3-digit ZIP code, and state, no study investigated the sensitivity of findings to the level of geographic aggregation. Second, spatial modeling requires the assessment of spatial autocorrelation in both the unadjusted and adjusted data, but few studies even assessed spatial autocorrelation in the data, and fewer still incorporated spatial dependencies in the model. This is important because when spatial autocorrelation is present, the independence assumption in standard statistical regression models is violated, potentially causing bias and loss of efficiency. Third, studies operationalized prescription opioid supply and socioeconomic conditions using a variety of different measures, and no study assessed the sensitivity of findings to the different measures of supply and socioeconomic conditions.
Second, the ecological relationship between prescription opioid supply and fatal drug poisonings was examined. For this, pooled cross-sectional time series data from 3,109 U.S. counties in 49 states (2006-2016) were used in Bayesian Poisson conditional autoregressive models to estimate the effect of county prescription opioid supply on four types of drug poisoning deaths: any drug (drug-related death), any opioid (opioid-related death), any prescription opioid but not heroin (prescription opioid-related death), and heroin (heroin-related death), adjusting for compositional and contextual differences across counties.
Comparisons were made by type of drug poisoning (any drug, any opioid, prescription opioids only, heroin), level of geographic aggregation (county versus state), and measure of prescription opioid supply (rate of opioid-prescribing per 100 persons and morphine milligram equivalents per-capita). Results indicated a positive association between prescription opioid supply and rates of fatal drug poisonings consistent across changes in type of drug poisoning, level of aggregation, and measure of prescription opioid supply. However, removing confounders from the model caused the direction of the effect estimate to reverse for drug poisoning deaths from any drug, any opioid, and heroin. These results suggested that differences in adjustment for confounding could explain most of the inconsistent findings in the literature.
Finally, a rigorous test of the hypothesis that worse socioeconomic conditions increase risk of fatal drug poisonings at the county level, and interact with prescription opioid supply was conducted. This analysis used the same pooled cross-sectional time series data from 3,109 U.S. counties in 49 states (2006-2016). The analysis modeled the effect of five key socioeconomic variables, including three single socioeconomic variables (unemployment, poverty rate, income inequality) and two index variables (Rey index, American Human Development Index [HDI]) on four types of drug poisoning deaths: any drug (drug-related death), any opioid (opioid-related death), any prescription opioid but not heroin (prescription opioid-related death), and heroin (heroin-related death).
Using a hierarchical Bayesian modeling approach to account for spatial dependence and the variability of fatal drug poisoning rates due to the small number of events, the independent effect of socioeconomic conditions on rates of drug poisoning deaths and their joint multiplicative and additive effect with prescription opioid supply were estimated. Results showed that rates of fatal drug poisonings were higher in more economically and socially disadvantaged counties; the five key indicator variables were differentially associated with drug poisoning rates; and the American Human Development Index (HDI) and income inequality were most strongly associated with fatal drug poisoning rates. Finally, the results indicate that both HDI and income inequality interact with county-level prescription opioid supply to affect drug poisoning rates. Specifically, the effect of higher prescription opioid supply on rates of fatal drug poisonings was greater in counties with higher HDI and more equal income distributions than counties with lower HDI and less equal income distributions. Overall, this dissertation increased knowledge about the separate and conjoint roles of supply- and demand-side factors in the geospatial distribution of fatal drug poisonings in the U.S. The idea that area-level prescription opioid supply are key drivers of prescription drug use, misuse, and addiction and the attendant consequences, including nonfatal and fatal drug poisonings, has been in the literature for well over a decade. However, no study to date has shown that area-level socioeconomic conditions modify the effect of prescription opioid supply on fatal drug poisonings. By identifying important contextual factors capable of modifying the effect of prescription opioid supply reductions on mortality, high-risk geographic areas can be prioritized for interventions to counter any unintended effects of reducing the prescription opioid supply in an area. As federal and state policies continue to target the rising rates of fatal drug poisonings, these findings show that area-level socioeconomic conditions may represent an important target for policy intervention during the current drug poisoning crisis and a critical piece of information necessary for predicting any future drug-related crises.
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Lokhande, Hrishikesh. "Pharmacodynamics miner : an automated extraction of pharmacodynamic drug interactions." Thesis, 2013. http://hdl.handle.net/1805/3757.
Full textAbstract:
Indiana University-Purdue University Indianapolis (IUPUI)Pharmacodynamics (PD) studies the relationship between drug concentration and drug effect on target sites. This field has recently gained attention as studies involving PD Drug-Drug interactions (DDI) assure discovery of multi-targeted drug agents and novel efficacious drug combinations. A PD drug combination could be synergistic, additive or antagonistic depending upon the summed effect of the drug combination at a target site. The PD literature has grown immensely and most of its knowledge is dispersed across different scientific journals, thus the manual identification of PD DDI is a challenge. In order to support an automated means to extract PD DDI, we propose Pharmacodynamics Miner (PD-Miner). PD-Miner is a text-mining tool, which is capable of identifying PD DDI from in vitro PD experiments. It is powered by two major features, i.e., collection of full text articles and in vitro PD ontology. The in vitro PD ontology currently has four classes and more than hundred subclasses; based on these classes and subclasses the full text corpus is annotated. The annotated full text corpus forms a database of articles, which can be queried based upon drug keywords and ontology subclasses. Since the ontology covers term and concept meanings, the system is capable of formulating semantic queries. PD-Miner extracts in vitro PD DDI based upon references to cell lines and cell phenotypes. The results are in the form of fragments of sentences in which important concepts are visually highlighted. To determine the accuracy of the system, we used a gold standard of 5 expert curated articles. PD-Miner identified DDI with a recall of 75% and a precision of 46.55%. Along with the development of PD Miner, we also report development of a semantically annotated in vitro PD corpus. This corpus includes term and sentence level annotations and serves as a gold standard for future text mining.
Books on the topic "Drugs Prescribing Data processing"
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Principles of electronic prescribing. London: Springer, 2008.
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), Centers for Medicare &. Medicaid Services (U S. E-prescribing: Connecting to better health care. [Baltimore, Md.?]: Centers for Medicare & Medicaid Services, 2008.
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E-prescribing: The electronic transformation of medicine. Sudbury, Mass: Jones and Bartlett Publishers, 2009.
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Duggan, Mark G. Does medicaid pay too much for prescription drugs?: A case study of atypical anti-psychotics. Cambridge, Mass: National Bureau of Economic Research, 2003.
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Virginia. Dept. of Medical Assistance Services. Report on programs and incentives to encourage e-prescribing by Medicaid providers. Richmond, VA: Commonwealth of Virginia, 2009.
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United States. Congress. Senate. A bill to amend title XVIII of the Social Security Act to provide incentives to physicians for writing electronic prescriptions. Washington, D.C: U.S. G.P.O., 2007.
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United States. Congress. Senate. Committee on the Judiciary. Electronic prescribing of controlled substances: Addressing health care and law enforcement priorities : hearing before the Committee on the Judiciary, United States Senate, One Hundred Tenth Congress, first session, December 4, 2007. Washington: U.S. G.P.O., 2008.
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Electronic prescribing: A safety and implementation guide. Sudbury, Mass: Jones and Bartlett Publishers, 2009.
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Bae, Jay. Medicaid drug use review demonstration projects: Report to Congress, 1995. [Baltimore, Maryland?]: U.S. Department of Health and Human Services,Health Care Financing Administration, Office of Research and Demonstrations, 2002.
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Senate, United States Congress. A bill to amend title XVIII of the Social Security Act to require physician utilization of the Medicare electronic prescription drug program. Washington, D.C: U.S. G.P.O., 2007.
Find full textBook chapters on the topic "Drugs Prescribing Data processing"
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Ghaemi, S. Nassir. "Carcinogenicity of Psychotropic Drugs." In Clinical Psychopharmacology, edited by S. Nassir Ghaemi, 307–10. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199995486.003.0025.
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The use of psychotropic medications has dramatically increased over the past two decades, especially with serotonin reuptake inhibitors and amphetamines. The majority of U.S. prescriptions for psychotropic drugs happen at the primary care level, usually for anxiety and depressive symptoms and their physical correlates. Given the extensive use of these agents, carcinogenic risk should be carefully weighed when prescribing long-term drug therapies. Some psychotropic drugs have some evidence of carcinogenicity based on animal (preclinical) studies. Other agents do not have such evidence. Whether such risk extends into long-term human use is unclear. The animal data are summarized here and available human data also are investigated.
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Abel, Kathryn M. "Pregnancy prescribing of psychotropic drugs: Keeping pace in a contemporary landscape." In Perinatal Psychiatry. Oxford University Press, 2014. http://dx.doi.org/10.1093/oso/9780199676859.003.0009.
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Pregnant women and their fetuses are more likely than ever to be exposed to antipsychotic medications; perhaps to the newer agents in particular. Drugs like clozapine, olanzapine, risperidone, and quetiapine are increasingly used in women of reproductive age for a range of psychiatric and behavioural disorders other than schizophrenia (Buchanan et al. 2009). Reproductive safety data remain surprisingly incomplete and guideline recommendations lend limited support to clinical risk-benefit analyses (Howard 2005; McKenna et al. 2005; NICE 2007). This is a problem not least because the gold standard randomized controlled trial is considered unethical for assessing psychotropic medication use during pregnancy, while other available observational studies are generally underpowered, with biased samples and therefore remain unfit for purpose in a rapidly changing prescribing landscape (NICE 2007). In a UK population approaching 66 million, –3,000–4,000 births per year may be exposed to antipsychotics or other psychotropic medications. This chapter provides a critical summary of current knowledge about potential risks of fetal antipsychotic and antiepileptic drug exposure and proposes how future observational studies might fill crucial gaps in the evidence. Most incident cases of severe mental illness (schizophrenia, related disorders, and bipolar disorder) occur during the reproductive years and most are treated with continuous psychotropic pharmacotherapy (Buchanan et al. 2009). Better care, deinstitutionalization and the use of newer agents with fewer effects on fertility means that women with psychotic disorders maybe increasingly likely to become pregnant (Howard 2005; NICE 2007), while the use of newer ‘atypical’ antipsychotics for other mental disorders common among women of childbearing age is also expanding (McKenna et al. 2005). For these reasons, psychotropic medications is increasingly likely to be prescribed to mothers during pregnancy (Newport et al. 2007). It is surprising then that reproductive safety data for psychotropic agents remains so incomplete (Barnes 2008; Webb et al. 2004) and guideline recommendations lend limited support to women, their partners and their treating clinicians in difficult clinical risk-benefit analyses (NICE 2007). Recent reports conclude that prospective studies are needed which can access unbiased, reliable (large enough) samples of ill mothers exposed to psychotropic medication and take account of key maternal characteristics (e.g. psychiatric diagnosis, smoking, pregnancy weight) in the estimation of risk (Barnes 2008; NICE 2007).
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Rashid, Mamoon, Vishal Goyal, Shabir Ahmad Parah, and Harjeet Singh. "Drug Prediction in Healthcare Using Big Data and Machine Learning." In Advances in Social Networking and Online Communities, 79–92. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-9096-5.ch005.
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The healthcare system is literally losing patients due to improper diagnosis, accidents, and infections in hospitals alone. To address these challenges, the authors are proposing the drug prediction model that will act as informative guide for patients and help them for taking right medicines for the cure of particular disease. In this chapter, the authors are proposing use of Hadoop distributed file system for the storage of medical datasets related to medicinal drugs. MLLib Library of Apache Spark is to be used for initial data analysis for drug suggestions related to symptoms gathered from particular user. The model will analyze the previous history of patients for any side effects of the drug to be recommended. This proposal will consider weather and maps API from Google as well so that the patients can easily locate the nearby stores where the medicines will be available. It is believed that this proposal of research will surely eradicate the issues by prescribing the optimal drug and its availability by giving the location of the retailer of that drug near the customer.
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Schaefer, Christof. "Managing a patient exposed to a teratogenic drug at different stages of pregnancy." In Practical management of the pregnant patient with rheumatic disease, edited by Karen Schreiber, Eliza Chakravarty, and Monika Østensen, 85–92. Oxford University Press, 2021. http://dx.doi.org/10.1093/med/9780198845096.003.0008.
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Pregnancy-related information provided by leaflets usually contains inadequate data to interpret risks of teratogenicity; and in the absence of data, prescribing information often recommends discontinuing use in anticipation of and during pregnancy. In contrast, individual information on drug risk assessment addresses three different clinical perspectives: 1) looking for drugs of choice or planning pregnancy under medication; 2) assessment of drugs’ risk after exposure during an unplanned pregnancy; 3) assessment of causality in cases of adverse pregnancy outcomes in association with drug exposure. Unfortunately, for many women with chronic rheumatic diseases, discontinuation of all medication leaves an unacceptable risk of disease reactivation. Although, for the majority of drugs, human data are still insufficient to rule out developmental risks it is possible to distinguish antirheumatic drugs of choice with apparently low or negligible risks from those with scarce data or controversies on their safety and those with evidenced risk for the unborn.
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Jara, Antonio J., Mona Alsaedy, Alberto F. Alcolea, Miguel Zamora, and Antonio F. Gómez-Skarmeta. "Intelligent System to Quality Assurance in Drugs Delivery." In Quality Assurance in Healthcare Service Delivery, Nursing and Personalized Medicine, 187–202. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-61350-120-7.ch010.
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Improving quality assurance and providing effective healthcare are some of the most important aims of information and communication technologies (ICT). This chapter presents a novel solution to improve quality assurance in drugs delivery, i.e., reduce clinical errors caused by drug interaction and dose. For that purpose, we have proposed an innovative system based on Internet of things for the drugs identification. Internet of things (IoT) is one of the latest advances in ICT, providing a global connectivity and management of sensors, devices, users, and information. Our contribution is a solution to examine drug related problems based on IoT technologies, i.e. smart phones and Web, to support ubiquitous access, 6LoWPAN technology to support ubiquitous data collection of patients, sensors and hospitals, and RFID/NFC to support global identification. These technologies offer a wide range of applications in healthcare, which improves the quality of services, reduces mistakes, and even detects health anomalies from vital signs. This chapter presents how IoT technology is applied in a pharmaceutical system to examine drugs in order to detect Adverse Drug Reactions (ADRs), harmful effects of pharmaceutical excipients, allergies, complications and contraindications related with liver and renal defects, and harmful side effects during pregnancy or lactation. Thereby, the system provides an enhanced approach assisting physicians in clinical decisions and drug prescribing. The solution presented is based on NFC (Near Field Communication), RFID (Radio Frequency Identification), and barcode identification technologies, which have been integrated in common devices such as smart-phones, PDAs, and PCs. In addition, a remote knowledge-based system based ontologies and rules-engine, has been built to define an intelligent drugs checker, which we have defined as Pharmaceutical Intelligent Information System, where the drug identifies collected from the RFID/NFC tag or barcode is checked, in order to detect whether the identified drug is suitable with respect to the patient’s health record.
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Mohapatra, Chinmayee, Biswaranjan Acharya, Siddhath Swarup Rautaray, and Manjusha Pandey. "Usage of Big Data Prediction Techniques for Predictive Analysis in HIV/AIDS." In Big Data Analytics in HIV/AIDS Research, 54–80. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3203-3.ch003.
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The term big data refers to the data that exceeds the processing or analyzing capacity of existing database management systems. The inability of existing DBMS to handle big data is due to its large volume, high velocity, pertaining veracity, heterogeneous variety, and on-atomic values. Nowadays, healthcare plays a vital role in everyone's life. It becomes a very large and open platform for everyone to do all kinds of research work without affecting human life. When it comes to disease, there are so many types found all over the world. But among them, AIDS (acquired immunodeficiency syndrome) is a disease that spreads so quickly and can easily turn life to death. There are many studies going on to create drugs to cure this deadly disease, but until now, there has been no success. In cases such as this, big data is implemented for better a result, which will have a good impact on society.
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Sabatier, Pierre, Jean Feydy, and Anne-Sophie Jannot. "Accelerating High-Dimensional Temporal Modelling Using Graphics Processing Units for Pharmacovigilance Signal Detection on Real-Life Data." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220401.
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Adverse drug reaction is a major public health issue. The increasing availability of medico-administrative databases offers major opportunities to detect real-life pharmacovigilance signals. We have recently adapted a pharmacoepidemiological method to the large dimension, the WCE (Weigthed Cumulative Exposure) statistical model, which makes it possible to model the temporal relationship between the prescription of a drug and the appearance of a side effect without any a priori hypothesis. Unfortunately, this method faces a computational time problem. The objective of this paper is to describe the implementation of the WCE statistical model using Graphics Processing Unit (GPU) programming as a tool to obtain the spectrum of adverse drug reactions from medico-administrative databases. The process is divided into three steps: pre-processing of care pathways using the Python library Panda, calculation of temporal co-variables using the Python library “KeOps”, estimation of the model parameters using the Python library “PyTorch” – standard in deep learning. Programming the WCE method by distributing the heaviest portions (notably spline calculation) on the GPU makes it possible to accelerate the time required for this method by 1000 times using a computer graphics card and up to 10,000 times with a GPU server. This implementation makes it possible to use WCE on all the drugs on the market to study their spectrum of adverse effects, to highlight new vigilance signals and thus to have a global vigilance tool on medico-administrative database. This is a proof of concept for the use of this technology in epidemiology.
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Morgan Snell, L., Andrew J. Barnes, and Peter Cunningham. "Epidemiology of Substance Use Disorders." In Substance Use Disorders, 3–28. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780190920197.003.0001.
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Nearly 3 million Americans have a current or previous opioid use disorder, and recent data indicate that 10.2% of US adults have ever misused pain relievers. In 2015, approximately 800,000 individuals used heroin, while 4 million misused prescription opioids. Although use of other drugs such as alcohol and cannabis is more prevalent, opioid use contributes to significant morbidity, mortality, and social and economic costs. While the current US opioid overdose epidemic began with prescription opioids, since 2015, heroin and synthetic opioids (e.g., fentanyl) have driven continued increases in opioid overdose deaths, contributing to a recent decline in overall life expectancy in the United States. Policies to address the opioid epidemic by changing clinical practice include provider education, monitoring prescribing practices, and expanding the clinical workforce necessary to treat opioid use disorders. The opioid epidemic appears to be largely a US phenomenon and a consequence of both structural challenges in the US healthcare system and growing socioeconomic disparities, and thus it will require policies including and beyond delivery system reforms to resolve it.
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Othman, Sarah Ben, Hayfa Zgaya, Michèle Vasseur, Bertrand Décaudin, Pascal Odou, and Slim Hammadi. "Introducing Augmented Reality Technique to Enhance the Preparation Circuit of Injectable Chemotherapy Drugs." In MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation. IOS Press, 2022. http://dx.doi.org/10.3233/shti220121.
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Chemotherapy preparations are often complex and subject to a strict regulatory context. The existing control methods are often limited to Double Visual Control (DVC). In this paper, the preparation circuit of chemotherapy drugs is evaluated through data collection and statistical analysis in order to highlight the difficulties encountered. The results regarding preparation and control times and the number of task interruptions highlight the unreliability of the DVC and its impact on processing time. As a solution, we propose a decision support system “Smart Prep” based on Augmented Reality (AR), co-developed, and commercialized by the Faculty of Pharmacy of Lille, Ecole Centrale de Lille and the company Computer Engineering. This system allows the preparation of chemotherapy drugs according to a step-by-step mode, a traceability of the preparation steps and a reduction of tasks’ interruptions.
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Gu, Linfeng, Sisi Li, Yutian Wang, Zhihui Lin, and Jingsong Wu. "Research on Intelligent Research Model of Clues in Cases of Production and Sale of Counterfeit Drugs." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220550.
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This model takes the vigorous promotion of the construction of “smart policing” and the construction of a modern police system as the background, creates a data-based, technology-based, vigorously improves the analysis and mining capabilities of public security organs on the hot issues of counterfeit drug crimes, and transforms the traditional post-processing and passive response methods of police work to the direction of pre-prediction and active intervention. At the same time, drawing on the design ideas of the system architecture, the model system is layered and decoupled, creating a multi-channel data collection, to case text preprocessing and standard definition, forming a “four-category personnel database”, and then classifying according to the theme of public security business to achieve a criminal model of production and sales of counterfeit drugs The overall framework is reused vertically in the field and flexibly extended horizontally.
Conference papers on the topic "Drugs Prescribing Data processing"
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Zhang, Wenqing. "Application of Quantitative Analysis of Controlled Targeted Drugs for Patients Based on Computer Big Data Technology." In 2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC). IEEE, 2022. http://dx.doi.org/10.1109/ipec54454.2022.9777462.
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Zhang, Jianfei, Ai-Te Kuo, Jianan Zhao, Qianlong Wen, Erin Winstanley, Chuxu Zhang, and Yanfang Ye. "Rx-refill Graph Neural Network to Reduce Drug Overprescribing Risks (Extended Abstract)." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/755.
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Prescription (aka Rx) drugs can be easily overprescribed and lead to drug abuse or opioid overdose. Accordingly, a state-run prescription drug monitoring program (PDMP) in the United States has been developed to reduce overprescribing. However, PDMP has limited capability in detecting patients' potential overprescribing behaviors, impairing its effectiveness in preventing drug abuse and overdose in patients. In this paper, we propose a novel model RxNet, which builds 1) a dynamic heterogeneous graph to model Rx refills that are essentially prescribing and dispensing (P&D) relationships among various patients, 2) an RxLSTM network to explore the dynamic Rx-refill behavior and medical condition variation of patients, and 3) a dosing-adaptive network to extract and recalibrate dosing patterns and obtain the refined patient representations which are finally utilized for overprescribing detection. The extensive experimental results on a one-year state-wide PDMP data demonstrate that RxNet consistently outperforms state-of-the-art methods in predicting patients at high risk of opioid overdose and drug abuse.
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Stamatis, A., N. Aretakis, and K. Mathioudakis. "Blade Fault Recognition Based on Signal Processing and Adaptive Fluid Dynamic Modelling." In ASME 1997 International Gas Turbine and Aeroengine Congress and Exhibition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/97-gt-197.
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An approach for identification of faults in blades of a gas turbine, based on physical modelling is presented. A measured quantity is used as an input and the deformed blading configuration is produced as an output. This is achieved without using any kind of “signature”, as is customary in diagnostic procedures for this kind of faults. A fluid dynamic model is used in a manner similar to what is known as “inverse design methods”: the solid boundaries which produce a certain flow field are calculated by prescribing this flow field. In the present case a signal, corresponding to the pressure variation on the blade-to-blade plane, is measured. The blade cascade geometry that has produced this signal is then produced by the method. In the paper the method is described and applications to test cases are presented. The test cases include theoretically produced faults as well as experimental cases, where actual measurement data are shown to produce the geometrical deformations which existed in the test engine.
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Zhijian, Lyu, Jiang Shaohua, Liang Yigao, and Gao Min. "GDGRU-DTA: Predicting Drug-Target Binding Affinity based on GNN and Double GRU." In 3rd International Conference on Data Mining and Machine Learning (DMML 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120703.
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The work for predicting drug and target affinity(DTA) is crucial for drug development and repurposing. In this work, we propose a novel method called GDGRU-DTA to predict the binding affinity between drugs and targets, which is based on GraphDTA, but we consider that protein sequences are long sequences, so simple CNN cannot capture the context dependencies in protein sequences well. Therefore, we improve it by interpreting the protein sequences as time series and extracting their features using Gate Recurrent Unit(GRU) and Bidirectional Gate Recurrent Unit(BiGRU). For the drug, our processing method is similar to that of GraphDTA, but uses two different graph convolution methods. Subsequently, the representation of drugs and proteins are concatenated for final prediction. We evaluate the proposed model on two benchmark datasets. Our model outperforms some state-of-the-art deep learning methods, and the results demonstrate the feasibility and excellent feature capture ability of our model.
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Ferreira, Lucas, Cristiane Nobre, Luis Zárate, and Mark Song. "Study of the evolution of antiemetic treatment through the application of Triadic Formal Concept Analysis." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/kdmile.2021.17454.
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Cancer treatment is always improving with new techniques and drugs, applied in a process with rigorous medical analysis. One way to analyze medical outcomes is to test a new drug in one group and placebos in another. In this article, we used a database with the results of a new drug for preventing chemotherapy-induced nausea and vomiting. After processing the database, we apply the Triadic Formal Concept approach to extract triadic rules, implications, and conditions that are used to identify correlations throughout the medical sessions. Our study shows that triadic analysis can help scientists perform clinical analyzes efficiently by looking at patient history data.
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Migliorelli, Lucia, Annalisa Cenci, Michele Bernardini, Luca Romeo, Sara Moccia, and Primo Zingaretti. "A Cloud-Based Healthcare Infrastructure for Neonatal Intensive-Care Units." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97526.
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Abstract Intensive medical attention of preterm babies is crucial to avoid short-term and long-term complications. Within neonatal intensive care units (NICUs), cribs are equipped with electronic devices aimed at: monitoring, administering drugs and supporting clinician in making diagnosis and offer treatments. To manage this huge data flux, a cloud-based healthcare infrastructure that allows data collection from different devices (i.e., patient monitors, bilirubinometers, and transcutaneous bilirubinometers), storage, processing and transferring will be presented. Communication protocols were designed to enable the communication and data transfer between the three different devices and a unique database and an easy to use graphical user interface (GUI) was implemented. The infrastructure is currently used in the “Women’s and Children’s Hospital G.Salesi” in Ancona (Italy), supporting clinicians and health opertators in their daily activities.
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Oliveira, Ricardo F., Senhorinha F. Teixeira, Helena Maria Cabral Marques, and José Carlos Teixeira. "A Correlative CFD Study Between Recirculation Area and FPM in VHC Design." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67329.
Full textAbstract:
A major part of asthma treatment is made by the use of preventive inhalation drugs. The Pressurized Metered-Dose Inhalator (pMDI) has been the backbone device for this treatment, due to its simplicity, portability and widely acceptance. But no device comes without its limitations, and pMDI is hard to handle properly by elders and children < 5 years old, resulting in reduced amount of drug to the patient lungs. Add-on devices (e.g. spacers) were developed to mitigate the need for coordination and reduce the oral/throat deposition, namely the Valved Holding Chambers (VHC). These devices are incorporated with a one-way valve and a chamber that allows the spray droplets to rapidly reduce their size upon release on a stagnated and confined flow. The VHC main ability, in terms of efficiency, is to reduce the coarse fraction (i.e. particles with diameter > 4.7μm) of the plume by impaction and allow the fine fraction to be inhaled by the patient. The VHC geometry will play a very importance role in the entrapment of small drug particles (i.e. fine fraction). The hypothesis proposed by this study is that a small particle has more probability to be trapped in geometries with higher recirculation areas (and stagnation zones). These macro vortices will cause a particle with small Stokes number to be entrapped; to assess this hypothesis a numerical study was modelled. The numerical study was carried out on an idealized geometry of a VHC device, using a 2D axisymmetric approach. Different coordinates for a “corner” point, were tested. FLUENT® was used to obtain the unsteady numerical solution, meshes were generated using Meshing® software from ANSYS®. Once the flow field is stabilized (around 0.6s), a pMDI spray was injected into the domain during 0.1 seconds and the simulation continued until perform 4 seconds. The simulation takes into account the vaporization of the HFA-134a propellant present in the droplets of spray and a User Defined Function (UDF) for modeling the particle -wall interaction. The post-processing of the results included the calculation of the recirculation area and the Fine Particle Mass (FPM) that exits the domain. Results show that the percentage of recirculation area decreases linearly with the increase of axial position of the corner point, and rapidly increases with the radial displacement. FPM results are not so linear; nevertheless they show opposite behavior to the recirculation area. Additionally, results show that high recirculation area reduces the amount of FPM emitted. Data can be correlated through a power function (FPM = 101.805*Area−0.244; R2 = 0.460). Results are more strongly correlated for lower values of radial displacement. The results seem to corroborate the hypothesis that smaller particles tend to be entrapped by recirculation areas.
Reports on the topic "Drugs Prescribing Data processing"
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Halker Singh, Rashmi B., Juliana H. VanderPluym, Allison S. Morrow, Meritxell Urtecho, Tarek Nayfeh, Victor D. Torres Roldan, Magdoleen H. Farah, et al. Acute Treatments for Episodic Migraine. Agency for Healthcare Research and Quality (AHRQ), December 2020. http://dx.doi.org/10.23970/ahrqepccer239.
Full textAbstract:
Objectives. To evaluate the effectiveness and comparative effectiveness of pharmacologic and nonpharmacologic therapies for the acute treatment of episodic migraine in adults. Data sources. MEDLINE®, Embase®, Cochrane Central Registrar of Controlled Trials, Cochrane Database of Systematic Reviews, PsycINFO®, Scopus, and various grey literature sources from database inception to July 24, 2020. Comparative effectiveness evidence about triptans and nonsteroidal anti-inflammatory drugs (NSAIDs) was extracted from existing systematic reviews. Review methods. We included randomized controlled trials (RCTs) and comparative observational studies that enrolled adults who received an intervention to acutely treat episodic migraine. Pairs of independent reviewers selected and appraised studies. Results. Data on triptans were derived from 186 RCTs summarized in nine systematic reviews (101,276 patients; most studied was sumatriptan, followed by zolmitriptan, eletriptan, naratriptan, almotriptan, rizatriptan, and frovatriptan). Compared with placebo, triptans resolved pain at 2 hours and 1 day, and increased the risk of mild and transient adverse events (high strength of the body of evidence [SOE]). Data on NSAIDs were derived from five systematic reviews (13,214 patients; most studied was ibuprofen, followed by diclofenac and ketorolac). Compared with placebo, NSAIDs probably resolved pain at 2 hours and 1 day, and increased the risk of mild and transient adverse events (moderate SOE). For other interventions, we included 135 RCTs and 6 comparative observational studies (37,653 patients). Compared with placebo, antiemetics (low SOE), dihydroergotamine (moderate to high SOE), ergotamine plus caffeine (moderate SOE), and acetaminophen (moderate SOE) reduced acute pain. Opioids were evaluated in 15 studies (2,208 patients).Butorphanol, meperidine, morphine, hydromorphone, and tramadol in combination with acetaminophen may reduce pain at 2 hours and 1 day, compared with placebo (low SOE). Some opioids may be less effective than some antiemetics or dexamethasone (low SOE). No studies evaluated instruments for predicting risk of opioid misuse, opioid use disorder, or overdose, or evaluated risk mitigation strategies to be used when prescribing opioids for the acute treatment of episodic migraine. Calcitonin gene-related peptide (CGRP) receptor antagonists improved headache relief at 2 hours and increased the likelihood of being headache-free at 2 hours, at 1 day, and at 1 week (low to high SOE). Lasmiditan (the first approved 5-HT1F receptor agonist) restored function at 2 hours and resolved pain at 2 hours, 1 day, and 1 week (moderate to high SOE). Sparse and low SOE suggested possible effectiveness of dexamethasone, dipyrone, magnesium sulfate, and octreotide. Compared with placebo, several nonpharmacologic treatments may improve various measures of pain, including remote electrical neuromodulation (moderate SOE), magnetic stimulation (low SOE), acupuncture (low SOE), chamomile oil (low SOE), external trigeminal nerve stimulation (low SOE), and eye movement desensitization re-processing (low SOE). However, these interventions, including the noninvasive neuromodulation devices, have been evaluated only by single or very few trials. Conclusions. A number of acute treatments for episodic migraine exist with varying degrees of evidence for effectiveness and harms. Use of triptans, NSAIDs, antiemetics, dihydroergotamine, CGRP antagonists, and lasmiditan is associated with improved pain and function. The evidence base for many other interventions for acute treatment, including opioids, remains limited.
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Apiyo, Eric, Zita Ekeocha, Stephen Robert Byrn, and Kari L. Clase. Improving Pharmacovigilliance Quality Management System in the Pharmacy and Poisions Board of Kenya. Purdue University, December 2021. http://dx.doi.org/10.5703/1288284317444.
Full textAbstract:
The purpose of this study was to explore ways of improving the pharmacovigilance quality system employed by the Pharmacy and Poisons Board of Kenya. The Pharmacy and Poisons Board of Kenya employs a hybrid system of pharmacovigilance that utilizes an online system of reporting pharmacovigilance incidences and a physical system, where a yellow book is physically filled by the healthcare worker and sent to the Pharmacy and Poisons Board for onward processing. This system, even though it has been relatively effective compared to other systems employed in Africa, has one major flaw. It is a slow and delayed system that captures the data much later after the fact and the agency will always be behind the curve in controlling the adverse incidents and events. This means that the incidences might continue to arise or go out of control. This project attempts to develop a system that would be more proactive in the collection of pharmacovigilance data and more predictive of pharmacovigilance incidences. The pharmacovigilance system should have the capacity to detect and analyze subtle changes in reporting frequencies and in patterns of clinical symptoms and signs that are reported as suspected adverse drug reactions. The method involved carrying out a thorough literature review of the latest trends in pharmacovigilance employed by different regulatory agencies across the world, especially the more stringent regulatory authorities. A review of the system employed by the Pharmacy and Poisons Board of Kenya was also done. Pharmacovigilance data, both primary and secondary, were collected and reviewed. Media reports on adverse drug reactions and poor-quality medicines over the period were also collected and reviewed. An appropriate predictive pharmacovigilance tool was also researched and identified. It was found that the Pharmacy and Poisons Board had a robust system of collecting historical pharmacovigilance data both from the healthcare workers and the general public. However, a more responsive data collection and evaluation system is proposed that will help the agency achieve its pharmacovigilance objectives. On analysis of the data it was found that just above half of all the product complaints, about 55%, involved poor quality medicines; 15% poor performance, 13% presentation, 8% adverse drug reactions, 7% market authorization, 2% expired drugs and 1% adulteration complaints. A regulatory pharmacovigilance prioritization tool was identified, employing a risk impact analysis was proposed for regulatory action.
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Stall, Nathan M., Kevin A. Brown, Antonina Maltsev, Aaron Jones, Andrew P. Costa, Vanessa Allen, Adalsteinn D. Brown, et al. COVID-19 and Ontario’s Long-Term Care Homes. Ontario COVID-19 Science Advisory Table, January 2021. http://dx.doi.org/10.47326/ocsat.2021.02.07.1.0.
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Key Message Ontario long-term care (LTC) home residents have experienced disproportionately high morbidity and mortality, both from COVID-19 and from the conditions associated with the COVID-19 pandemic. There are several measures that could be effective in preventing COVID-19 outbreaks, hospitalizations, and deaths in Ontario’s LTC homes, if implemented. First, temporary staffing could be minimized by improving staff working conditions. Second, homes could be further decrowded by a continued disallowance of three- and four-resident rooms and additional temporary housing for the most crowded homes. Third, the risk of SARS-CoV-2 infection in staff could be minimized by approaches that reduce the risk of transmission in communities with a high burden of COVID-19. Summary Background The Province of Ontario has 626 licensed LTC homes and 77,257 long-stay beds; 58% of homes are privately owned, 24% are non-profit/charitable, 16% are municipal. LTC homes were strongly affected during Ontario’s first and second waves of the COVID-19 pandemic. Questions What do we know about the first and second waves of COVID-19 in Ontario LTC homes? Which risk factors are associated with COVID-19 outbreaks in Ontario LTC homes and the extent and death rates associated with outbreaks? What has been the impact of the COVID-19 pandemic on the general health and wellbeing of LTC residents? How has the existing Ontario evidence on COVID-19 in LTC settings been used to support public health interventions and policy changes in these settings? What are the further measures that could be effective in preventing COVID-19 outbreaks, hospitalizations, and deaths in Ontario’s LTC homes? Findings As of January 14, 2021, a total of 3,211 Ontario LTC home residents have died of COVID-19, totaling 60.7% of all 5,289 COVID-19 deaths in Ontario to date. There have now been more cumulative LTC home outbreaks during the second wave as compared with the first wave. The infection and death rates among LTC residents have been lower during the second wave, as compared with the first wave, and a greater number of LTC outbreaks have involved only staff infections. The growth rate of SARS-CoV-2 infections among LTC residents was slower during the first two months of the second wave in September and October 2020, as compared with the first wave. However, the growth rate after the two-month mark is comparatively faster during the second wave. The majority of second wave infections and deaths in LTC homes have occurred between December 1, 2020, and January 14, 2021 (most recent date of data extraction prior to publication). This highlights the recent intensification of the COVID-19 pandemic in LTC homes that has mirrored the recent increase in community transmission of SARS-CoV-2 across Ontario. Evidence from Ontario demonstrates that the risk factors for SARS-CoV-2 outbreaks and subsequent deaths in LTC are distinct from the risk factors for outbreaks and deaths in the community (Figure 1). The most important risk factors for whether a LTC home will experience an outbreak is the daily incidence of SARS-CoV-2 infections in the communities surrounding the home and the occurrence of staff infections. The most important risk factors for the magnitude of an outbreak and the number of resulting resident deaths are older design, chain ownership, and crowding. Figure 1. Anatomy of Outbreaks and Spread of COVID-19 in LTC Homes and Among Residents Figure from Peter Hamilton, personal communication. Many Ontario LTC home residents have experienced severe and potentially irreversible physical, cognitive, psychological, and functional declines as a result of precautionary public health interventions imposed on homes, such as limiting access to general visitors and essential caregivers, resident absences, and group activities. There has also been an increase in the prescribing of psychoactive drugs to Ontario LTC residents. The accumulating evidence on COVID-19 in Ontario’s LTC homes has been leveraged in several ways to support public health interventions and policy during the pandemic. Ontario evidence showed that SARS-CoV-2 infections among LTC staff was associated with subsequent COVID-19 deaths among LTC residents, which motivated a public order to restrict LTC staff from working in more than one LTC home in the first wave. Emerging Ontario evidence on risk factors for LTC home outbreaks and deaths has been incorporated into provincial pandemic surveillance tools. Public health directives now attempt to limit crowding in LTC homes by restricting occupancy to two residents per room. The LTC visitor policy was also revised to designate a maximum of two essential caregivers who can visit residents without time limits, including when a home is experiencing an outbreak. Several further measures could be effective in preventing COVID-19 outbreaks, hospitalizations, and deaths in Ontario’s LTC homes. First, temporary staffing could be minimized by improving staff working conditions. Second, the risk of SARS-CoV-2 infection in staff could be minimized by measures that reduce the risk of transmission in communities with a high burden of COVID-19. Third, LTC homes could be further decrowded by a continued disallowance of three- and four-resident rooms and additional temporary housing for the most crowded homes. Other important issues include improved prevention and detection of SARS-CoV-2 infection in LTC staff, enhanced infection prevention and control (IPAC) capacity within the LTC homes, a more balanced and nuanced approach to public health measures and IPAC strategies in LTC homes, strategies to promote vaccine acceptance amongst residents and staff, and further improving data collection on LTC homes, residents, staff, visitors and essential caregivers for the duration of the COVID-19 pandemic. Interpretation Comparisons of the first and second waves of the COVID-19 pandemic in the LTC setting reveal improvement in some but not all epidemiological indicators. Despite this, the second wave is now intensifying within LTC homes and without action we will likely experience a substantial additional loss of life before the widespread administration and time-dependent maximal effectiveness of COVID-19 vaccines. The predictors of outbreaks, the spread of infection, and deaths in Ontario’s LTC homes are well documented and have remained unchanged between the first and the second wave. Some of the evidence on COVID-19 in Ontario’s LTC homes has been effectively leveraged to support public health interventions and policies. Several further measures, if implemented, have the potential to prevent additional LTC home COVID-19 outbreaks and deaths.