Relevant bibliographies by topics / Personalized medicine support systems

Academic literature on the topic 'Personalized medicine support systems'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Personalized medicine support systems"

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Radhakrishnan, Arun, and Gowthamarajan Kuppusamy. "Theoretical Formulation Strategies towards Neutralizing Inter-individual Variability Associated with Tacrolimus Immunosuppressant Therapy: A Case Study on Nextgeneration Personalized Medicine." Current Drug Metabolism 22, no. 12 (October 2021): 939–56. http://dx.doi.org/10.2174/1389200222666211015153317.

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: Individualizing drug therapy and attaining maximum benefits of a drug devoid of adverse reactions is the benefit of personalized medicine. One of the important factors contributing to inter-individual variability is genetic polymorphism. As of now, dose titration is the only followed golden standard for implementing personalized medicine. Converting the genotypic data into an optimized dose has become easier now due to technology development. However, for many drugs, finding an individualized dose may not be successful, which further leads to a trial and error approach. These dose titration strategies are generally followed at the clinical level, and so industrial involvement and further standardizations are not feasible. On the other side, technologically driven pharmaceutical industries have multiple smart drug delivery systems which are underutilized towards personalized medicine. Transdisciplinary research with drug delivery science can additionally support the personalization by converting the traditional concept of “dose titration towards personalization” with novel “dose-cum-dosage form modification towards next-generation personalized medicine”; the latter approach is useful to overcome gene-based inter-individual variability by either blocking, to downregulate, or bypassing the biological protein generated by the polymorphic gene. This article elaborates an advanced approach to implementing personalized medicine with the support of novel drug delivery systems. As a case study, we further reviewed the genetic polymorphisms associated with tacrolimus and customized novel drug delivery systems to overcome these challenges factored towards personalized medicine for better clinical outcomes, thereby paving a new strategy for implementing personalized medicine for all other drug candidates.
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Hizel, H. Candan. "Highly personalized reports for personalized drug selection by expert systems as clinical decision support." Personalized Medicine 14, no. 2 (March 2017): 93–97. http://dx.doi.org/10.2217/pme-2016-0083.

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Banjar, Haneen, David Adelson, Fred Brown, and Naeem Chaudhri. "Intelligent Techniques Using Molecular Data Analysis in Leukaemia: An Opportunity for Personalized Medicine Support System." BioMed Research International 2017 (2017): 1–21. http://dx.doi.org/10.1155/2017/3587309.

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The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient’s genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice.
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Maojo, V., J. A. Mitchell, and L. J. Frey. "Section 7: Bioinformatics: Bioinformatics Linkage of Heterogeneous Clinical and Genomic Information in Support of Personalized Medicine." Yearbook of Medical Informatics 16, no. 01 (August 2007): 98–105. http://dx.doi.org/10.1055/s-0038-1638533.

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SummaryBiomedical Informatics as a whole faces a difficult epistemological task, since there is no foundation to explain the complexities of modeling clinical medicine and the many relationships between genotype, phenotype, and environment. This paper discusses current efforts to investigate such relationships, intended to lead to better diagnostic and therapeutic procedures and the development of treatments that could make personalized medicine a reality.To achieve this goal there are a number of issues to overcome. Primary are the rapidly growing numbers of heterogeneous data sources which must be integrated to support personalized medicine. Solutions involving the use of domain driven information models of heterogeneous data sources are described in conjunction with controlled ontologies and terminologies. A number of such applications are discussed.Researchers have realized that many dimensions of biology and medicine aim to understand and model the informational mechanisms that support more precise clinical diagnostic, prognostic and therapeutic procedures. As long as data grows exponentially, novel Biomedical Informatics approaches and tools are needed to manage the data. Although researchers are typically able to manage this information within specific, usually narrow contexts of clinical investigation, novel approaches for both training and clinical usage must be developed.After some preliminary overoptimistic expectations, it seems clear now that genetics alone cannot transform medicine. In order to achieve this, heterogeneous clinical and genomic data source must be integrated in scientifically meaningful and productive systems. This will include hypothesis-driven scientific research systems along with well understood information systems to support such research. These in turn will enable the faster advancement of personalized medicine.
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Flores Fonseca, Víctor Manuel, and Aldo Quelopana. "An Intelligent System Prototype to support and sharing diagnoses of maligned tumours, based on personalized medicine philosophy." Inteligencia Artificial 19, no. 58 (December 18, 2016): 17. http://dx.doi.org/10.4114/intartif.vol19iss58pp17-22.

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Circulatory systems diseases are one of the most important causes of death in Chilean population according to a report presented by the Chilean National Bureau of Statistics (INE). Undoubtedly, these sad numbers arise an opportunity to analyse ways to improve this situation. Personalized Medicine is a new approach used to adapt standard medical treatments to individual characteristics of patients. Currently, several kinds of personalized-medicine software applications are building using Artificial Intelligent techniques and supported by techniques as Cloud Computing and Big Data. This architecture provides complex and varied information access, such as clinical data, genome data, patients’ treatment or drugs information, among others. This document describes a proposal to produce a method for generating and sharing medical information, particularly of maligned tumors in Chile. The prototype will be developed within the framework of the personalized medicine.
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Walsh, Seán, Evelyn E. C. de Jong, Janna E. van Timmeren, Abdalla Ibrahim, Inge Compter, Jurgen Peerlings, Sebastian Sanduleanu, et al. "Decision Support Systems in Oncology." JCO Clinical Cancer Informatics, no. 3 (December 2019): 1–9. http://dx.doi.org/10.1200/cci.18.00001.

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Precision medicine is the future of health care: please watch the animation at https://vimeo.com/241154708 . As a technology-intensive and -dependent medical discipline, oncology will be at the vanguard of this impending change. However, to bring about precision medicine, a fundamental conundrum must be solved: Human cognitive capacity, typically constrained to five variables for decision making in the context of the increasing number of available biomarkers and therapeutic options, is a limiting factor to the realization of precision medicine. Given this level of complexity and the restriction of human decision making, current methods are untenable. A solution to this challenge is multifactorial decision support systems (DSSs), continuously learning artificial intelligence platforms that integrate all available data—clinical, imaging, biologic, genetic, cost—to produce validated predictive models. DSSs compare the personalized probable outcomes—toxicity, tumor control, quality of life, cost effectiveness—of various care pathway decisions to ensure optimal efficacy and economy. DSSs can be integrated into the workflows both strategically (at the multidisciplinary tumor board level to support treatment choice, eg, surgery or radiotherapy) and tactically (at the specialist level to support treatment technique, eg, prostate spacer or not). In some countries, the reimbursement of certain treatments, such as proton therapy, is already conditional on the basis that a DSS is used. DSSs have many stakeholders—clinicians, medical directors, medical insurers, patient advocacy groups—and are a natural consequence of big data in health care. Here, we provide an overview of DSSs, their challenges, opportunities, and capacity to improve clinical decision making, with an emphasis on the utility in oncology.
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Gaebel, Jan, Johannes Keller, Daniel Schneider, Adrian Lindenmeyer, Thomas Neumuth, and Stefan Franke. "The Digital Twin: Modular Model-Based Approach to Personalized Medicine." Current Directions in Biomedical Engineering 7, no. 2 (October 1, 2021): 223–26. http://dx.doi.org/10.1515/cdbme-2021-2057.

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Abstract To overcome obstacles and complexity of decision making in clinical oncology, we propose an integrated clinical decision support approach; the Digital Twin. We analyse the reasons for frustration in applying clinical decision support and provide a multi-levelled approach to implementing a flexible system to support and strengthen clinical decisions. Describing medical patterns and contexts with Resource Description Framework (RDF) allows for standardised way of connecting medical knowledge and processing modules. Having flexible web-based interfaces integrated a multitude of heterogeneous data processing systems to either make clinical data available altogether, or provide calculations and assessments. Transition of the Digital Twin to clinical practice promises effective assistance and safer clinical decisions.
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Leong, T. Y. "Toward Patient-Centered, Personalized and Personal Decision Support and Knowledge Management: A Survey." Yearbook of Medical Informatics 21, no. 01 (August 2012): 104–12. http://dx.doi.org/10.1055/s-0038-1639439.

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SummaryThis paper summarizes there cent trends and highlights the challenges and opportunities in decision support and knowledge management for patient-centered, personalized, and personal healthcare.The discussions are based on a broad survey of related references, focusing on the most recent publications. Major advances are examined in the areas of i) shared decision making paradigms, ii) continuity of care infrastructures and architectures, iii) human factors and system design approaches, iv) knowledge management innovations, and v) practical deployment and change considerations.Many important initiatives, projects, and plans with promising results have been identified. The common themes focus on supporting the individual patients who are playing an increasing central role in their own care decision processes. New collaborative decision making paradigms and information infrastructure sare required to ensure effective continuity of care. Human factors and usability are crucial for the successful development and deployment of the relevant systems, tools, and aids. Advances in personalized medicine can be achieved through integrating genomic, phenotypic and other biological, individual, and population level information, and gaining useful insights from building and analyzing biological and other models at multiple levels of abstraction. Therefore, new Information and Communication Technologies and evaluation approaches are needed to effectively manage the scale and complexity of biomedical and health information, and adapt to the changing nature of clinical decision support.Recent research in decision support andknowledge management combines heterogeneous information and personal data to provide cost-effective, calibrated, personalized support in shared decision making at the point of care. Current and emerging efforts concentrate on developing or extendingconventional paradigms, techniques, systems,and architectures for the newpredictive, preemptive, and participatory healthcare model for patient-centered, personalized medicine. There is also an increasing emphasis on managing complexity with changing care models, processes, and settings.
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Dopazo, Joaquín, Douglas Maya-Miles, Federico García, Nicola Lorusso, Miguel Ángel Calleja, María Jesús Pareja, José López-Miranda, et al. "Implementing Personalized Medicine in COVID-19 in Andalusia: An Opportunity to Transform the Healthcare System." Journal of Personalized Medicine 11, no. 6 (May 26, 2021): 475. http://dx.doi.org/10.3390/jpm11060475.

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The COVID-19 pandemic represents an unprecedented opportunity to exploit the advantages of personalized medicine for the prevention, diagnosis, treatment, surveillance and management of a new challenge in public health. COVID-19 infection is highly variable, ranging from asymptomatic infections to severe, life-threatening manifestations. Personalized medicine can play a key role in elucidating individual susceptibility to the infection as well as inter-individual variability in clinical course, prognosis and response to treatment. Integrating personalized medicine into clinical practice can also transform health care by enabling the design of preventive and therapeutic strategies tailored to individual profiles, improving the detection of outbreaks or defining transmission patterns at an increasingly local level. SARS-CoV2 genome sequencing, together with the assessment of specific patient genetic variants, will support clinical decision-makers and ultimately better ways to fight this disease. Additionally, it would facilitate a better stratification and selection of patients for clinical trials, thus increasing the likelihood of obtaining positive results. Lastly, defining a national strategy to implement in clinical practice all available tools of personalized medicine in COVID-19 could be challenging but linked to a positive transformation of the health care system. In this review, we provide an update of the achievements, promises, and challenges of personalized medicine in the fight against COVID-19 from susceptibility to natural history and response to therapy, as well as from surveillance to control measures and vaccination. We also discuss strategies to facilitate the adoption of this new paradigm for medical and public health measures during and after the pandemic in health care systems.
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Litinskaia, E. L. "Control and Decision-Making Support in a Personalized Insulin Therapy System." Proceedings of Universities. Electronics 26, no. 2 (April 2021): 162–71. http://dx.doi.org/10.24151/1561-5405-2021-26-2-162-171.

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Insulin therapy automation is an actual research line in the glycemic control of diabetes mellitus type 1 patients. Development of closed-loop systems and methods will allow blood glucose maintaining in the physiological range. The work proposes the personalized insulin therapy system considered as a closed-loop control system based on feedback and external disturbances compensation principles. Automatic feedback-based glycemic control includes proportional reg-ulation of basal insulin infusion rate in relation to optimized thresholds inside the target range. To achieve bidirectional glycemic regulation the author proposes model predictive control for calculation of not only optimal profile of bolus infusion but also recommended corrective dose of carbohydrates. Besides, the comparative analysis of trends in measured and predicted profiles of blood glucose allows detecting and compensation of its unpredicted deviations. In silico testing of developed algorithms on nine virtual adults for 72 hours shows an ability for glucose maintaining in the target range for whole system operation time.
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Dissertations / Theses on the topic "Personalized medicine support systems"

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Cheng, Chih-Wen. "Development of integrated informatics analytics for improved evidence-based, personalized, and predictive health." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54872.

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Advanced information technologies promise a massive influx of individual-specific medical data. These rich sources offer great potential for an increased understanding of disease mechanisms and for providing evidence-based and personalized clinical decision support. However, the size, complexity, and biases of the data pose new challenges, which make it difficult to transform the data to useful and actionable knowledge using conventional statistical analysis. The so-called “Big Data” era has created an emerging and urgent need for scalable, computer-based data mining methods that can turn data into useful, personalized decision support knowledge in a flexible, cost-effective, and productive way. The goal of my Ph.D. research is to address some key challenges in current clinical deci-sion support, including (1) the lack of a flexible, evidence-based, and personalized data mining tool, (2) the need for interactive interfaces and visualization to deliver the decision support knowledge in an accurate and effective way, (3) the ability to generate temporal rules based on patient-centric chronological events, and (4) the need for quantitative and progressive clinical predictions to investigate the causality of targeted clinical outcomes. The problem statement of this dissertation is that the size, complexity, and biases of the current clinical data make it very difficult for current informatics technologies to extract individual-specific knowledge for clinical decision support. This dissertation addresses these challenges with four overall specific aims: Evidence-Based and Personalized Decision Support: To develop clinical decision support systems that can generate evidence-based rules based on personalized clinical conditions. The systems should also show flexibility by using data from different clinical settings. Interactive Knowledge Delivery: To develop an interactive graphical user interface that expedites the delivery of discovered decision support knowledge and to propose a new visualiza-tion technique to improve the accuracy and efficiency of knowledge search. Temporal Knowledge Discovery: To improve conventional rule mining techniques for the discovery of relationships among temporal clinical events and to use case-based reasoning to evaluate the quality of discovered rules. Clinical Casual Analysis: To expand temporal rules with casual and time-after-cause analyses to provide progressive clinical prognostications without prediction time constraints. The research of this dissertation was conducted with frequent collaboration with Children’s Healthcare of Atlanta, Emory Hospital, and Georgia Institute of Technology. It resulted in the development and adoption of concrete application deliverables in different medical settings, including: the neuroARM system in pediatric neuropsychology, the PHARM system in predictive health, and the icuARM, icuARM-II, and icuARM-KM systems in intensive care. The case studies for the evaluation of these systems and the discovered knowledge demonstrate the scope of this research and its potential for future evidence-based and personalized clinical decision support.
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Jacobson, Timothy. "A Trans-Dimensional View of Drug Resistance Evolution in Multiple Myeloma Patients." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6099.

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Multiple Myeloma (MM) is a treatable, yet incurable, malignancy of bone marrowplasma cells. This cancer affects many patients and many succumb to relapse of tumor burden despite a large number of available chemotherapeutic agents developed for therapy. This is because MM tumors are heterogeneous and receive protection from therapeutic agents by the microenvironment and other mechanisms including homologous MM-MM aggregation. Therefore, therapy failure and frequent patient relapse is due to the evolution of drug resistance, not a lack of available drugs. To analyze and understand this problem, the evolution of drug resistance has been explored and presented herein. We seek to describe the methods through which MM cells become resistant to therapy, and how this resistance evolves throughout a patient’s treatment history. We achieve this in five steps. First we review the patient’s clinical history, including treatments and changes in tumor burden. Second, we trace the evolutionary tree of sub-clones within the tumor burden using standard of care fluorescence in situ hybridization (FISH). Thirdly, immunohistochemistry slides are stained and aligned to quantify the level of environmental protection received by surrounding cells and plasma in the bone marrow microenvironment (coined environment mediated drug resistance score [EMDR]). The fourth analysis type is produced through a novel 384-well plate ex vivo chemosensitivity assay to quantify sensitivity of primary MM cells to chemotherapeutic agents and extrapolate these findings to 90-day clinical response predictions. In addition to direct clinical application in the choice of best treatment, this tool was also used to study changes in sensitivity of patient tumors to other drugs, and it was observed that, upon relapse, in addition to developing resistance to the current line of therapy, tumors become cross-resistant to agents that they were never exposed to. Finally, MM-MM homologous aggregation is quantified to assess the level of drug resistance contributed by clustering of patient tumor cells, which causes upregulation of Bcl-2 expression and other resistance mechanisms1. The findings of such experimentation improve comprehension of the driving factors that contribute to drug resistance evolution on a personalized treatment basis. The aforementioned factors all contribute in varying degrees for unique patient cases, seven of which are presented in depth for this project. In summary: Environmental protection plays a critical initial role in drug resistance, which is followed by increase in tumor genetic heterogeneity as a result of mutations and drug-induced Darwinian selection. Eventually, environment-independent drug resistant subpopulations emerge, allowing the tumor to spread to unexplored areas of the bone marrow while maintaining inherited drug resistant phenotype2. It is our hope that these findings will help in shifting perspective regarding optimal management of MM by finding new therapeutic procedures that address all aspects of drug resistance to minimize chance of relapse and improve quality of life for patients.
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Douali, Nassim. "Conception et évaluation des méthodes et des systèmes d'aide a la décision pour une médecine personnalisée." Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066083.

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La sécurité est une première étape essentielle dans l'amélioration de la qualité des soins. Devant l’importance de ces erreurs qui menacent 12 millions de patients aux USA chaque année ; plusieurs travaux ont essayé de trouver des solutions pour réduire les erreurs médicales et les effets indésirables des médicaments : La médecine basée sur la preuve, la médecine personnalisée et les systèmes d’aide à la décision médicale. Les sociétés savantes élaborent de façon périodique des recommandations de bonnes pratiques pour aboutir à instaurer une médecine basée sur la preuve. Ces recommandations sont considérées comme des outils efficaces pour faire pont entre la pratique médicale des praticiens et les preuves scientifiques.La simple diffusion de GBPC n’a qu’un impact limité sur les pratiques cliniques.Plusieurs études ont montré que l’informatisation de ces guides en les intégrant dans le Workflow clinique permet d’améliorer l’adhérence des médecins à ces recommandations. Les guides de bonnes pratiques cliniques ne couvrent pas les caractéristiques individuelles des patients. Un des objectifs d’amélioration des soins et de la réduction des effets indésirables des patients est la personnalisation de la prise en charge. Cette personnalisation nécessite l’utilisation de toutes les informations (cliniques, biologiques, génétiques, radiologiques, sociales..) pour caractériser le profil du patient. Nous avons développé une méthode de raisonnement hybride, CBFCM, capable d’utiliser des connaissances et des données hétérogènes. L’implémentation de la méthode a été faite avec des outils du web sémantique. Nous avons développé un environnement Open Source pour la modélisation et la formalisation des connaissances médicales (recommandations..). Nous avons validé la méthode avec plusieurs études dans le domaine des infections urinaires mais aussi dans d’autres domaines (pneumologie, stéatose hépatique non alcoolique, diabète gestationnel..). L’intégration des données génétiques, cliniques et biologiques nous a permis d’améliorer la prédiction de certaines maladies (NASH)
Several studies have tried to find ways to reduce medical and adverse drug errors:The evidence-based medicine, personalized medicine and clinical decision support systems. Many recommandations are developped periodically to improve a best practices. These recommendations are considered effective tools to bridge between medical practitioners and practice of scientific evidence. The use of the Clinical Practice Guidelines has a limited impact on clinical practice. Several studies showed that the computerization of these guides by integrating them into the clinical workflow improves adherence of physicians to these recommendations.One of the aims of improving care and reducing adverse effects of patients is personalizing care. This customization requires the use of all the information (clinical, biological, genetic, radiological, social..) to characterize the profile of the patient.We have developed a method of hybrid reasoning "Case Based Fuzzy CognitiveMaps" able to use knowledge and heterogeneous data. The implementation of themethod was made with semantic web technologies. We have developed an open source environment for modeling and formalization of medical knowledge.We validated the method with several studies in the field of urinary tract infections,but also in other areas (respiratory, nonalcoholic fatty liver disease, gestational diabetes..). The integration of genetic, clinical and laboratory data have allowed us to improve the prediction of certain diseases (NASH)
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Banwell, Linda M. "PLUS : a system architecture for Personalized Library User Support." Thesis, University of Exeter, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359596.

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Rico-Fontalvo, Florentino Antonio. "A Decision Support Model for Personalized Cancer Treatment." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5621.

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This work is motivated by the need of providing patients with a decision support system that facilitates the selection of the most appropriate treatment strategy in cancer treatment. Treatment options are currently subject to predetermined clinical pathways and medical expertise, but generally, do not consider the individual patient characteristics or preferences. Although genomic patient data are available, this information is rarely used in the clinical setting for real-life patient care. In the area of personalized medicine, the advancement in the fundamental understanding of cancer biology and clinical oncology can promote the prevention, detection, and treatment of cancer diseases. The objectives of this research are twofold. 1) To develop a patient-centered decision support model that can determine the most appropriate cancer treatment strategy based on subjective medical decision criteria, and patient's characteristics concerning the treatment options available and desired clinical outcomes; and 2) to develop a methodology to organize and analyze gene expression data and validate its accuracy as a predictive model for patient's response to radiation therapy (tumor radiosensitivity). The complexity and dimensionality of the data generated from gene expression microarrays requires advanced computational approaches. The microarray gene expression data processing and prediction model is built in four steps: response variable transformation to emphasize the lower and upper extremes (related to Radiosensitive and Radioresistant cell lines); dimensionality reduction to select candidate gene expression probesets; model development using a Random Forest algorithm; and validation of the model in two clinical cohorts for colorectal and esophagus cancer patients. Subjective human decision-making plays a significant role in defining the treatment strategy. Thus, the decision model developed in this research uses language and mechanisms suitable for human interpretation and understanding through fuzzy sets and degree of membership. This treatment selection strategy is modeled using a fuzzy logic framework to account for the subjectivity associated to the medical strategy and the patient's characteristics and preferences. The decision model considers criteria associated to survival rate, adverse events and efficacy (measured by radiosensitivity) for treatment recommendation. Finally, a sensitive analysis evaluates the impact of introducing radiosensitivity in the decision-making process. The intellectual merit of this research stems from the fact that it advances the science of decision-making by integrating concepts from the fields of artificial intelligence, medicine, biology and biostatistics to develop a decision aid approach that considers conflictive objectives and has a high practical value. The model focuses on criteria relevant to cancer treatment selection but it can be modified and extended to other scenarios beyond the healthcare environment.
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Tzavaras, Aris. "Intelligent decision support systems in ventilation management." Thesis, City University London, 2009. http://openaccess.city.ac.uk/12084/.

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Introduction: Intensive Care Unit (ICU) medical personnel, in an ongoing process termed ventilation management, utilize patient physiology and pathology data to define ventilator apparatus settings. Aims: The aim of the research is to develop and evaluate in comparison hybrid ventilation advisor systems, that could support ventilation management process, specific to lung pathology for patients ventilated in control mode. Methodology: A questionnaire was designed and circulated to Intensivists. Patient data, as defined by the questionnaire analysis, were collected and categorized into three lung pathologies. Three ICU doctors evaluated correlation analysis of the recorded data. Evaluation results were used for identifying models basic architecture. Two custom software toolboxes were developed for developing hybrid systems; namely the EVolution Of Fuzzy INference Engines (EVOFINE) and the FUzzy Neural (FUN) toolbox. Eight hybrid systems developed with EVOFINE, FUN, ANFIS and ANN techniques were evaluated against applied clinical decisions and patient scenarios. Results: Seventeen (17) models were designed for each of the eight (8) modeling techniques. The modelled process consisted of twelve physiology variables and six ventilator settings. The number of models’ inputs ranged from single to six based on correlation and evaluation findings. Evaluation against clinical recommendations has shown that ANNs performed better; mean average error as percentage for four of the applied techniques was 0.16%, 1.29% & 0.62 for ANN empirical, 0.05%, 2.23% & 2.30% for ANFIS, 0.93%, 2.33% & 1.89% for EVOFINE and 0.73%, 2.63% & 6.56 for FUN NM, in Normal, COPD and ALI-ARDS categories respectively. Additionally evaluation against clinical disagreement SD has shown that 70.6% of the NN empirical models were performing in 90% of their suggestions within clinical SD, while the percentages were 53%, 53% and 59% for the EVOFINE, ANFIS and NN Normalized models respectively. The EVOFINE and ANFIS produced Fuzzy Systems whose architecture is transparent for the user. Visual observation of ANFIS architectures revealed possibly hazardous advices. Evaluation against clinical disagreement has shown that the NN empirical was not producing hazardous advices, while EVOFINE, ANFIS and NN Normalized were shown to produce potentially hazardous advice in 17.6%, 23% and 5.8% of the developed models.
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McMinn, Megan. "Assessing Health Behavior Modification for Participants in the OSU-Coriell Personalized Medicine Collaborative Following Genomic Counseling." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu149226309823361.

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Nirantharakumar, Krishnarajah. "Clinical decision support systems in the care of hospitalised patients with diabetes." Thesis, University of Birmingham, 2013. http://etheses.bham.ac.uk//id/eprint/4734/.

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This thesis explored the role of health informatics (decision support systems) in caring for hospitalised patients with diabetes through a systematic review and by analysing data from University Hospital Birmingham, UK. Findings from the thesis: 1) highlight the potential role of computerised physician order entry system in improving guideline based anti-diabetic medication prescription in particular insulin prescription, and their effectiveness in contributing to better glycaemic control; 2) quantify the occurrence of missed discharge diagnostic codes for diabetes using electronic prescription data and suggests 60% of this could be potentially reduced using an algorithm that could be introduced as part of the information system; 3) found that hypoglycaemia and foot disease in hospitalised diabetes patients were independently associated with higher in-hospital mortality rates and longer length of stay; 4) quantify the hypoglycaemia rates in non-diabetic patients and proposes one method of establishing a surveillance system to identify non diabetic hypoglycaemic patients; and 5) introduces a prediction model that may be useful to identify patients with diabetes at risk of poor clinical outcomes during their hospital stay.
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Walton, Robert Thompson. "Computerised decision support systems to give advice to doctors about drug therapy." Thesis, Queen Mary, University of London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287572.

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Schärfe, Charlotta Pauline Irmgard [Verfasser], and Oliver [Akademischer Betreuer] Kohlbacher. "Towards Personalized Medicine : Computational Approaches to Support Drug Design and Clinical Decision Making / Charlotta Pauline Irmgard Schärfe ; Betreuer: Oliver Kohlbacher." Tübingen : Universitätsbibliothek Tübingen, 2019. http://d-nb.info/1176510053/34.

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Books on the topic "Personalized medicine support systems"

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Bodiroga-Vukobrat, Nada, Daniel Rukavina, Krešimir Pavelić, and Gerald G. Sander, eds. Personalized Medicine in Healthcare Systems. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16465-2.

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Rienhoff, O., U. Piccolo, and B. Schneider, eds. Expert Systems and Decision Support in Medicine. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-48706-4.

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Couvreur, Patrick, and Simona Mura. Nanotheranostics for personalized medicine. New Jersey: World Scientific, 2015.

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1946-, Berner Eta S., ed. Clinical decision support systems: Theory and practice. New York: Springer, 1999.

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Clercq, Paul Adrianus de. Guideline-based decision support in medicine: Modeling guidelines for the development and application of clinical decision support systems. Eindhoven: Technische Universiteit Eindhoven, 2003.

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Brahnam, Sheryl. Advanced Computational Intelligence Paradigms in Healthcare 5: Intelligent Decision Support Systems. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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Intelligent sytems modeling and decision support in bioengineering. Boston, MA: Artech House, 2006.

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Rationalizing medical work: Decision-support techniques and medical practices. Cambridge, Mass: MIT Press, 1997.

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IEEE, Symposium on Computer-Based Medical Systems (17th 2004 Bethesda Md ). CBMS 2004: Proceedings : 17th IEEE Symposium on Computer-Based Medical Systems : 24-25 June, 2004, Bethesda, Maryland. Los Alamitos, Calif: IEEE Computer Society, 2004.

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IEEE Symposium on Computer-Based Medical Systems (14th 2001 Bethesda, Md.). Proceedings, 14th IEEE Symposium on Computer-Based Medical Systems: CBMS 2001 : 26-27 July 2001, Bethesda, Maryland. Los Alamitos, Calif: IEEE Computer Society, 2001.

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Book chapters on the topic "Personalized medicine support systems"

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Javorník, Michal, Otto Dostál, and Aleš Roček. "Probabilistic Modelling and Decision Support in Personalized Medicine." In Intelligent Systems for Sustainable Person-Centered Healthcare, 211–23. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-79353-1_11.

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AbstractThe concept of personalized medicine, often called the biggest revolution in medicine, is becoming an emerging practice. The article presents personalized medicine in a broader context as an interdisciplinary issue covering the current trends of information and communication technology in medicine, legal aspects, and probabilistic network modelling. Employing the concept of probabilistic network reasoning means extracting the meaningful knowledge, mathematizing it, incorporating the particular patient information and then using inference mechanisms of the created mathematical model for personalized decision support. Bayesian networks can serve as a multidimensional decision support framework representing the real-world medical domain. Their power, together with the possibilities of global sharing of necessary medical knowledge, represents a promising approach of extracting new, often hidden, knowledge about the given medical domain and thus opens up new ways of achieving the delivery of personalized medicine. Establishing patient diagnosis and treatment prognoses are the critical issues in personalized decision support. Mathematical modelling is beginning to play an irreplaceable role here.
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Ali, Dhafer Ben, Itebeddine Ghorbel, Nebras Gharbi, Kais Belhaj Hmida, Faiez Gargouri, and Lotfi Chaari. "Consolidated Clinical Document Architecture: Analysis and Evaluation to Support the Interoperability of Tunisian Health Systems." In Advances in Predictive, Preventive and Personalised Medicine, 43–52. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11800-6_5.

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Overby, Casey L. "Personalized Medicine." In Encyclopedia of Systems Biology, 1678–80. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_237.

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Tranvåg, Eirik Joakim, and Roger Strand. "Rationing of Personalised Cancer Drugs: Rethinking the Co-production of Evidence and Priority Setting Practices." In Human Perspectives in Health Sciences and Technology, 235–50. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92612-0_14.

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AbstractRising health care costs is a challenge for all health care systems, and new and expensive cancer drugs is an important contributor to this. Many countries – like Norway – have therefore established priority setting institutions and systems for drug appraisals where equal treatment, neutrality and transparency are key values. Despite this, controversy surrounding drug reimbursement decisions are persistent.The development of personalised cancer medicine is seen by many as a potential solution to difficult priority setting decisions, by tailoring the right drug to the right patient at the right time. We, however, see personalised oncology and medicine in general not only as a solution, but also as a potential contributor high costs and to persisting controversy. We will argue that attempts to improve and strengthen the priority setting system – without accepting that a wider perspective on science and society is required – is likely to fuel even more controversy.In contrast, our suggestion takes a different approach building on post-normal science. From a co-production perspective, scientific, technological and societal developments are causally entangled into each other. Alongside refining priority setting principles, one can and ought to raise normative questions about the trajectory of personalised cancer medicine and of how to create a well-functioning public sphere. How can we imagine a well-functioning system of technological development and health care priority setting? Which changes in research policy and funding could support such a system? And which properties could biomarkers have in order to help society manage the health gap?
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Rathi, Preeti, Deepanshu Verma, Ashutosh Singh, and Neha Garg. "Microbiome for Personalized Medicine." In Metagenomic Systems Biology, 141–57. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8562-3_7.

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Béranger, Jérôme. "Ethics-Oriented Personalized Medicine." In Medical Information Systems Ethics, 165–219. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119178224.ch4.

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Mance, Davor, Diana Mance, and Dinko Vitezić. "Personalized Medicine and Personalized Pricing: Degrees of Price Discrimination." In Personalized Medicine in Healthcare Systems, 171–80. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16465-2_14.

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Webb, Craig Paul, and David Michael Cherba. "Systems Biology of Personalized Medicine." In Bioinformatics for Systems Biology, 615–30. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-59745-440-7_32.

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Prakash, Omar, and S. Meiyappan. "Anesthesia Support Systems." In Computers and Medicine, 309–19. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2698-7_20.

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Pavelić, Krešimir, Željko Perdija, and Sandra Kraljević Pavelić. "Barriers Towards New Medicine: Personalized and Integrative Medicine Concepts." In Personalized Medicine in Healthcare Systems, 227–39. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16465-2_19.

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Conference papers on the topic "Personalized medicine support systems"

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Douali, Nassim, and Marie-Christine Jaulent. "Genomic and personalized medicine decision support system." In 2012 International Conference on Complex Systems (ICCS). IEEE, 2012. http://dx.doi.org/10.1109/icocs.2012.6458611.

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Pugalendhi, Ganesh Kumar, and Ku-Jin Kim. "Computational support system for personalized medicine." In BCB '15: ACM International Conference on Bioinformatics, Computational Biology and Biomedicine. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2808719.2811422.

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Kouris, I., C. Tsirmpas, S. G. Mougiakakou, D. Iliopoulou, and D. Koutsouris. "E-Health towards ecumenical framework for personalized medicine via Decision Support System." In 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010). IEEE, 2010. http://dx.doi.org/10.1109/iembs.2010.5626308.

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Gribova, Valeriya, Dmitriy Okun', and Roman Kovalev. "PRINCIPLES AND ARCHITECTURE OF THE SPECIALIZED SHELL FOR BUILDING INTELLIGENT SYSTEMS FOR TREATMENT PRESCRIBE." In XIV International Scientific Conference "System Analysis in Medicine". Far Eastern Scientific Center of Physiology and Pathology of Respiration, 2020. http://dx.doi.org/10.12737/conferencearticle_5fe01d9bd6c696.88986403.

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The paper describes the basic principles of development and architecture of an intelligent medical decision support system based on a specialized shell. The system allows you to prescribe a personalized treatment in various fields of medicine. The system is based on the ontological approach and uses generally accepted medical terminology to form knowledge
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Yesha, Yelena, Vandana P. Janeja, Naphtali Rishe, and Yaacov Yesha. "Personalized Decision Support System to Enhance Evidence Based Medicine through Big Data Analytics." In 2014 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2014. http://dx.doi.org/10.1109/ichi.2014.71.

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Storz, Philip, Sandra Wickner, Benjamin Batt, Johannes Schuh, Denise Junger, Yvonne Möller, Nisar Malek, and Christian Thies. "bwHealthApp: A Software System to Support Personalized Medicine by Individual Monitoring of Vital Parameters of Outpatients." In 14th International Conference on Health Informatics. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010324106130620.

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Tsiknakis, Manolis, Stelios Sfakianakis, Kostas Marias, and Norbert Graf. "A technical infrastructure to support personalized medicine." In 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE). IEEE, 2012. http://dx.doi.org/10.1109/bibe.2012.6399763.

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Di Tucci, Lorenzo, Giulia Guidi, Sara Notargiacomo, Luca Cerina, Alberto Scolari, and Marco D. Santambrogio. "HUGenomics: A support to personalized medicine research." In 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry - Innovation to Shape the Future for Society and Industry (RTSI). IEEE, 2017. http://dx.doi.org/10.1109/rtsi.2017.8065925.

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Ellingson, Roger M., Wendy J. Helt, Patrick V. Helt, and Stephen A. Fausti. "Instrumentation System Upgrade Supports Mobile Personalized Healthcare Delivery." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.259474.

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Ellingson, Roger M., Wendy J. Helt, Patrick V. Helt, and Stephen A. Fausti. "Instrumentation System Upgrade Supports Mobile Personalized Healthcare Delivery." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.4398883.

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Reports on the topic "Personalized medicine support systems"

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Adleh, Fadi, and Diane Duclos. Key Considerations: Supporting ‘Wheat-to-Bread’ Systems in Fragmented Syria. SSHAP, July 2022. http://dx.doi.org/10.19088/sshap.2022.027.

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Since the end of 2021, the food crisis in Syria has worsened. Humanitarian agencies working in Syria, as well as other experts, have warned the food crisis could rapidly lead to famine unless immediately addressed. This brief describes the social and political dimensions of food insecurity in Syria. It provides insights into how territorial fragmentation affects wheat-to-bread systems, outlines key threats to wheat production, and sets out key considerations for the humanitarian sector, researchers, and donors responding to the crisis. Sources for this brief include published papers, reports, media articles, and open-source datasets. It also draws on consultations with farmers and other experts that were conducted in November and December 2021. Consultations were held across the three main areas of control in Syria: North East Syria, North West Syria, and territories controlled by the government of Syria. This briefing was written by Fadi Adleh (independent researcher) and Diane Duclos (London School of Hygiene and Tropical Medicine) for the Social Science in Humanitarian Action Platform (SSHAP). It was reviewed externally by Edward Thomas (Rift Valley Institute) and support for field assessments was provided by Ali Ahmad (agronomist). The briefing was edited by Victoria Haldane and Leslie Jones (Anthrologica) and internally reviewed by Santiago Ripoll, Melissa Parker, and Annie Wilkinson. The brief is the responsibility of SSHAP.
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Marienko, Maiia V., Yulia H. Nosenko, and Mariya P. Shyshkina. Personalization of learning using adaptive technologies and augmented reality. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4418.

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The research is aimed at developing the recommendations for educators on using adaptive technologies and augmented reality in personalized learning implementation. The latest educational technologies related to learning personalization and the adaptation of its content to the individual needs of students and group work are considered. The current state of research is described, the trends of development are determined. Due to a detailed analysis of scientific works, a retrospective of the development of adaptive and, in particular, cloud-oriented systems is shown. The preconditions of their appearance and development, the main scientific ideas that contributed to this are analyzed. The analysis showed that the scientists point to four possible types of semantic interaction of augmented reality and adaptive technologies. The adaptive cloud-based educational systems design is considered as the promising trend of research. It was determined that adaptability can be manifested in one or a combination of several aspects: content, evaluation and consistency. The cloud technology is taken as a platform for integrating adaptive learning with augmented reality as the effective modern tools to personalize learning. The prospects of the adaptive cloud-based systems design in the context of teachers training are evaluated. The essence and place of assistive technologies in adaptive learning systems design are defined. It is shown that augmented reality can be successfully applied in inclusive education. The ways of combining adaptive systems and augmented reality tools to support the process of teachers training are considered. The recommendations on the use of adaptive cloud-based systems in teacher education are given.
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Modlo, Yevhenii O., Serhiy O. Semerikov, Stanislav L. Bondarevskyi, Stanislav T. Tolmachev, Oksana M. Markova, and Pavlo P. Nechypurenko. Methods of using mobile Internet devices in the formation of the general scientific component of bachelor in electromechanics competency in modeling of technical objects. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3677.

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An analysis of the experience of professional training bachelors of electromechanics in Ukraine and abroad made it possible to determine that one of the leading trends in its modernization is the synergistic integration of various engineering branches (mechanical, electrical, electronic engineering and automation) in mechatronics for the purpose of design, manufacture, operation and maintenance electromechanical equipment. Teaching mechatronics provides for the meaningful integration of various disciplines of professional and practical training bachelors of electromechanics based on the concept of modeling and technological integration of various organizational forms and teaching methods based on the concept of mobility. Within this approach, the leading learning tools of bachelors of electromechanics are mobile Internet devices (MID) – a multimedia mobile devices that provide wireless access to information and communication Internet services for collecting, organizing, storing, processing, transmitting, presenting all kinds of messages and data. The authors reveals the main possibilities of using MID in learning to ensure equal access to education, personalized learning, instant feedback and evaluating learning outcomes, mobile learning, productive use of time spent in classrooms, creating mobile learning communities, support situated learning, development of continuous seamless learning, ensuring the gap between formal and informal learning, minimize educational disruption in conflict and disaster areas, assist learners with disabilities, improve the quality of the communication and the management of institution, and maximize the cost-efficiency. Bachelor of electromechanics competency in modeling of technical objects is a personal and vocational ability, which includes a system of knowledge, skills, experience in learning and research activities on modeling mechatronic systems and a positive value attitude towards it; bachelor of electromechanics should be ready and able to use methods and software/hardware modeling tools for processes analyzes, systems synthesis, evaluating their reliability and effectiveness for solving practical problems in professional field. The competency structure of the bachelor of electromechanics in the modeling of technical objects is reflected in three groups of competencies: general scientific, general professional and specialized professional. The implementation of the technique of using MID in learning bachelors of electromechanics in modeling of technical objects is the appropriate methodic of using, the component of which is partial methods for using MID in the formation of the general scientific component of the bachelor of electromechanics competency in modeling of technical objects, are disclosed by example academic disciplines “Higher mathematics”, “Computers and programming”, “Engineering mechanics”, “Electrical machines”. The leading tools of formation of the general scientific component of bachelor in electromechanics competency in modeling of technical objects are augmented reality mobile tools (to visualize the objects’ structure and modeling results), mobile computer mathematical systems (universal tools used at all stages of modeling learning), cloud based spreadsheets (as modeling tools) and text editors (to make the program description of model), mobile computer-aided design systems (to create and view the physical properties of models of technical objects) and mobile communication tools (to organize a joint activity in modeling).
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Leavy, Michelle B., Costas Boussios, Robert L. Phillips, Jr., Diana Clarke, Barry Sarvet, Aziz Boxwala, and Richard Gliklich. Outcome Measure Harmonization and Data Infrastructure for Patient-Centered Outcomes Research in Depression: Final Report. Agency for Healthcare Research and Quality (AHRQ), June 2022. http://dx.doi.org/10.23970/ahrqepcwhitepaperdepressionfinal.

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Objective. The objective of this project was to demonstrate the feasibility and value of collecting harmonized depression outcome measures in the patient registry and health system settings, displaying the outcome measures to clinicians to support individual patient care and population health management, and using the resulting measures data to support patient-centered outcomes research (PCOR). Methods. The harmonized depression outcome measures selected for this project were response, remission, recurrence, suicide ideation and behavior, adverse effects of treatment, and death from suicide. The measures were calculated in the PRIME Registry, sponsored by the American Board of Family Medicine, and PsychPRO, sponsored by the American Psychiatric Association, and displayed on the registry dashboards for the participating pilot sites. At the conclusion of the data collection period (March 2020-March 2021), registry data were analyzed to describe implementation of measurement-based care and outcomes in the primary care and behavioral health care settings. To calculate and display the measures in the health system setting, a Substitutable Medical Apps, Reusable Technology (SMART) on Fast Healthcare Interoperability Resource (FHIR) application was developed and deployed at Baystate Health. Finally a stakeholder panel was convened to develop a prioritized research agenda for PCOR in depression and to provide feedback on the development of a data use and governance toolkit. Results. Calculation of the harmonized outcome measures within the PRIME Registry and PsychPRO was feasible, but technical and operational barriers needed to be overcome to ensure that relevant data were available and that the measures were meaningful to clinicians. Analysis of the registry data demonstrated that the harmonized outcome measures can be used to support PCOR across care settings and data sources. In the health system setting, this project demonstrated that it is technically and operationally feasible to use an open-source app to calculate and display the outcome measures in the clinician’s workflow. Finally, this project produced tools and resources to support future implementations of harmonized measures and use of the resulting data for research, including a prioritized research agenda and data use and governance toolkit. Conclusion. Standardization of outcome measures across patient registries and routine clinical care is an important step toward creating robust, national-level data infrastructure that could serve as the foundation for learning health systems, quality improvement initiatives, and research. This project demonstrated that it is feasible to calculate the harmonized outcome measures for depression in two patient registries and a health system setting, display the results to clinicians to support individual patient management and population health, and use the outcome measures data to support research. This project also assessed the value and burden of capturing the measures in different care settings and created standards-based tools and other resources to support future implementations of harmonized outcome measures in depression and other clinical areas. The findings and lessons learned from this project should serve as a roadmap to guide future implementations of harmonized outcome measures in depression and other clinical areas.
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Agu, Monica, Zita Ekeocha, Stephen Robert Byrn, and Kari L. Clase. The Impact of Mentoring as a GMP Capability Building Tool in The Pharmaceutical Manufacturing Industry in Nigeria. Purdue University, December 2012. http://dx.doi.org/10.5703/1288284317447.

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Good Manufacturing Practices (GMP), a component of Pharmaceutical Quality Systems, is aimed primarily at managing and minimizing the risks inherent in pharmaceutical manufacture to ensure the quality, safety and efficacy of products. Provision of adequate number of personnel with the necessary qualifications/practical experience and their continuous training and evaluation of effectiveness of the training is the responsibility of the manufacturer. (World Health Organization [WHO], 2014; International Organization for Standardization [ISO], 2015). The classroom method of training that has been used for GMP capacity building in the pharmaceutical manufacturing industry in Nigeria over the years, delivered by experts from stringently regulated markets, have not yielded commensurate improvement in the Quality Management Systems (QMS) in the industry. It is necessary and long over-due to explore an alternative training method that has a track record of success in other sectors. A lot of studies carried out on mentoring as a development tool in several fields such as academia, medicine, business, research etc., reported positive outcomes. The aim of this study was to explore mentoring as an alternative GMP training method in the pharmaceutical manufacturing industry in Nigeria. Specifically, the aim of this study was to evaluate the impact of mentoring as a GMP capability building tool in the pharmaceutical manufacturing industry in Nigeria, with focus on GMP documentations in XYZ pharmaceutical manufacturing company located in South-Western region of Nigeria. The methodology comprised gap assessment of GMP documentation of XYZ company to generate current state data, development of training materials based on the identified gaps and use of the training materials for the mentoring sessions. The outcome of the study was outstanding as gap assessment identified the areas of need that enabled development efforts to be targeted at these areas, unlike generic classroom training. The mentees’ acceptance of the mentoring support was evident by their request for additional training in some other areas related to the microbiology operations that were not covered in the gap assessment. This result portrays mentoring as a promising tool for GMP capacity building, but more structured studies need to be conducted in this area to generate results that can be generalized.
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