Relevant bibliographies by topics / Dose prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Dose prediction'

Author: Grafiati
Published: 24 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Dose prediction"

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Marek, Elizabeth, Jeremiah D. Momper, Ronald N. Hines, Cheryl M. Takao, Joan C. Gill, Vera Pravica, Andrea Gaedigk, Gilbert J. Burckart, and Kathleen A. Neville. "Prediction of Warfarin Dose in Pediatric Patients: An Evaluation of the Predictive Performance of Several Models." Journal of Pediatric Pharmacology and Therapeutics 21, no. 3 (May 1, 2016): 224–32. http://dx.doi.org/10.5863/1551-6776-21.3.224.

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OBJECTIVES: The objective of this study was to evaluate the performance of pediatric pharmacogenetic-based dose prediction models by using an independent cohort of pediatric patients from a multicenter trial. METHODS: Clinical and genetic data (CYP2C9 [cytochrome P450 2C9] and VKORC1 [vitamin K epoxide reductase]) were collected from pediatric patients aged 3 months to 17 years who were receiving warfarin as part of standard care at 3 separate clinical sites. The accuracy of 8 previously published pediatric pharmacogenetic-based dose models was evaluated in the validation cohort by comparing predicted maintenance doses to actual stable warfarin doses. The predictive ability was assessed by using the proportion of variance (R2), mean prediction error (MPE), and the percentage of predictions that fell within 20% of the actual maintenance dose. RESULTS: Thirty-two children reached a stable international normalized ratio and were included in the validation cohort. The pharmacogenetic-based warfarin dose models showed a proportion of variance ranging from 35% to 78% and an MPE ranging from −2.67 to 0.85 mg/day in the validation cohort. Overall, the model developed by Hamberg et al showed the best performance in the validation cohort (R2 = 78%; MPE = 0.15 mg/day) with 38% of the predictions falling within 20% of observed doses. CONCLUSIONS: Pharmacogenetic-based algorithms provide better predictions than a fixed-dose approach, although an optimal dose algorithm has not yet been developed.
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Swartz, Conrad M. "Drug Dose Prediction With Flexible Test Doses." Journal of Clinical Pharmacology 31, no. 7 (July 1991): 662–67. http://dx.doi.org/10.1002/j.1552-4604.1991.tb03753.x.

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&NA;. "IV aminoglycoside dose prediction." Inpharma Weekly &NA;, no. 995 (July 1995): 18. http://dx.doi.org/10.2165/00128413-199509950-00043.

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Qasim, Husam, Sophie Sominsky, Aharon Lubetsky, Noa Markovits, Chun Li, C. Stein, Hillel Halkin, Eva Gak, Ronen Loebstein, and Daniel Kurnik. "Effect of the VKORC1 D36Y variant on warfarin dose requirement and pharmacogenetic dose prediction." Thrombosis and Haemostasis 108, no. 10 (2012): 781–88. http://dx.doi.org/10.1160/th12-03-0151.

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SummaryPharmacogenetic dosing algorithms help predict warfarin maintenance doses, but their predictive performance differs in different populations, possibly due to unsuspected population-specific genetic variants. The objectives of this study were to quantify the effect of the VKORC1 D36Y variant (a marker of warfarin resistance previously described in 4% of Ashkenazi Jews) on warfarin maintenance doses and to examine how this variant affects the performance of the International Warfarin Pharmacogenetic Consortium (IWPC) dose prediction model. In 210 Israeli patients on chronic warfarin therapy recruited at a tertiary care centre, we applied the IWPC model and then added D36Y genotype as covariate to the model (IWPC+D36Y) and compared predicted with actual doses. Median weekly warfarin dose was 35 mg (interquartile range [IQR], 24.5 to 52.5 mg). Among 16 heterozygous D36Y carriers (minor allele frequency = 3.8%), warfarin weekly dose was increased by a median of 43.7 mg (IQR, 40.5 to 47.2 mg) compared to non-carriers after adjustment for all IWPC parameters, a greater than two-fold dose increase. The IWPC model performed suboptimally (coefficient of determination R2=27.0%; mean absolute error (MAE), 14.4 ± 16.2 mg/ week). Accounting for D36Y genotype using the IWPC+D36Y model resulted in a significantly better model performance (R2=47.2%, MAE=12.6±12.4 mg/week). In conclusion, even at low frequencies, variants with a strong impact on warfarin dose may greatly decrease the performance of a commonly used dose prediction model. Unexpected discrepancies of the performance of universal prediction models in subpopulations should prompt searching for unsuspected confounders, including rare genetic variants.
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Laidlaw, J., P. Bentham, G. Khan, V. Staples, A. Dhariwal, B. Coope, E. Day, C. Fear, C. Marley, and J. Stemman. "A comparison of stimulus dosing methods for electroconvulsive therapy." Psychiatric Bulletin 24, no. 5 (May 2000): 184–87. http://dx.doi.org/10.1192/pb.24.5.184.

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Aims and MethodsA prospective study comparing initial electroconvulsive therapy treatment doses determined by empirical dose titration with estimates derived from two simple dose prediction methods and a fixed-dose regimen (275 mC).ResultsThirty-three patients had seizure thresholds between 25 mC and 403 mC. The dose titration method led to a mean initial treatment dose of 195 mC that was intermediate between those predicted by the age method (275 mC) and the half-age method (137 mC). Estimates were within acceptable limits in 33% of cases for the age method, 64% for the half-age method and 40% for the fixed-dose method.Clinical ImplicationsEither dose prediction or dose titration methods may be more appropriate in different clinical situations. The half-age method appears to be a more accurate predictor of optimum initial treatment dose.
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Gizynska, M., D. Blatkiewicz, B. Czyzew, M. Galecki, M. Gil-Ulkowska, P. Kukolowicz, and M. Ziemek. "EP-1510: Cumulated dose prediction." Radiotherapy and Oncology 115 (April 2015): S822—S823. http://dx.doi.org/10.1016/s0167-8140(15)41502-6.

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Matsumoto, Hiroshi, Yoshikuni Yakabe, Fumiyo Saito, Koichi Saito, Kayo Sumida, Masaru Sekijima, Koji Nakayama, Hideki Miyaura, Masanori Otsuka, and Tomoyuki Shirai. "New Short Term Prediction Method for Chemical Carcinogenicity by Hepatic Transcript Profiling following 28-Day Toxicity Tests in Rats." Cancer Informatics 10 (January 2011): CIN.S7789. http://dx.doi.org/10.4137/cin.s7789.

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We have previously shown the hepatic gene expression profiles of carcinogens in 28-day toxicity tests were clustered into three major groups (Group-1 to 3). Here, we developed a new prediction method for Group-1 carcinogens which consist mainly of genotoxic rat hepatocarcinogens. The prediction formula was generated by a support vector machine using 5 selected genes as the predictive genes and predictive score was introduced to judge carcinogenicity. It correctly predicted the carcinogenicity of all 17 Group-1 chemicals and 22 of 24 non-carcinogens regardless of genotoxicity. In the dose-response study, the prediction score was altered from negative to positive as the dose increased, indicating that the characteristic gene expression profile emerged over a range of carcinogen-specific doses. We conclude that the prediction formula can quantitatively predict the carcinogenicity of Group-1 carcinogens. The same method may be applied to other groups of carcinogens to build a total system for prediction of carcinogenicity.
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Xie, Cheng, Ling Xue, Yuzhen Zhang, Jianguo Zhu, Ling Zhou, Yongfu Hang, Xiaoliang Ding, Bin Jiang, and Liyan Miao. "Comparison of the prediction performance of different warfarin dosing algorithms based on Chinese patients." Pharmacogenomics 21, no. 1 (January 2020): 23–32. http://dx.doi.org/10.2217/pgs-2019-0124.

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Aim: To compare the prediction performance of different warfarin dosing algorithms based on Chinese patients. Materials & methods: A total of 18 algorithms were tested in 325 patients. The predictive efficacy of selected algorithms was evaluated by calculating the percentage of patients whose predicted dose fell within ±20% of their actual stable warfarin dose and the mean absolute error. Results: The percentage within ± 20% and the mean absolute error of the algorithms ranged from 11.9 to 41.2% and -0.20 (-0.29 to -0.11) mg/d to -1.63 (-1.75 to -1.50) mg/d. The algorithms established by Miao et al. and Wei et al. had optimal predictive performance. Conclusion: Algorithms based on geographical populations might be more suitable for the prediction of stable warfarin doses in local patients.
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Holford, Nick H. G., Shu C. Ma, and Brian J. Anderson. "Prediction of morphine dose in humans." Pediatric Anesthesia 22, no. 3 (December 28, 2011): 209–22. http://dx.doi.org/10.1111/j.1460-9592.2011.03782.x.

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OMORI, Toshiaki, Shinsuke KATO, Minsik KIM, and Shigehiro NUKATSUKA. "RADIATION DOSE PREDICTION FOR DETACHED HOUSES." Journal of Environmental Engineering (Transactions of AIJ) 82, no. 735 (2017): 481–89. http://dx.doi.org/10.3130/aije.82.481.

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Dissertations / Theses on the topic "Dose prediction"

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Eriksson, Niclas. "On the Prediction of Warfarin Dose." Doctoral thesis, Uppsala universitet, Klinisk farmakologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-172864.

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Warfarin is one of the most widely used anticoagulants in the world. Treatment is complicated by a large inter-individual variation in the dose needed to reach adequate levels of anticoagulation i.e. INR 2.0 – 3.0. The objective of this thesis was to evaluate which factors, mainly genetic but also non-genetic, that affect the response to warfarin in terms of required maintenance dose, efficacy and safety with special focus on warfarin dose prediction. Through candidate gene and genome-wide studies, we have shown that the genes CYP2C9 and VKORC1 are the major determinants of warfarin maintenance dose. By combining the SNPs CYP2C9 *2, CYP2C9 *3 and VKORC1 rs9923231 with the clinical factors age, height, weight, ethnicity, amiodarone and use of inducers (carbamazepine, phenytoin or rifampicin) into a prediction model (the IWPC model) we can explain 43 % to 51 % of the variation in warfarin maintenance dose. Patients requiring doses < 29 mg/week and doses ≥ 49 mg/week benefitted the most from pharmacogenetic dosing. Further, we have shown that the difference across ethnicities in percent variance explained by VKORC1 was largely accounted for by the allele frequency of rs9923231. Other novel genes affecting maintenance dose (NEDD4 and DDHD1), as well as the replicated CYP4F2 gene, have small effects on dose predictions and are not likely to be cost-effective, unless inexpensive genotyping is available. Three types of prediction models for warfarin dosing exist: maintenance dose models, loading dose models and dose revision models. The combination of these three models is currently being used in the warfarin treatment arm of the European Pharmacogenetics of Anticoagulant Therapy (EU-PACT) study. Other clinical trials aiming to prove the clinical validity and utility of pharmacogenetic dosing are also underway. The future of pharmacogenetic warfarin dosing relies on results from these ongoing studies, the availability of inexpensive genotyping and the cost-effectiveness of pharmacogenetic driven warfarin dosing compared with new oral anticoagulant drugs.
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SKARPMAN, MUNTER JOHANNA. "Dose-Volume Histogram Prediction using KernelDensity Estimation." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-155893.

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Dose plans developed for stereotactic radiosurgery are assessed by studying so called Dose-Volume Histograms. Since it is hard to compare an individual dose plan with doseplans created for other patients, much experience and knowledge is lost. This thesis therefore investigates a machine learning approach to predicting such Dose-Volume Histograms for a new patient, by learning from previous dose plans.The training set is chosen based on similarity in terms of tumour size. The signed distances between voxels in the considered volume and the tumour boundary decide the probability of receiving a certain dose in the volume. By using a method based on Kernel Density Estimation, the intrinsic probabilistic properties of a Dose-Volume Histogramare exploited.Dose-Volume Histograms for the brainstem of 22 Acoustic Schwannoma patients, treated with the Gamma Knife,have been predicted, solely based on each patient’s individual anatomical disposition. The method has proved higher prediction accuracy than a “quick-and-dirty” approach implemented for comparison. Analysis of the bias and variance of the method also indicate that it captures the main underlying factors behind individual variations. However,the degree of variability in dose planning results for the Gamma Knife has turned out to be very limited. Therefore, the usefulness of a data driven dose planning tool for the Gamma Knife has to be further investigated.
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Nilsson, Viktor. "Prediction of Dose Probability Distributions Using Mixture Density Networks." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273610.

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In recent years, machine learning has become utilized in external radiation therapy treatment planning. This involves automatic generation of treatment plans based on CT-scans and other spatial information such as the location of tumors and organs. The utility lies in relieving clinical staff from the labor of manually or semi-manually creating such plans. Rather than predicting a deterministic plan, there is great value in modeling it stochastically, i.e. predicting a probability distribution of dose from CT-scans and delineated biological structures. The stochasticity inherent in the RT treatment problem stems from the fact that a range of different plans can be adequate for a patient. The particular distribution can be thought of as the prevalence in preferences among clinicians. Having more information about the range of possible plans represented in one model entails that there is more flexibility in forming a final plan. Additionally, the model will be able to reflect the potentially conflicting clinical trade-offs; these will occur as multimodal distributions of dose in areas where there is a high variance. At RaySearch, the current method for doing this uses probabilistic random forests, an augmentation of the classical random forest algorithm. A current direction of research is learning the probability distribution using deep learning. A novel parametric approach to this is letting a suitable deep neural network approximate the parameters of a Gaussian mixture model in each volume element. Such a neural network is known as a mixture density network. This thesis establishes theoretical results of artificial neural networks, mainly the universal approximation theorem, applied to the activation functions used in the thesis. It will then proceed to investigate the power of deep learning in predicting dose distributions, both deterministically and stochastically. The primary objective is to investigate the feasibility of mixture density networks for stochastic prediction. The research question is the following. U-nets and Mixture Density Networks will be combined to predict stochastic doses. Does there exist such a network, powerful enough to detect and model bimodality? The experiments and investigations performed in this thesis demonstrate that there is indeed such a network.
Under de senaste åren har maskininlärning börjat nyttjas i extern strålbehandlingsplanering. Detta involverar automatisk generering av behandlingsplaner baserade på datortomografibilder och annan rumslig information, såsom placering av tumörer och organ. Nyttan ligger i att avlasta klinisk personal från arbetet med manuellt eller halvmanuellt skapa sådana planer. I stället för att predicera en deterministisk plan finns det stort värde att modellera den stokastiskt, det vill säga predicera en sannolikhetsfördelning av dos utifrån datortomografibilder och konturerade biologiska strukturer. Stokasticiteten som förekommer i strålterapibehandlingsproblemet beror på att en rad olika planer kan vara adekvata för en patient. Den särskilda fördelningen kan betraktas som förekomsten av preferenser bland klinisk personal. Att ha mer information om utbudet av möjliga planer representerat i en modell innebär att det finns mer flexibilitet i utformningen av en slutlig plan. Dessutom kommer modellen att kunna återspegla de potentiellt motstridiga kliniska avvägningarna; dessa kommer påträffas som multimodala fördelningar av dosen i områden där det finns en hög varians. På RaySearch används en probabilistisk random forest för att skapa dessa fördelningar, denna metod är en utökning av den klassiska random forest-algoritmen. En aktuell forskningsriktning är att generera in sannolikhetsfördelningen med hjälp av djupinlärning. Ett oprövat parametriskt tillvägagångssätt för detta är att låta ett lämpligt djupt neuralt nätverk approximera parametrarna för en Gaussisk mixturmodell i varje volymelement. Ett sådant neuralt nätverk är känt som ett mixturdensitetsnätverk. Den här uppsatsen fastställer teoretiska resultat för artificiella neurala nätverk, främst det universella approximationsteoremet, tillämpat på de aktiveringsfunktioner som används i uppsatsen. Den fortsätter sedan att utforska styrkan av djupinlärning i att predicera dosfördelningar, både deterministiskt och stokastiskt. Det primära målet är att undersöka lämpligheten av mixturdensitetsnätverk för stokastisk prediktion. Forskningsfrågan är följande. U-nets och mixturdensitetsnätverk kommer att kombineras för att predicera stokastiska doser. Finns det ett sådant nätverk som är tillräckligt kraftfullt för att upptäcka och modellera bimodalitet? Experimenten och undersökningarna som utförts i denna uppsats visar att det faktiskt finns ett sådant nätverk.
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Harris, Shelley A. "The development and validation of a pesticide dose prediction model." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape9/PQDD_0002/NQ41170.pdf.

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Irving, Benjamin. "Radiation dose measurement and prediction for linear slit scanning radiography." Master's thesis, University of Cape Town, 2008. http://hdl.handle.net/11427/3251.

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This study describes dose measurements made for linear slit scanning radiography (LSSR) and a dose prediction model that was developed for LSSR. The measurement and calculation methods used for determining entrance dose and effective dose (E) in conventional X-ray imaging systems were verified for use with LSSR. Entrance dose and E were obtained for LSSR and compared to dose measurements on conventional radiography units. Entrance dose measurements were made using an ionisation chamber and dosemeter; E was calculated from these entrance dose measurements using a Monte Carlo simulator. Comparisons with data from around the world showed that for most examinations the doses obtained for LSSR were considerably lower than those of conventional radiography units for the same image quality. Reasons for the low dose obtained with LSSR include scatter reduction and the beam geometry of LSSR. These results have been published as two papers in international peer reviewed journals. A new method to calculate entrance dose and effective dose for LSSR is described in the second part of this report. This method generates the energy spectrum for a particular set of technique factors, simulates a filter through which the beam is attenuated and then calculates entrance dose directly from this energy spectrum. The energy spectrum is then combined with previously generated organ energy absorption data for a standard sized patient to calculate effective dose to a standard sized patient.Energy imparted for different patient thicknesses can then be used to adjust the effective dose to a patient of any size. This method is performed for a large number of slit beams moving across the body in order to more effectively simulate LSSR. This also allows examinations with technique factors that vary for different parts of the anatomy to be simulated. This method was tested against measured data and Monte Carlo simulations. This model was shown to be accurate, while being specifically suited to LSSR and being considerably faster than Monte Carlo simulations.
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Eriksson, Ivar. "Image Distance Learning for Probabilistic Dose–Volume Histogram and Spatial Dose Prediction in Radiation Therapy Treatment Planning." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273608.

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Construction of radiotherapy treatments for cancer is a laborious and time consuming task. At the same time, when presented with a treatment plan, an oncologist can quickly judge whether or not it is suitable. This means that the problem of constructing these treatment plans is well suited for automation. This thesis investigates a novel way of automatic treatment planning. The treatment planning system this pipeline is constructed for provides dose mimicking functionality with probability density functions of dose–volume histograms (DVHs) and spatial dose as inputs. Therefore this will be the output of the pipeline. The input is historically treated patient scans, segmentations and spatial doses. The approach involves three modules which are individually replaceable with little to no impact on the remaining two modules. The modules are: an autoencoder as a feature extractor to concretise important features of a patient segmentation, a distance optimisation step to learn a distance in the previously constructed feature space and, finally, a probabilistic spatial dose estimation module using sparse pseudo-input Gaussian processes trained on voxel features. Although performance evaluation in terms of clinical plan quality was beyond the scope of this thesis, numerical results show that the proposed pipeline is successful in capturing salient features of patient geometry as well as predicting reasonable probability distributions for DVH and spatial dose. Its loosely connected nature also gives hope that some parts of the pipeline can be utilised in future work.
Skapandet av strålbehandlingsplaner för cancer är en tidskrävande uppgift. Samtidigt kan en onkolog snabbt fatta beslut om en given plan är acceptabel eller ej. Detta innebär att uppgiften att skapa strålplaner är väl lämpad för automatisering. Denna uppsats undersöker en ny metod för att automatiskt generera strålbehandlingsplaner. Planeringssystemet denna metod utvecklats för innehåller funktionalitet för dosrekonstruktion som accepterar sannolikhetsfördelningar för dos–volymhistogram (DVH) och dos som input. Därför kommer detta att vara utdatan för den konstruerade metoden. Metoden är uppbyggd av tre beståndsdelar som är individuellt utbytbara med liten eller ingen påverkan på de övriga delarna. Delarna är: ett sätt att konstruera en vektor av kännetecken av en patients segmentering, en distansoptimering för att skapa en distans i den tidigare konstruerade känneteckensrymden, och slutligen en skattning av sannolikhetsfördelningar med Gaussiska processer tränade på voxelkännetecken. Trots att utvärdering av prestandan i termer av klinisk plankvalitet var bortom räckvidden för detta projekt uppnåddes positiva resultat. De estimerade sannolikhetsfördelningarna uppvisar goda karaktärer för både DVHer och doser. Den löst sammankopplade strukturen av metoden gör det dessutom möjligt att delar av projektet kan användas i framtida arbeten.
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Patel, Raj B., and Raj B. Patel. "Prediction of Human Intestinal Absorption." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/624487.

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The proposed human intestinal absorption prediction model is applied to over 900 pharmaceuticals and has about 82.5% true prediction power. This study will provide a screening tool that can differentiate well absorbed and poorly absorbed drugs in the early stage of drug discovery and development. This model is based on fundamental physicochemical properties and can be applied to virtual compounds. The maximum well-absorbed dose (i.e., the maximum dose that will be more than 50 percent absorbed) calculated using this model can be utilized as a guideline for drug design, synthesis, and pre-clinical studies.
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Schuler, Paul Joseph. "Polymer dose prediction for sludge dewatering with a belt filter press." Thesis, Virginia Tech, 1990. http://hdl.handle.net/10919/42227.

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This study was undertaken to examine the polymer mixing requirements for sludge dewatering with a belt filter press. This involved correlating full-scale field studies to small scale laboratory testing. Bench testing involved the use of a high-speed mixer and two sludge dewatering response tests: the capillary suction time test and the time-to filter test. Full-scale testing measured the belt press response to belt speed, sludge throughput, and polymer dose. Data indicated that the conditioning and dewatering scheme of the three belt filter presses was a low shear, low total mixing energy operation. The Gt, or total mixing energy, of these operations was in the range of 8,000-12,000. Optimal dose predicted by the bench-scale testing correlated well to the optimal dose for maximum cake solids coming off the belt filter press. Also, the amount of water removed from the sludge with the belt press was largely a function of the type of solids present in the sludge and less of a function of the number of rollers or residence time in the press.
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Eriksson, Oskar. "Scenario dose prediction for robust automated treatment planning in radiation therapy." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302568.

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Cancer is a group of diseases that are characterized by abnormal cell growth and is considered a leading cause of death globally. There are a number of different cancer treatment modalities, one of which is radiation therapy. In radiation therapy treatment planning, it is important to make sure that enough radiation is delivered to the tumor and that healthy organs are spared, while also making sure to account for uncertainties such as misalignment of the patient during treatment. To reduce the workload on clinics, data-driven automated treatment planning can be used to generate treatment plans for new patients based on previously delivered plans. In this thesis, we propose a novel method for robust automated treatment planning where a deep learning model is trained to deform a dose in accordance with a set of potential scenarios that account for the different uncertainties while maintaining certain statistical properties of the input dose. The predicted scenario doses are then used in a robust optimization problem with the goal of finding a treatment plan that is robust to these uncertainties. The results show that the proposed method for deforming doses yields realistic doses of high quality and that the proposed pipeline can potentially generate doses that conform better to the target than the current state of the art but at the cost of dose homogeneity.
Cancer är ett samlingsnamn för sjukdomar som karaktäriseras av onormal celltillväxt och betraktas som en ledande dödsorsak globalt. Det finns olika typer av cancerbehandling, varav en är strålterapi. Inom strålterapiplanering är det viktigt att säkerställa att tillräckligt med strålning ges till tumören, att friska organ skonas, och att osäkerheter som felplacering av patienten under behandlingen räknas med. För att minska arbetsbelastningen på kliniker används data-driven automatisk strålterapiplanering för att generera behandlingsplaner till nya patienter baserat på tidigare levererade behandlingar. I denna uppsats föreslår vi en ny metod för robust automatisk strålterapiplanering där en djupinlärningsmodell tränas till att deformera en dos i enlighet med en mängd potentiella scenarion som motsvarar de olika osäkerheterna medan vissa statistiska egenskaper bibehålls från originaldosen. De predicerade scenariodoserna används sedan i ett robust optimeringsproblem där målet är att hitta en behandlingsplan som är robust mot dessa osäkerheter. Resultaten visar att den föreslagna metoden för dosdeformation ger realistiska doser av hög kvalitet, vilket i sin tur kan leda till robusta doser med högre doskonformitet än tidigare metoder men på bekostnad av doshomogenitet.
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McCurdy, Boyd Matthew Clark. "Development of a portal dose image prediction algorithm for arbitrary detector systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/NQ62655.pdf.

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Books on the topic "Dose prediction"

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Reid, J. A. Keith. The effects of age-dependent dose conversion factors from ICRP-72 on biosphere model dose predictions. Pinawa, Man: AECL, Whiteshell Laboratories, Environmental Science Branch, 1997.

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Seltzer, Stephen M. Technical progress report on predictions of dose from electrons in space ... [Washington, DC: National Aeronautics and Space Administration, 1992.

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Sparrow, Paul R. Does national culture really matter?: Predicting HRM preferences of Taiwanese employers. Sheffield: Sheffield University Management School, 1997.

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Kay, Helen. Does the validity of the selection system depend more on the criteria than the predictor? Manchester: UMIST, 1995.

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Ward, Peter L. The Loma Prieta earthquake of October 17, 1989: A brief geologic view of what caused the Loma Prieta earthquake and implications for future California earthquakes: what happened ... what is expected ... what can be done. [Reston, Va.]: U.S. Geological Survey, 1990.

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Barnoski, Robert P. Sex offender sentencing in Washington State: Does the prison treatment program reduce recidivism? Olympia, WA: Washington State Institute for Public Policy, 2006.

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Harris, Shelley Anne. The development and validation of a pesticide dose prediction model. 1999.

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Andrzej, Wojcik, and Colin J. Martin. Biological effects of ionizing radiation. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199655212.003.0003.

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Biological effects of radiation have been interpreted based on the assumption that DNA is the primary target, but recent research has shown that non-targeted mechanisms may affect cells that are not directly exposed. The most important effect in humans from low doses of radiation is the induction of cancer, but risks of other effects such as cataract and cardiac or circulatory disease are becoming apparent. Epidemiological studies of Japanese survivors of atomic bombs demonstrate a clear linear relationship between solid cancer incidence and organ dose. This is supported by other epidemiological data. This has become the gold standard for prediction of malignancy based on a linear no-threshold ‘LNT’ extrapolation, which links risk directly to radiation dose. However, the risk calculations involve many assumptions and approximations. They are designed to provide guidance on which a workable protection framework can be based. It is important that practitioners are aware of their limitations.
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Trainor, Laurel J., and Robert J. Zatorre. The neurobiological basis of musical expectations. Edited by Susan Hallam, Ian Cross, and Michael Thaut. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199298457.013.0016.

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This article explores how the auditory system processes incoming information and generates perceptual representations that allow it to make predictions about future sound events from past context, and how music appears to make use of this general processing mechanism. It focuses on expectation formation in auditory cortex because this is where the most research has been done, but there is also evidence for prediction mechanisms at subcortical levels and at levels beyond sensory areas. The article presents a framework for thinking about the neurological basis of expectation and prediction in musical processing using selected examples.
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Anjum, Rani Lill, and Stephen Mumford. Does Science Need Laws of Nature? Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198733669.003.0018.

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There is a view that as well as causation, science invokes general laws of nature. In one account, the universe is law-governed, but it is not clear whether this is to be understood literally or not. The generality of laws is useful in prediction and explanation. Laws provide also systematicity and simplicity. But it is questionable what the real being of laws is. Their being might reduce to singular powers or tendencies of individual things. While laws could be used in prediction and explanation, it is not clear that laws are indispensable for this purpose. Singular powers might be able to play the same role: or play it better.
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Book chapters on the topic "Dose prediction"

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Ma, Jianhui, Ti Bai, Dan Nguyen, Michael Folkerts, Xun Jia, Weiguo Lu, Linghong Zhou, and Steve Jiang. "Individualized 3D Dose Distribution Prediction Using Deep Learning." In Artificial Intelligence in Radiation Therapy, 110–18. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32486-5_14.

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Jove, Esteban, Jose M. Gonzalez-Cava, José-Luis Casteleiro-Roca, Héctor Quintián, Juan Albino Méndez-Pérez, José Luis Calvo-Rolle, Francisco Javier de Cos Juez, Ana León, María Martín, and José Reboso. "Remifentanil Dose Prediction for Patients During General Anesthesia." In Lecture Notes in Computer Science, 537–46. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92639-1_45.

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Kang, Jiayin, Yaozong Gao, Yao Wu, Guangkai Ma, Feng Shi, Weili Lin, and Dinggang Shen. "Prediction of Standard-Dose PET Image by Low-Dose PET and MRI Images." In Machine Learning in Medical Imaging, 280–88. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10581-9_35.

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Berndt, J., M. Misslbeck, and P. Kneschaurek. "Dose QA Using EPID and a Dose Prediction Algorithm Independent of the Planning System." In IFMBE Proceedings, 460–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03474-9_128.

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Sunag, Bhagya, and Shrinivas Desai. "Low-Dose Imaging: Prediction of Projections in Sinogram Space." In Computational Vision and Bio-Inspired Computing, 541–51. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6862-0_43.

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Geburtig, Anja, Volker Wachtendorf, Peter Trubiroha, Matthias Zäh, Artur Schönlein, Axel Müller, Teodora Vatahska, Gerhard Manier, and Thomas Reichert. "Polypropylene Numerical Photoageing Simulation by Dose–Response Functions with Respect to Irradiation and Temperature: ViPQuali Project." In Service Life Prediction of Exterior Plastics, 215–29. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06034-7_14.

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Zhang, Jingjing, Shuolin Liu, Teng Li, Ronghu Mao, Chi Du, and Jianfei Liu. "Voxel-Level Radiotherapy Dose Prediction Using Densely Connected Network with Dilated Convolutions." In Artificial Intelligence in Radiation Therapy, 70–77. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32486-5_9.

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Liu, Jianfei, Q. Jackie Wu, Fang-Fang Yin, John P. Kirkpatrick, Alvin Cabrera, and Yaorong Ge. "An Active Optical Flow Model for Dose Prediction in Spinal SBRT Plans." In Recent Advances in Computational Methods and Clinical Applications for Spine Imaging, 27–35. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14148-0_3.

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Tao, Yanyun, Dan Xiang, Yuzhen Zhang, and Bin Jiang. "Swarm ANN/SVR-Based Modeling Method for Warfarin Dose Prediction in Chinese." In Lecture Notes in Computer Science, 351–58. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61833-3_37.

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Wang, Jing, Ronghu Mao, Jiwei Liu, and Jianfei Liu. "Study on Dose Distribution Prediction of Esophageal Cancer Patients Using U-Net Model." In Lecture Notes in Electrical Engineering, 632–40. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9686-2_70.

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Conference papers on the topic "Dose prediction"

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McCormack, Percival D. "Radiation Dose Prediction for Space Station." In Intersociety Conference on Environmental Systems. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1986. http://dx.doi.org/10.4271/860924.

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Squires, Steven, Georgia Ionescu, Elaine F. Harkness, Alistair Mackenzie, Gareth Evans, Anthony Maxwell, Sacha Howell, and Susan M. Astley. "Automatic density prediction in low dose mammography." In Fifteenth International Workshop on Breast Imaging, edited by Chantal Van Ongeval, Nicholas Marshall, and Hilde Bosmans. SPIE, 2020. http://dx.doi.org/10.1117/12.2564714.

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Townsend, Lawrence W., J. Wesley Hines, Alexander Usynin, and Garrett M. Pitcher. "Solar particle event dose prediction using kernel regression." In 2009 IEEE Aerospace conference. IEEE, 2009. http://dx.doi.org/10.1109/aero.2009.4839330.

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Crow, M. J., A. B. Latif, A. I. Critchley, C. Stainton, P. Nealon, and S. M. Rajah. "COMPUTER PREDICTION OF ANTICOAGULATION STATUS AND WARFARIN DOSE FOLLOWING CARDIAC SURGERY." In XIth International Congress on Thrombosis and Haemostasis. Schattauer GmbH, 1987. http://dx.doi.org/10.1055/s-0038-1643275.

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Fluctuations are freguently seen in the anticoagulant status of patients in the immediate post operative period following prosthetic heart valve replacement. These patients are at high risk of haemorrhage or thromboembolism. We have used a pharmokinetic model of warfarin metabolism to develop a computer programme to predict the maintenance dose of warfarin from early prothrombin activity determinations. This will enable controlled anticoagulation to be achieved. The expression for warfarin kinetics employs 4 constants determined by the residual sum of the sguares, which are used immediately to redefine dosage predictions. In a pilot study data obtained from 16 patients post operation 3, 5 and 7 days after commencing treatment, has been used to predict the reguired maintenance dose at 21 days. These predicted doses were then compared with the maintenance dose achieved by clinical practice. The programme was told to optimise its dose to achieve a PT ratio of 3 whereas clinically the ratio was allowed to vary in the therapeutic range of 2 to 4. Predicted doses at 21 days are shown.in the table:Correlation between predicted and clinical maintenance doses after 3 and 5 days treatment was poor but had improved significantly by 7 days, despite similar levels of prothrombin activity. Predicted prothrombin activity never exceeded the upper limit of the therapeutic range, and the predicted dose can be uprated on addition of further data within 2 minutes.After 7 days computer predicted warfarin dose has produced a good correlation with the clinical maintenance dose (the doses of only 3 patients varying by more than 1 mg/day). The significant fluctuations seen in the prothrombin ratio during clinical dosage were not observed with computer dosing and we now feel it is safe to use this programme to anticoagulate patients post operatively.
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Michez, A., J. Boch, J. Dardie, F. Wrobel, A. D. Touboul, T. Maraine, F. Saigne, E. Lorfevre, and F. Bezerra. "TCAD prediction of dose effects on MOSFETs with ECORCE." In 2017 17th European Conference on Radiation and Its Effects on Components and Systems (RADECS). IEEE, 2017. http://dx.doi.org/10.1109/radecs.2017.8696230.

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Rahman, Raziur, and Ranadip Pal. "Analyzing drug sensitivity prediction based on dose response curve characteristics." In 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE, 2016. http://dx.doi.org/10.1109/bhi.2016.7455854.

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Ding, Alice K., Jon S. Heiselman, and Michael I. Miga. "Image data-driven thermal dose prediction for microwave ablation therapy." In Image-Guided Procedures, Robotic Interventions, and Modeling, edited by Baowei Fei and Cristian A. Linte. SPIE, 2020. http://dx.doi.org/10.1117/12.2550550.

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Tao, Yanyun, Yuzhen Zhang, and Bin Jiang. "Evolutionary learning-based modeling for warfarin dose prediction in Chinese." In GECCO '17: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3067695.3082492.

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Aldemir, T., A. Yilmaz, and B. Zha. "O?-Site Dose Prediction for Decision Making Using Recurrent Neural Networks." In Tranactions - 2019 Winter Meeting. AMNS, 2019. http://dx.doi.org/10.13182/t31320.

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Lei, Yang, Yabo Fu, Tonghe Wang, Walter J. Curran, Tian Liu, Pretesh Patel, and Xiaofeng Yang. "Prostate dose prediction in HDR Brachytherapy using unsupervised multi-atlas fusion." In Image Processing, edited by Bennett A. Landman and Ivana Išgum. SPIE, 2021. http://dx.doi.org/10.1117/12.2580979.

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Reports on the topic "Dose prediction"

1

Ahmed, Kareem. Multitude Characterization and Prediction of DOE Advanced Biofuels Properties. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1807468.

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Kohnert, Aaron Anthony, G. van Couvering, G. S. Was, and Brian D. Wirth. Models Predicting Void Swelling Incubation Dose as a function of Irradiation Conditions. Office of Scientific and Technical Information (OSTI), May 2019. http://dx.doi.org/10.2172/1524349.

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Haves, Philip, baptiste Ravache, and mehry Yazdanian. Accuracy of HVAC Load Predictions: Validation of EnergyPlus and DOE-2 using FLEXLAB Measurements. Office of Scientific and Technical Information (OSTI), April 2020. http://dx.doi.org/10.2172/1619175.

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Kunkel, Lynn. The Health Belief Model as a Predictor of Gynecological Exams: Does Sexual Orientation Matter? Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6819.

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Asay-Davis, Xylar Storm. Final Report: Modeling coupled ice sheet-ocean interactions in the Model for Prediction Across Scales (MPAS) and in DOE Earth System Models. Office of Scientific and Technical Information (OSTI), January 2019. http://dx.doi.org/10.2172/1490084.

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Carter, Troy. Final report for DOE DE-SC0016073: Towards a comprehensive, self-consistent, and predictive theory of the L-H transition. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1659671.

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Marzouk, Youssef. Final Report, DOE Early Career Award: Predictive modeling of complex physical systems: new tools for statistical inference, uncertainty quantification, and experimental design. Office of Scientific and Technical Information (OSTI), August 2016. http://dx.doi.org/10.2172/1312896.

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Payer, Joe H., and John R. Scully. Research Opportunities in Corrosion Science for Long-Term Prediction of Materials Performance: A Report of the DOE Workshop on “Corrosion Issues of Relevance to the Yucca Mountain Waste Repository”. Office of Scientific and Technical Information (OSTI), July 2003. http://dx.doi.org/10.2172/1278488.

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Farhi, Edward, and Hartmut Neven. Classification with Quantum Neural Networks on Near Term Processors. Web of Open Science, December 2020. http://dx.doi.org/10.37686/qrl.v1i2.80.

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We introduce a quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning. The quantum circuit consists of a sequence of parameter dependent unitary transformations which acts on an input quantum state. For binary classification a single Pauli operator is measured on a designated readout qubit. The measured output is the quantum neural network’s predictor of the binary label of the input state. We show through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets. We then discuss presenting the data as quantum superpositions of computational basis states corresponding to different label values. Here we show through simulation that learning is possible. We consider using our QNN to learn the label of a general quantum state. By example we show that this can be done. Our work is exploratory and relies on the classical simulation of small quantum systems. The QNN proposed here was designed with near-term quantum processors in mind. Therefore it will be possible to run this QNN on a near term gate model quantum computer where its power can be explored beyond what can be explored with simulation.
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Author, Not Given. CRADA No. BNL-C-97-10 between BNL and Cotton, Inc. Final abstract and final report [Final Report of Research carried out under DOE CRADA No. BNL-C-97-10 - "Prediction of Yield in Cotton"]. Office of Scientific and Technical Information (OSTI), January 2000. http://dx.doi.org/10.2172/770448.

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