Thematische Bibliographien / Dose prediction

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Auswahl der wissenschaftlichen Literatur zum Thema „Dose prediction“

Autor: Grafiati

Veröffentlicht am 24. Juni 2021

Zuletzt aktualisiert am 21. Juni 2025

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Zeitschriftenartikel zum Thema "Dose prediction"

Swartz, Conrad M. "Drug Dose Prediction With Flexible Test Doses." Journal of Clinical Pharmacology 31, no. 7 (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 (1995): 18. http://dx.doi.org/10.2165/00128413-199509950-00043.

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Liu, Y., Z. Chen, Q. Zhou, et al. "A Feasibility Study of Dose Band Prediction in Radiotherapy: Predicting a Dose Spectrum." International Journal of Radiation Oncology*Biology*Physics 117, no. 2 (2023): e691. http://dx.doi.org/10.1016/j.ijrobp.2023.06.2164.

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Gizynska, M., D. Blatkiewicz, B. Czyzew, et al. "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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Mitchel, R. E. J. "Radiation Risk Prediction and Genetics: The Influence of the TP53 Gene in vivo." Dose-Response 3, no. 4 (2005): dose—response.0. http://dx.doi.org/10.2203/dose-response.003.04.007.

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Risk prediction and dose limits for human radiation exposure are based on the assumption that risk is proportional to total dose. However, there is concern about the appropriateness of those limits for people who may be genetically cancer prone. The TP53 gene product functions in regulatory pathways for DNA repair, cell cycle checkpoints and apoptosis, processes critical in determining ionizing radiation risk for both carcinogenesis and teratogenesis. Mice that are deficient in TP53 function are cancer prone. This review examines the influence of variations in TP53 gene activity on cancer and teratogenic risk in mice exposed to radiation in vivo, and compares those observations to the assumptions and predictions of radiation risk inherent in the existing system of radiation protection. Current assumptions concerning a linear response with dose, dose additivity, lack of thresholds and dose rate reduction factors all appear incorrect at low doses. TP53 functional variations can further modify radiation risk from either high or low doses, or risk from radiation exposures combined with other stresses, and those modifications can result in both quantitative and qualitative changes in risk.

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Polizzi, Mitchell, Robert W. Watkins, and William T. Watkins. "Data-Driven Dose-Volume Histogram Prediction." Advances in Radiation Oncology 7, no. 2 (2022): 100841. http://dx.doi.org/10.1016/j.adro.2021.100841.

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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 (2011): 209–22. http://dx.doi.org/10.1111/j.1460-9592.2011.03782.x.

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MIZUTANI, YOSHIKATSU. "Trial of warfarin maintenance dose prediction." Rinsho yakuri/Japanese Journal of Clinical Pharmacology and Therapeutics 26, no. 1 (1995): 177–78. http://dx.doi.org/10.3999/jscpt.26.177.

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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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Lippi, Giuseppe, Gian Luca Salvagno, and Gian Cesare Guidi. "Genetic Factors for Warfarin Dose Prediction." Clinical Chemistry 53, no. 9 (2007): 1721–22. http://dx.doi.org/10.1373/clinchem.2007.092338.

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Dissertationen zum Thema "Dose prediction"

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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Includes abstract. Includes bibliographical references (leaves 112-117). 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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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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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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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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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. Master of Science

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Podda, G. "PREDICTION OF OPTIMAL WARFARIN MAINTENANCE DOSE USING ADVANCED ARTIFICIAL NEURAL NETWORKS." Doctoral thesis, Università degli Studi di Milano, 2013. http://hdl.handle.net/2434/219087.

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Introduction. The individual response to vitamin K antagonists (VKA) is highly variable, being influenced by clinical factors and genetic variants of enzymes that are involved in the metabolism of VKA (CYP2C)) and vitamin K (VKORC1). Currently, the dose of VKA is adjusted based on measurements of the prothrombin time. In the last years, mathematical algorithms were developed for estimating the appropriate VKA dose, based on different mathematical approaches working on clinical and genetic data. Artificial Neural Networks (ANN) are computerized algorithms resembling interactive processes of the human brain, which allow to study very complex non-linear phenomena like biological systems. Aim. To evaluate the performance of new generation ANN on a large data base of patients on chronic VKA treatment. Methods. Clinical and genetic data from 377 patients (186 m; 191 f) treated with a VKA (warfarin) average weekly maintenance dose (WMD) of 23.7 mg (11.5 SD) were used to create a dose algorithm. Forty-eight variables, including demographic, clinical and genetic data (5 CYP2C9 and 3 VKORC1 genetic variants) were entered into Twist® system, which can select fundamental variables during their evolution in search for the best predictive model. The final model, based on 23 variables expressed a functional approximation of the actual dose within a validation protocol based on a tripartite division of the data set (training, testing, validation). Results. In the validation cohort, the pharmacogenetic algorithm reached high accuracy, with an average absolute error of 5.7 mg WMD. In the subset of patients requiring ≤21 mg (45 % of the cohort) and 21-49 mg (51 % of the cohort) the absolute error was 3.86 mg and 5.45 with a high percentage of subjects being correctly identified (72%, 74% respectively). Conclusion. ANN can be applied successfully for VKA maintenance dose prediction and represent a robust basis for a prospective multicentre clinical trial of the efficacy of genetically informed dose estimation for patients who require VKA.

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Bücher zum Thema "Dose prediction"

Reid, J. A. Keith. The effects of age-dependent dose conversion factors from ICRP-72 on biosphere model dose predictions. 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 ... 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 University Management School, 1997.

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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. U.S. Geological Survey, 1990.

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

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

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Chistyakova, Guzel, Lyudmila Ustyantseva, Irina Remizova, Vladislav Ryumin, and Svetlana Bychkova. CHILDREN WITH EXTREMELY LOW BODY WEIGHT: CLINICAL CHARACTERISTICS, FUNCTIONAL STATE OF THE IMMUNE SYSTEM, PATHOGENETIC MECHANISMS OF THE FORMATION OF NEONATAL PATHOLOGY. AUS PUBLISHERS, 2022. http://dx.doi.org/10.26526/monography_62061e70cc4ed1.46611016.

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The purpose of the monograph, which contains a modern view of the problem of adaptation of children with extremely low body weight, is to provide a wide range of doctors with basic information about the clinical picture, functional activity of innate and adaptive immunity, prognostic criteria of postnatal pathology, based on their own research. The specific features of the immunological reactivity of premature infants of various gestational ages who have developed bronchopulmonary dysplasia (BPD) and retinopathy of newborns (RN) from the moment of birth and after reaching postconceptional age (37-40 weeks) are described separately. The mechanisms of their implementation with the participation of factors of innate and adaptive immunity are considered in detail. Methods for early prediction of BPD and RN with the determination of an integral indicator and an algorithm for the management of premature infants with a high risk of postnatal complications at the stage of early rehabilitation are proposed. The information provided makes it possible to personify the treatment, preventive and rehabilitation measures in premature babies. The monograph is intended for obstetricians-gynecologists, neonatologists, pediatricians, allergists-immunologists, doctors of other specialties, residents, students of the system of continuing medical education. This work was done with financial support from the Ministry of Education and Science, grant of the President of the Russian Federation No. MK-1140.2020.7.

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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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Buchteile zum Thema "Dose prediction"

Nguyen, Dan. "Imaged-Based Dose Planning Prediction." In Medical Image Synthesis. CRC Press, 2023. http://dx.doi.org/10.1201/9781003243458-8.

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Chauhan, Akash, Ayush Dubey, Md Aftab Alam, Rishabha Malviya, and Mohammad Javed Naim. "Dose Prediction in Oncology using Big Data." In Big Data in Oncology: Impact, Challenges, and Risk Assessment. River Publishers, 2023. http://dx.doi.org/10.1201/9781003442639-9.

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Jove, Esteban, Jose M. Gonzalez-Cava, José-Luis Casteleiro-Roca, et al. "Remifentanil Dose Prediction for Patients During General Anesthesia." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92639-1_45.

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Feng, Zhenghao, Lu Wen, Peng Wang, et al. "DiffDP: Radiotherapy Dose Prediction via a Diffusion Model." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43987-2_19.

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Ma, Jianhui, Ti Bai, Dan Nguyen, et al. "Individualized 3D Dose Distribution Prediction Using Deep Learning." In Artificial Intelligence in Radiation Therapy. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32486-5_14.

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Kang, Jiayin, Yaozong Gao, Yao Wu, et al. "Prediction of Standard-Dose PET Image by Low-Dose PET and MRI Images." In Machine Learning in Medical Imaging. 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. 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. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6862-0_43.

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Wang, Bin, Lin Teng, Lanzhuju Mei, et al. "Deep Learning-Based Head and Neck Radiotherapy Planning Dose Prediction via Beam-Wise Dose Decomposition." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16449-1_55.

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Geburtig, Anja, Volker Wachtendorf, Peter Trubiroha, et al. "Polypropylene Numerical Photoageing Simulation by Dose–Response Functions with Respect to Irradiation and Temperature: ViPQuali Project." In Service Life Prediction of Exterior Plastics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06034-7_14.

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Konferenzberichte zum Thema "Dose prediction"

Feng, Zhenghao, Lu Wen, Yuanyuan Xu, Binyu Yan, Jiliu Zhou, and Yan Wang. "Content-Aware Adversarial Network with Gradient-Enhanced Dose Rectification for Radiotherapy Dose Prediction." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635667.

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Fu, Linjie, Xia Li, Xiuding Cai, Xueyao Wang, Yali Shen, and Yu Yao. "MD-Dose: A diffusion model based on the Mamba for radiation dose prediction." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822581.

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Wen, Lu, Qihun Zhang, Zhenghao Feng, et al. "Triplet-Constraint Transformer with Multi-Scale Refinement for Dose Prediction in Radiotherapy." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635240.

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Cui, Jiaqi, Yuanyuan Xu, Jianghong Xiao, et al. "Dose Prediction Driven Radiotherapy Parameters Regression via Intra- and Inter-Relation Modeling." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635537.

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Chen, Jingyun, Martin King, and Yading Yuan. "FedKBP: federated dose prediction framework for knowledge-based planning in radiation therapy." In Image-Guided Procedures, Robotic Interventions, and Modeling, edited by Maryam E. Rettmann and Jeffrey H. Siewerdsen. SPIE, 2025. https://doi.org/10.1117/12.3044379.

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Ahmed, Abrar, Minhazul Islam Mahi, M. Akhtaruzzaman, et al. "Towards Designing a Medication Assistive Intelligent System with Automated Insulin-dose Prediction for Elderlies." In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2025. https://doi.org/10.1109/ecce64574.2025.11012957.

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Wen, Lu, Wenxia Yin, Zhenghao Feng, Xi Wu, Deng Xiong, and Yan Wang. "ARANet: Attention-based Residual Adversarial Network with Deep Supervision for Radiotherapy Dose Prediction of Cervical Cancer." In 2024 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE International Conference on Robotics, Automation and Mechatronics (RAM). IEEE, 2024. http://dx.doi.org/10.1109/cis-ram61939.2024.10672659.

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Zhang, J., A. Bousse, Y. Li, et al. "Pre-therapy dose prediction in targeted radionuclide therapy using semi-supervised learning: An in-silico preliminary study." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10655434.

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Henique, Gautier, Chulmin Bang, Daniel Markel, et al. "Dose Aware Toxicity Prediction in Head and Neck Cancer Patients Using a Deformable 3D CNN on Daily CBCT Acquisitions." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635238.

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Orji, Martina P., Kyle Williams, Jonathan Troville, Swetadri Vasan Setlur Nagesh, Stephen Rudin, and Daniel R. Bednarek. "Investigation of the effect of training set parameters on deep neural network prediction accuracy of fluoroscopic procedure-room scatter dose distributions." In Physics of Medical Imaging, edited by John M. Sabol, Shiva Abbaszadeh, and Ke Li. SPIE, 2025. https://doi.org/10.1117/12.3046512.

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Berichte der Organisationen zum Thema "Dose prediction"

Committee on Toxicology. New Approach Methodologies (NAMs) In Regulatory Risk Assessment Workshop Report 2020- Exploring Dose Response. Food Standards Agency, 2024. http://dx.doi.org/10.46756/sci.fsa.cha679.

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The UK Food Standards Agency (FSA) and the Committee on Toxicity of Chemicals in Food, Consumer Products and the Environment (COT) held an “Exploring Dose Response” workshop in a multidisciplinary setting inviting regulatory agencies, government bodies, academia and industry. The workshop provided a platform from which to address and enable expert discussions on the latest in silico prediction models, new approach methodologies (NAMs), physiologically based pharmacokinetics (PBPK), future methodologies, integrated approaches to testing and assessment (IATA) as well as methodology validation. Using a series of presentations from external experts and case study (plastic particles, polymers, tropane alkaloids, selective androgen receptor modulators) discussions, the workshop outlined and explored an approach that is fit for purpose applied to future human health risk assessment in the context of food safety. Furthermore, possible future research opportunities were explored to establish points of departure (PODs) using non-animal alternative models and to improve the use of exposure metrics in risk assessment.

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

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Wilkowski. PR-276-04503-R02 Multi-Scale Mechanics and Welding Process Simulation in Weld Integrity Assessment. Pipeline Research Council International, Inc. (PRCI), 2014. http://dx.doi.org/10.55274/r0010845.

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This report is on a jointly funded project by DOE and PRCI. The DOE version of this report was separately published in 2008. This project dealt with various aspects of prediction of the stress-based and strain-based girthweld flaw tolerance of linepipe steels, as well as some supplementary investigations.

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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), 2019. http://dx.doi.org/10.2172/1524349.

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Konsam, Manis Kumar, Amanda Thounajam, Prasad Vaidya, Gopikrishna A, Uthej Dalavai, and Yashima Jain. Machine Learning-Enhanced Control System for Optimized Ceiling Fan and Air Conditioner Operation for Thermal Comfort. Indian Institute for Human Settlements, 2024. http://dx.doi.org/10.24943/mlcsocfacotc6.2023.

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This paper proposes and tests the implementation of a sustainable cooling approach that uses a machine learning model to predict operative temperatures, and an automated control sequence that prioritises ceiling fans over air conditioners. The robustness of the machine learning model (MLM) is tested by comparing its prediction with that of a straight-line model (SLM) using the metrics of Mean Bias Error (MBE) and Root Mean Squared Error (RMSE). This comparison is done across several rooms to see how each prediction method performs when the conditions are different from those of the original room where the model was trained. A control sequence has been developed where the MLM’s prediction of Operative Temperature (OT) is used to adjust the adaptive thermal comfort band for increased air speed delivered by the ceiling fans to maintain acceptable OT. This control sequence is tested over a two-week period in two different buildings by comparing it with a constant air temperature setpoint (24ºC).

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Iselin, Columbus O'Donnell. Preliminary report on the prediction of "Afternoon Effect". Woods Hole Oceanographic Institution, 2022. http://dx.doi.org/10.1575/1912/29562.

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With moderate or light winds and a clear sky the diurnal heating which occurs near the sea surface can cause a serious reduction in the range of submarine detection, especially on shallow targets. This has usually been called the “afternoon effect", although as will be noticed below the ranges often remain short long after sun down. The heating of surface waters which causes such sharp downward refraction can of course be noted on a bathythermograph record, provided pen vibration does not confuse the upper part of the trace. Unfortunately it is the upper 20 or 30 feet of a bathythermograph curve which in the case of ships moving faster than 12 knots is often somewhat difficult to read with sufficient certainty. Moreover, in planning a days operations it is clearly desirable to know in advance how much reduction in range may be expected from diurnal warming.

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Eilerts. L52026 Improved Prediction of Burnthrough for In-Service Welding. Pipeline Research Council International, Inc. (PRCI), 2002. http://dx.doi.org/10.55274/r0011153.

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Experiments were conducted to evaluate the effect of pressure on burnthrough risk.� The results indicate that hoop stress has a significant effect for thin-wall pipe.� The experimental data was used to develop and evaluate an alternative burnthrough prediction approach that accounts for pressure in the pipe.� The approach that was developed assumes that the volume of heated metal under the arc behaves similar to an area of metal loss caused by corrosion pitting.� An equivalent pit size is determined from the pipe diameter and wall thickness and the calculated weld penetration.� The predicted burst pressure (i.e., the pressure limit) is then determined using RSTRENG.� While this approach was shown to be relatively accurate for the experimental welds that were made, it does not consider the removal of heat by the contents and it was evaluated for a limited range of conditions.

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Verma, Monika, Thomas Hertel, and Paul Preckel. Predicting Within Country Household Food Expenditure Variation Using International Cross-Section Estimates. GTAP Working Paper, 2009. http://dx.doi.org/10.21642/gtap.wp57.

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There is a long and distinguished literature involving demand analysis using international cross-section data. Such models are widely used for predicting national per capita consumption. However, there is nothing in this literature testing the performance of estimated models in predicting demands across the income spectrum within a single country. This paper fills the gap. We estimate an AIDADS model using cross-section international per capita data, and find that it does well in predicting food demand across the income distribution within Bangladesh. This suggests that there may be considerable value in using international cross-section analysis to study poverty and distributional impacts of policies.

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Shiva, B. G. GMC-93-T03 Regenerative Heat Transfer in Reciprocating Compressors. Pipeline Research Council International, Inc. (PRCI), 1993. http://dx.doi.org/10.55274/r0011944.

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Evaluates the impact of heat transfer on reciprocating compressor performance, especially with respect to flow capacity. This paper gives results of the experimental measurements done to determine the contribution of regenerative heat transfer to suction gas heating and its comparison with earlier empirical models. It forms part of ongoing research on estimating the effects of heat transfer on compressor performance with a view to modeling such effects for improved prediction of compressor performance.

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Paolinelli, Luciano, and Srdjan Nesic. PR646-173609-Z01 Water Wetting Prediction Tool for Pipeline Integrity. Pipeline Research Council International, Inc. (PRCI), 2021. http://dx.doi.org/10.55274/r0012111.

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Current approaches for pipeline integrity management, as related to internal corrosion, largely depend on Internal Corrosion Direct Assessment (ICDA) type approach. An essential part of these assessments is to predict if corrosive water phase is in direct contact with the internal pipe wall, a phenomenon commonly called "water wetting". In general, water wetting prediction models currently used by pipe integrity engineers are lagging behind the current level understanding. The Institute for Corrosion and Multiphase Technology (ICMT) at Ohio University has developed and validated a mechanistic model that combines multiphase flow and wetting physics of oil and water on steel surfaces. This work that is the outcome of many years of focused industrially sponsored research is published in the open literature. However, its implementation into an integrity management tool has not been done yet. This PRCI project is devoted to developing a tool to predict water wetting for product pipelines as well as for crude oil pipelines and flow lines, based on Ohio University's work.

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