Academic literature on the topic 'Clinical risk prediction'
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Journal articles on the topic "Clinical risk prediction"
Dolan, M., and M. Doyle. "Violence risk prediction." British Journal of Psychiatry 177, no. 4 (October 2000): 303–11. http://dx.doi.org/10.1192/bjp.177.4.303.
Full textHalabi, Susan, Cai Li, and Sheng Luo. "Developing and Validating Risk Assessment Models of Clinical Outcomes in Modern Oncology." JCO Precision Oncology, no. 3 (December 2019): 1–12. http://dx.doi.org/10.1200/po.19.00068.
Full textLawrie, Stephen M. "Clinical risk prediction in schizophrenia." Lancet Psychiatry 1, no. 6 (November 2014): 406–8. http://dx.doi.org/10.1016/s2215-0366(14)70310-4.
Full textFonarow, Gregg C., Deborah B. Diercks, and W. Franklin Peacock. "Assessing Clinical Risk Prediction Tools." Annals of Emergency Medicine 50, no. 6 (December 2007): 741–42. http://dx.doi.org/10.1016/j.annemergmed.2007.05.028.
Full textNguyen, A. Tuan, Hyewon Jeong, Eunho Yang, and Sung Ju Hwang. "Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 9081–91. http://dx.doi.org/10.1609/aaai.v35i10.17097.
Full textLambert, Samuel A., Gad Abraham, and Michael Inouye. "Towards clinical utility of polygenic risk scores." Human Molecular Genetics 28, R2 (July 31, 2019): R133—R142. http://dx.doi.org/10.1093/hmg/ddz187.
Full textKy, Bonnie, Carla L. Warneke, Daniel John Lenihan, Puneet S. Cheema, Dennis Frederic Moore, Mark G. Campbell, Chilakamarri Yeshwant, et al. "Clinical risk prediction in anthracycline cardiotoxicity." Journal of Clinical Oncology 32, no. 15_suppl (May 20, 2014): 9624. http://dx.doi.org/10.1200/jco.2014.32.15_suppl.9624.
Full textvan Geel, Tineke, Geert-Jan Dinant, Piet Geusens, and Joop van den Bergh. "Fracture risk prediction in clinical practice." Maturitas 81, no. 1 (May 2015): 112. http://dx.doi.org/10.1016/j.maturitas.2015.02.035.
Full textMagee, L. A., and P. v. Dadelszen. "Clinical risk prediction of pre-eclampsia." BMJ 342, apr07 4 (April 7, 2011): d1863. http://dx.doi.org/10.1136/bmj.d1863.
Full textLi, Juan, Mingyao Lai, and Linbo Cai. "MEDB-58. Risk factors and risk prediction models for medulloblastoma recurrence." Neuro-Oncology 24, Supplement_1 (June 1, 2022): i119—i120. http://dx.doi.org/10.1093/neuonc/noac079.432.
Full textDissertations / Theses on the topic "Clinical risk prediction"
Townsend, Daphne. "Clinical trial of estimated risk stratification prediction tool." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27926.
Full textJackson, Rebecca L. "Contextualized Risk Assessment in Clinical Practice: Utility of Actuarial, Clinical, and Structured Clinical Approaches to Predictions of Violence." Thesis, University of North Texas, 2004. https://digital.library.unt.edu/ark:/67531/metadc4603/.
Full textGrant, Stuart William. "Risk prediction models in cardiovascular surgery." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/risk-prediction-models-in-cardiovascular-surgery(1befbc5d-2aa6-4d24-8c32-e635cf55e339).html.
Full textDonovan, Brittney Marie. "Early risk prediction tools for gestational diabetes mellitus." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6408.
Full textGhassemi, Marzyeh. "Representation learning in multi-dimensional clinical timeseries for risk and event prediction." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112389.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 99-108).
There are major practical and technical barriers to understanding human health, and therefore a need for methods that thrive on large, complex, noisy data. In this work, we present machine learning methods that distill large amounts of heterogeneous health data into latent state representations. These representations are then used to estimate risks of poor outcomes, and response to intervention in multivariate physiological signals. We evaluate the reduced latent representations by 1) establishing their predictive value in important clinical tasks and 2) showing that the latent space representations themselves provide useful insight into underlying systems. In particular, we focus on case studies that can provide evidence-based risk assessment and forecasting in settings with guidelines that have not traditionally been data-driven. In this thesis we evaluate several methods to create patient representations, and use these features to predict important outcomes. Representation learning can be thought of as a form of phenotype discovery, where we attempt to discover spaces in the new representation that are markers of important events. We argue that these latent representations are useful markers when they 1) create better prediction results on outcomes of interest, and 2) do not duplicate features that are currently known bio-markers. We present four case studies of learning representations, and evaluate the representations on real predictive tasks. First, we create forward-facing prediction models using baseline clinical features, and those from a Latent Dirichlet Allocation (LDA) model trained with clinical progress notes. We then evaluate the per-patient latent state membership to predict mortality in an intensive care setting as time moves forward. Second, we use non-parametric Multi-task Gaussian Process (MTGP) hyper-parameters as latent features to estimate correlations within and between signals in sparse, heterogeneous time series data. We evaluate the hyper-parameters for forecasting missing signals in traumatic brain injury patients, and predicting mortality in intensive care unit patients. Third, we train switching-state autoregressive models (SSAMs) to model the underlying states that emit patient vital signs over time. We evaluate the time-specific latent state distributions as features to predict vasopressor onset and weaning in intensive care unit patients. Finally, we use statistical and symbolic features extracted from wearable ambulatory accelerometers (ACC) mounted to the neck to classify patient pathology, and stratify patients' risk of voice misuse. We evaluate the utility of both statistically generated features and symbolic representations of glottal pulses towards patient classification.
by Marzyeh Ghassemi.
Ph. D.
Olsson, Thomas. "Risk Prediction at the Emergency Department." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4632.
Full textPayne, Beth. "Development, validation and pilot implementation of the miniPIERS (Pre-eclampsia Integrated Estimate of RiSk) clinical risk prediction model." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/51460.
Full textMedicine, Faculty of
Obstetrics and Gynaecology, Department of
Graduate
Yan, Jia. "Using Genetic Information in Risk Prediction for Alcohol Dependence." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2878.
Full textMartínez, Millana Antonio. "ASSESSMENT OF RISK SCORES FOR THE PREDICTION AND DETECTION OF TYPE 2 DIABETES MELLITUS IN CLINICAL SETTINGS." Doctoral thesis, Universitat Politècnica de València, 2017. http://hdl.handle.net/10251/86209.
Full textLos indicadores de salud y sociológicos confirman que la esperanza de vida está aumentando, y por lo tanto, los años que los pacientes tienen que vivir con enfermedades crónicas y comorbilidades. Diabetes tipo 2 es una de las enfermedades crónicas más comunes, especialmente relacionadas con el sobrepeso y edades superiores a los sesenta años. Como enfermedad metabólica, la diabetes tipo 2 afecta a múltiples órganos causando daño en los vasos sanguíneos y el sistema nervioso a escala micro y macro. La mortalidad de sujetos con diabetes es tres veces mayor que la mortalidad de sujetos con otras enfermedades crónicas. Por un lado, la estrategia de manejo se centra en el mantenimiento de los niveles de glucosa en sangre bajo un umbral mediante la prescripción de fármacos antidiabéticos y una combinación de hábitos alimentarios saludables y actividad física moderada. Estudios recientes han demostrado la eficacia de nuevas estrategias para retrasar e incluso prevenir la aparición de la diabetes tipo 2 mediante una combinación de estilo de vida activo y saludable en cohortes de sujetos de riesgo medio a alto. Por otro lado, la investigación prospectiva se ha dirigido a grupos de la población para construir modelos de riesgo que pretenden obtener una regla para la clasificación de las personas según las probabilidades de desarrollar la enfermedad. Actualmente hay más de doscientos modelos de riesgo para hacer esta identificación, no obstante la inmensa mayoría no han sido debidamente evaluados en grupos externos y, hasta la fecha, ninguno de ellos ha sido probado en un estudio poblacional. El estudio de investigación presentado en esta tesis doctoral pretende utilizar modelos riesgo validados externamente para la predicción y detección de la Diabetes Tipo 2 en una base de datos poblacional del Hospital La Fe de Valencia (España). La hipótesis del estudio es que la integración de los modelos de riesgo de predicción y detección existentes la práctica clínica aumenta la detección temprana de casos de alto riesgo. Para evaluar esta hipótesis, se han realizado tres estudios sobre las dimensiones clínicas, del usuario y de la tecnología para evaluar hasta qué punto los modelos y el hospital están dispuestos a explotar dichos modelos para identificar grupos de alto riesgo y conducir estrategias preventivas eficaces. Los hallazgos presentados en esta tesis sugieren que los registros de salud electrónicos no están preparados para alimentar masivamente modelos de riesgo. Algunos de los modelos evaluados han demostrado un buen desempeño de clasificación, lo que acompañó a la buena aceptación de herramientas basadas en la web y el desempeño técnico aceptable del sistema de tecnología de información y comunicación, sugiere que después de algún trabajo estos modelos pueden conducir un nuevo paradigma de la detección activa de la Diabetes Tipo 2.
Els indicadors sociològics i de salut confirmen un augment en l'esperança de vida, i per tant, dels anys que les persones han de viure amb malalties cròniques i comorbiditats. la diabetis de tipus 2 és una de les malalties cròniques més comunes, especialment relacionades amb l'excés de pes i edats superiors als seixanta anys. Com a malaltia metabòlica, la diabetis de tipus 2 afecta múltiples òrgans causant dany als vasos sanguinis i el sistema nerviós a escala micro i macro. La mortalitat de subjectes amb diabetis és tres vegades superior a la mortalitat de subjectes amb altres malalties cròniques. D'una banda, l'estratègia de maneig se centra en el manteniment dels nivells de glucosa en sang sota un llindar mitjançant la prescripció de fàrmacs antidiabètics i una combinació d'hàbits alimentaris saludables i activitat física moderada. Estudis recents han demostrat l'eficàcia de noves estratègies per a retardar i fins i tot prevenir l'aparició de la diabetis de tipus 2 mitjançant una combinació d'estil de vida actiu i saludable en cohorts de subjectes de risc mitjà a alt. D'altra banda, la investigació prospectiva s'ha dirigit a grups específics de la població per construir models de risc que pretenen obtenir una regla per a la classificació de les persones segons les probabilitats de desenvolupar la malaltia. Actualment hi ha més de dos-cents models de risc per fer aquesta identificació, però la immensa majoria no han estat degudament avaluats en grups externs i, fins ara, cap d'ells ha estat provat en un estudi poblacional. L'estudi d'investigació presentat en aquesta tesi doctoral utilitza models de risc validats externament per a la predicció i detecció de diabetis de tipus 2 en una base de dades poblacional de l'Hospital La Fe de València (Espanya). La hipòtesi de l'estudi és que la integració dels models de risc de predicció i detecció existents la pràctica clínica augmenta la detecció de casos d'alt risc. Per avaluar aquesta hipòtesi, s'han realitzat tres estudis sobre les dimensions clíniques, de l'usuari i de la tecnologia per avaluar fins a quin punt els models i l'hospital estan disposats a explotar aquests models per identificar grups d'alt risc i conduir estratègies preventives. Les troballes presentades sugereixen que els registres de salut electrònics no estan preparats per alimentar massivament models de risc. Alguns dels models avaluats han demostrat una bona classificació, el que va acompanyar a la bona acceptació d'eines basades en el web i el rendiment tècnic acceptable del sistema de tecnologia d'informació i comunicacions implementat. La conclusió es que encara es necesari treball per que aquests models poden conduir un nou paradigma de la detecció activa de la diabetis de tipus 2.
Martínez Millana, A. (2017). ASSESSMENT OF RISK SCORES FOR THE PREDICTION AND DETECTION OF TYPE 2 DIABETES MELLITUS IN CLINICAL SETTINGS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/86209
TESIS
Bello, Ghalib. "Application and Extension of Weighted Quantile Sum Regression for the Development of a Clinical Risk Prediction Tool." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/608.
Full textBooks on the topic "Clinical risk prediction"
International Workshop on Epileptic Seizure Prediction (3rd 2007 Freiburg im Breisgau, Germany). Seizure prediction in epilepsy: From basic mechanisms to clinical applications. Weinheim: Wiley-VCH, 2008.
Find full textChistyakova, 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. au: AUS PUBLISHERS, 2022. http://dx.doi.org/10.26526/monography_62061e70cc4ed1.46611016.
Full textHochman, Michael E. Identifying Children with Low-Risk Head Injuries Who Do Not Require Computed Tomography. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190223700.003.0002.
Full textLangton, Calvin M. Contrasting approaches to risk assessment with adult male sexual offenders: An evaluation of recidivism prediction schemes and the utility of supplementary clinical information for enhancing predictive accuracy. 2003.
Find full textLee, Christoph I. Repeat Bone Mineral Density Screening and Osteoporotic Fracture Prediction. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190223700.003.0035.
Full textSeshadri, Sudha, and Stéphanie Debette, eds. Risk Factors for Cerebrovascular Disease and Stroke. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199895847.001.0001.
Full textEder, Lihi. The clinical course and outcome of psoriatic arthritis. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198737582.003.0021.
Full textInherited Susceptibility To Cancer Clinical Predictive And Ethical Perspectives. Cambridge University Press, 2009.
Find full text1960-, Foulkes William D., and Hodgson S. V, eds. Inherited susceptibility to cancer: Clinical, predictive, and ethical perspectives. Cambridge: Cambridge University Press, 1998.
Find full textWunsch, Hannah, and Andrew A. Kramer. The role and limitations of scoring systems. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0028.
Full textBook chapters on the topic "Clinical risk prediction"
Subramanian, Vigneshwar, and Michael W. Kattan. "Clinical Risk Assessment and Prediction." In Health Informatics, 17–29. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18626-5_2.
Full textde Graaf, Jacqueline, Patrick Couture, and Allan D. Sniderman. "ApoB in Cardiovascular Risk Prediction." In ApoB in Clinical Care, 135–46. Houten: Bohn Stafleu van Loghum, 2015. http://dx.doi.org/10.1007/978-90-368-0980-1_5.
Full textPresannan, Bhagya, N. Ramasubramanian, and A. Santhana Vijayan. "Disease Risk Prediction from Clinical Texts." In Advances in Intelligent Systems and Computing, 319–25. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9515-5_30.
Full textCording, Jacinta R., Tony Ward, and Sarah M. Beggs Christofferson. "Risk Prediction and Sex Offending." In Sexually Violent Predators: A Clinical Science Handbook, 225–41. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04696-5_14.
Full textDrubay, Damien, Ben Van Calster, and Stefan Michiels. "Development and Validation of Risk Prediction Models." In Principles and Practice of Clinical Trials, 1–22. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-52677-5_138-1.
Full textDrubay, Damien, Ben Van Calster, and Stefan Michiels. "Development and Validation of Risk Prediction Models." In Principles and Practice of Clinical Trials, 2003–24. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-52636-2_138.
Full textTeo, Kareen, Ching Wai Yong, Joon Huang Chuah, Khairunnisa Hasikin, Maheza Irna Mohd Salim, Yan Chai Hum, and Khin Wee Lai. "Assessing Clinical Usefulness of Readmission Risk Prediction Model." In 6th Kuala Lumpur International Conference on Biomedical Engineering 2021, 389–96. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90724-2_42.
Full textFarrell, Rachel, and Peter J. Kelly. "Serum Biomarkers in Prediction of Stroke Risk and Outcome." In Handbook of Stroke Prevention in Clinical Practice, 257–78. Totowa, NJ: Humana Press, 2004. http://dx.doi.org/10.1007/978-1-59259-769-7_16.
Full textApfel, C. C., and N. Roewer. "Prediction of Postoperative Nausea and Vomiting Using Clinical Risk Factors." In Problems of the Gastrointestinal Tract in Anesthesia, the Perioperative Period, and Intensive Care, 289–301. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60200-9_32.
Full textHengeveld, M. W., J. van der Wal, and A. J. F. M. Kerkhof. "Clinical Prediction of Suicidal Behavior Among High-Risk Suicide Attempters." In Current Issues of Suicidology, 189–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73358-1_28.
Full textConference papers on the topic "Clinical risk prediction"
Wang, Fei, Ping Zhang, Buyue Qian, Xiang Wang, and Ian Davidson. "Clinical risk prediction with multilinear sparse logistic regression." In KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2623330.2623755.
Full textDemarco, Maria, Noorie Hyun, Hormuzd Katki, Brian Befano, Li Cheung, Tina R. Raine-Bennett, Barbara Fetterman, et al. "Abstract A28: Risk model for clinical management of HPV-infected women." In Abstracts: AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; November 16-19, 2016; Orlando, FL. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-7755.carisk16-a28.
Full textGarg, Priya, and Deepti Aggarwal. "Application of Swarm-Based Feature Selection and Extreme Learning Machines in Lung Cancer Risk Prediction." In Intelligent Computing and Technologies Conference. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.115.1.
Full textTiwaskar, S. A., Rutuja Gosavi, Riddhima Dubey, Shaila Jadhav, and Komal Iyer. "Comparison of Prediction Models for Heart Failure Risk: A Clinical Perspective." In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). IEEE, 2018. http://dx.doi.org/10.1109/iccubea.2018.8697509.
Full textCheng, Chih-Wen, and May D. Wang. "Improving personalized clinical risk prediction based on causality-based association rules." In BCB '15: ACM International Conference on Bioinformatics, Computational Biology and Biomedicine. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2808719.2808759.
Full textSinghal, Pankhuri, Yogasudha Veturi, Renae Judy, Yoson Park, Marijana Vujkovic, Olivia Veatch, Rachel Kember, and Shefali Setia Verma. "Session Introduction: SALUD: Scalable Applications of cLinical risk Utility and preDiction." In Pacific Symposium on Biocomputing 2023. WORLD SCIENTIFIC, 2022. http://dx.doi.org/10.1142/9789811270611_0037.
Full textAlhassan, Zakhriya, David Budgen, Riyad Alshammari, Tahani Daghstani, A. Stephen McGough, and Noura Al Moubayed. "Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data." In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2018. http://dx.doi.org/10.1109/icmla.2018.00087.
Full textLowe, G. D. O. "EPIDEMIOLOGY AND RISK PREDICTION OF VENOUS THROMBOEMBOLISM." In XIth International Congress on Thrombosis and Haemostasis. Schattauer GmbH, 1987. http://dx.doi.org/10.1055/s-0038-1642965.
Full textDing, Xiyu, Mei-Hua Hall, and Timothy Miller. "Incorporating Risk Factor Embeddings in Pre-trained Transformers Improves Sentiment Prediction in Psychiatric Discharge Summaries." In Proceedings of the 3rd Clinical Natural Language Processing Workshop. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.clinicalnlp-1.4.
Full textZang, Chengxi, and Fei Wang. "SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records." In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 2021. http://dx.doi.org/10.1109/icdm51629.2021.00097.
Full textReports on the topic "Clinical risk prediction"
Kent, David M., Jason Nelson, Jenica N. Upshaw, Gaurav Gulati, Riley Brazil, Esmee Venema, Christine M. Lundquist, et al. Using Different Data Sets to Test How Well Clinical Prediction Models Work to Predict Patients' Risk of Heart Disease. Patient-Centered Outcomes Research Institute (PCORI), September 2021. http://dx.doi.org/10.25302/09.2021.me.160635555.
Full textApiyo, Eric, Zita Ekeocha, Stephen Robert Byrn, and Kari L. Clase. Improving Pharmacovigilliance Quality Management System in the Pharmacy and Poisions Board of Kenya. Purdue University, December 2021. http://dx.doi.org/10.5703/1288284317444.
Full textDy, Sydney M., Julie M. Waldfogel, Danetta H. Sloan, Valerie Cotter, Susan Hannum, JaAlah-Ai Heughan, Linda Chyr, et al. Integrating Palliative Care in Ambulatory Care of Noncancer Serious Chronic Illness: A Systematic Review. Agency for Healthcare Research and Quality (AHRQ), February 2020. http://dx.doi.org/10.23970/ahrqepccer237.
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