Academic literature on the topic 'Personalized prediction'
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Journal articles on the topic "Personalized prediction":
Galetzka, Wolfgang, Bernd Kowall, Cynthia Jusi, Eva-Maria Huessler, and Andreas Stang. "Distance-Metric Learning for Personalized Survival Analysis." Entropy 25, no. 10 (September 30, 2023): 1404. http://dx.doi.org/10.3390/e25101404.
Thoma, Clemens. "Personalized response prediction." Nature Reviews Gastroenterology & Hepatology 15, no. 11 (October 2, 2018): 657. http://dx.doi.org/10.1038/s41575-018-0072-z.
Liu, Jie, Bin Liu, Yanchi Liu, Huipeng Chen, Lina Feng, Hui Xiong, and Yalou Huang. "Personalized Air Travel Prediction." ACM Transactions on Intelligent Systems and Technology 9, no. 3 (February 13, 2018): 1–26. http://dx.doi.org/10.1145/3078845.
TAYEBI, MOHAMMAD A., UWE GLÄSSER, MARTIN ESTER, and PATRICIA L. BRANTINGHAM. "Personalized crime location prediction." European Journal of Applied Mathematics 27, no. 3 (April 28, 2016): 422–50. http://dx.doi.org/10.1017/s0956792516000140.
Gusev, I. V., D. V. Gavrilov, R. E. Novitsky, T. Yu Kuznetsova, and S. A. Boytsov. "Improvement of cardiovascular risk assessment using machine learning methods." Russian Journal of Cardiology 26, no. 12 (October 25, 2021): 4618. http://dx.doi.org/10.15829/1560-4071-2021-4618.
Localio, A. Russell, Cynthia D. Mulrow, and Michael E. Griswold. "Advancing Personalized Medicine Through Prediction." Annals of Internal Medicine 172, no. 1 (November 12, 2019): 63. http://dx.doi.org/10.7326/m19-3010.
Xu, Yanyu, Shenghua Gao, Junru Wu, Nianyi Li, and Jingyi Yu. "Personalized Saliency and Its Prediction." IEEE Transactions on Pattern Analysis and Machine Intelligence 41, no. 12 (December 1, 2019): 2975–89. http://dx.doi.org/10.1109/tpami.2018.2866563.
Vassileva, Vessela. "Prostate cancer—personalized response prediction." Nature Reviews Clinical Oncology 6, no. 11 (November 2009): 618. http://dx.doi.org/10.1038/nrclinonc.2009.156.
Lee, Chuan-Chun, Chia-Jui Yen, and Tsunglin Liu. "Prediction of personalized microRNA activity." Gene 518, no. 1 (April 2013): 101–6. http://dx.doi.org/10.1016/j.gene.2012.11.068.
Chen, Rirong, Jieqi Zheng, Li Li, Chao Li, Kang Chao, Zhirong Zeng, Minhu Chen, and Shenghong Zhang. "Metabolomics facilitate the personalized management in inflammatory bowel disease." Therapeutic Advances in Gastroenterology 14 (January 2021): 175628482110644. http://dx.doi.org/10.1177/17562848211064489.
Dissertations / Theses on the topic "Personalized prediction":
Fernando, Warnakulasuriya Chandima. "Blood Glucose Prediction Models for Personalized Diabetes Management." Thesis, North Dakota State University, 2018. https://hdl.handle.net/10365/28179.
Shen, Yuanyuan. "Ordinal Outcome Prediction and Treatment Selection in Personalized Medicine." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:17463982.
Biostatistics
Reggiani, Francesco. "Development and assessment of bioinformatics methods for personalized medicine." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3424693.
Il genoma umano è una risorsa ricca di informazioni per i ricercatori che si dedicano allo studio delle patologie complesse. L’obiettivo di questo genere di ricerche è giungere ad una migliore comprensione di queste malattie e quindi sviluppare nuove strategie terapeutiche per la cura dei pazienti affetti. Dall’inizio di questo secolo, un numero crescente di tecnologie per il sequenziamento del DNA sono state sviluppate, sono conosciute come tecnologie “Next Generation Sequencing” (NGS). Le tecnologie NGS hanno gradualmente diminuito il costo del sequenziamento di un genoma umano fino a circa 1000 dollari, ciò ha consentito l’utilizzo di questi strumenti nella pratica clinica e nella ricerca, in particolare negli studi di associazione genome-wide o “Genome-wide association studies” (GWAS). Questi lavori hanno portato alla luce l’associazione di alcune varianti con alcune patologie o caratteri complessi. Queste varianti potrebbero essere utilizzate per valutare il rischio che un individuo sviluppi una particolare patologia. Sfortunatamente diverse sorgenti di errore sono in grado di ostacolare l’uso e l’interpretazione dei dati genomici: da una parte abbiamo il rumore legato al processo di sequenziamento e gli errori di allineamento delle reads. Dall’altra parte gli SNP non sempre possono essere utilizzati in modo affidabile per predire l’insorgenza della malattia a cui sono stati associati. Il Critical Assessment of Genome Interpretation è stato organizzato con l’obiettivo di definire lo stato dell’arte nei metodi che stimano l’effetto di variazioni genetiche a livello molecolare o fenotipico. Negli anni il CAGI ha dato vita a più competizioni in cui diversi gruppi di ricerca hanno testato i loro metodi di predizione su diversi dataset condivisi. L’assenza di linee generali su come condurre la valutazione delle performance dei predittori, ha reso difficile un confronto fra metodi sviluppati in edizioni diverse del CAGI. In questo contesto, il progetto di dottorato si è focalizzato nello sviluppo di un software per la valutazione di metodi di apprendimento automatici basati sulla regressione o la predizione di fenotipi multipli. Questo strumento si fonda su criteri di analisi della performance, derivanti dalla letteratura e da precedenti esperimenti del CAGI. Questo software è stato sviluppato in R ed utilizzato per ripetere o valutare ex novo la qualità dei predittori in un gran numero di esperimenti del CAGI. Le conoscenze acquisite durante lo sviluppo di questo progetto, sono state utilizzate per valutare due competizioni del CAGI 5: la Pericentriolar Material 1 (PCM1) e il Pannello per le Disabilità Intellettive (ID). L’esperienza derivante dal completamento dei lavori precedentemente elencati, ha guidato lo sviluppo e il miglioramento delle prestazioni di un metodo predittivo. In particolare è stato sviluppato un software per la predizione dei livelli di colesterolo, basato su dati genotipici, di cui è stata testata la validità con criteri matematici allo stato dell’arte. Questo strumento è stato la pietra portante di un progetto fondato dal Ministero della Salute Italiano.
Bucci, Francesca. "Personalized biomechanical model of a patient with severe hip osteoarthritis for the prediction of pelvic biomechanics." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/15879/.
Youssfi, Younès. "Exploring Risk Factors and Prediction Models for Sudden Cardiac Death with Machine Learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG006.
Sudden cardiac death (SCD) is defined as a sudden natural death presumed to be of cardiac cause, heralded by abrupt loss of consciousness in the presence of witness, or in the absence of witness occurring within an hour after the onset of symptoms. Despite progress in clinical profiling and interventions, it remains a major public health problem, accounting for 10 to 20% of deaths in industrialised countries, with survival after SCD below 10%. The annual incidence is estimated 350,000 in Europe, and 300,000 in the United States. Efficient treatments for SCD management are available. One of the most effective options is the use of implantable cardioverter defibrillators (ICD). However, identifying the best candidates for ICD implantation remains a difficult challenge, with disappointing results so far. This thesis aims to address this problem, and to provide a better understanding of SCD in the general population, using statistical modeling. We analyze data from the Paris Sudden Death Expertise Center and the French National Healthcare System Database to develop three main works:- The first part of the thesis aims to identify new subgroups of SCD to improve current stratification guidelines, which are mainly based on cardiovascular variables. To this end, we use natural language processing methods and clustering analysis to build a meaningful representation of medical history of patients.- The second part aims to build a prediction model of SCD in order to propose a personalized and explainable risk score for each patient, and accurately identify very-high risk subjects in the general population. To this end, we train a supervised classification algorithm, combined with the SHapley Additive exPlanation method, to analyze all medical events that occurred up to 5 years prior to the event.- The last part of the thesis aims to identify the most relevant information to select in large medical history of patients. We propose a bi-level variable selection algorithm for generalized linear models, in order to identify both individual and group effects from predictors. Our algorithm is based on a Bayesian approach and uses a Sequential Monte Carlo method to estimate the posterior distribution of variables inclusion
Bellón, Molina Víctor. "Prédiction personalisée des effets secondaires indésirables de médicaments." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEM023/document.
Adverse drug reaction (ADR) is a serious concern that has important health and economical repercussions. Between 1.9%-2.3% of the hospitalized patients suffer from ADR, and the annual cost of ADR have been estimated to be of 400 million euros in Germany alone. Furthermore, ADRs can cause the withdrawal of a drug from the market, which can cause up to millions of dollars of losses to the pharmaceutical industry.Multiple studies suggest that genetic factors may play a role in the response of the patients to their treatment. This covers not only the response in terms of the intended main effect, but also % according toin terms of potential side effects. The complexity of predicting drug response suggests that machine learning could bring new tools and techniques for understanding ADR.In this doctoral thesis, we study different problems related to drug response prediction, based on the genetic characteristics of patients.We frame them through multitask machine learning frameworks, which combine all data available for related problems in order to solve them at the same time.We propose a novel model for multitask linear prediction that uses task descriptors to select relevant features and make predictions with better performance as state-of-the-art algorithms. Finally, we study strategies for increasing the stability of the selected features, in order to improve interpretability for biological applications
Wood, Dawn Helaine. "Personality representation : predicting behaviour for personalised learning support." Thesis, University of Hull, 2010. http://hydra.hull.ac.uk/resources/hull:6862.
Levillain, Hugo. "Prediction and improvement of radioembolization outcome using personalised treatment and dosimetry." Doctoral thesis, Universite Libre de Bruxelles, 2021. https://dipot.ulb.ac.be/dspace/bitstream/2013/320561/3/PhDTM.docx.
Doctorat en Sciences biomédicales et pharmaceutiques (Médecine)
info:eu-repo/semantics/nonPublished
Tay, Darwin. "Decision support continuum paradigm for cardiovascular disease : towards personalized predictive models." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/25032.
Mohammed, Rafiq. "Personalized call center traffic prediction to enhance management solution with reference to call traffic jam mitigation a case study on Telecom New Zealand Ltd. : a dissertation submitted to Auckland University of Technology in partial fulfillment of the requirements for the degree of Master of Computer and Information Sciences (MCIS), 2008 /." Click here to access this resource online, 2008. http://hdl.handle.net/10292/479.
Books on the topic "Personalized prediction":
Grech, Godfrey, and Iris Grossman, eds. Preventive and Predictive Genetics: Towards Personalised Medicine. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15344-5.
Olga, Golubnitschaja, ed. Predictive diagnostics and personalized treatment: Dream or reality. Hauppauge, NY: Nova Science Publishers, 2009.
Podbielska, Halina, and Marko Kapalla, eds. Predictive, Preventive, and Personalised Medicine: From Bench to Bedside. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34884-6.
Chaari, Lotfi, ed. Digital Health Approach for Predictive, Preventive, Personalised and Participatory Medicine. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11800-6.
Chaari, Lotfi, ed. Digital Health in Focus of Predictive, Preventive and Personalised Medicine. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49815-3.
Berliner, Leonard, and Heinz U. Lemke, eds. An Information Technology Framework for Predictive, Preventive and Personalised Medicine. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12166-6.
Barh, Debmalya. Precision Medicine: Prediction, Prevention with Personalization. Taylor & Francis Group, 2018.
Mansnérus, Juli, Raimo Lahti, and Amanda Blick, eds. Personalized medicine: Legal and ethical challenges. University of Helsinki, Faculty of Law, 2020. http://dx.doi.org/10.31885/9789515169419.
Wunsch, 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.
Fotiadis, Dimitrios I., Eleni I. Georga, and Stelios K. Tigas. Personalized Predictive Modelling in Type1 Diabetes. Elsevier Science & Technology Books, 2017.
Book chapters on the topic "Personalized prediction":
Spöring, Francesco. "Personalized Antidepressant Prescription." In Medical Ethics, Prediction, and Prognosis, 133–47. 1 [edition]. | New York : Routledge, 2017. | Series: Routledge annals of bioethics ; 17: Routledge, 2017. http://dx.doi.org/10.4324/9781315208084-11.
Tayebi, Mohammad A., and Uwe Glässer. "Personalized Crime Location Prediction." In Social Network Analysis in Predictive Policing, 99–126. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41492-8_7.
Emura, Takeshi, Shigeyuki Matsui, and Virginie Rondeau. "Personalized Dynamic Prediction of Survival." In Survival Analysis with Correlated Endpoints, 77–93. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3516-7_5.
Yeh, Chan-Chang, Shian-Shyong Tseng, Pei-Chin Tsai, and Jui-Feng Weng. "Building a Personalized Music Emotion Prediction System." In Advances in Multimedia Information Processing - PCM 2006, 730–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11922162_84.
Bahi, Abderaouf, Ibtissem Gasmi, and Sassi Bentrad. "Personalized Movie Recommendation Prediction Using Reinforcement Learning." In Communications in Computer and Information Science, 46–56. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43838-7_4.
Purushotham, Sanjay, and C. C. Jay Kuo. "Modeling Group Dynamics for Personalized Group-Event Recommendation." In Social Computing, Behavioral-Cultural Modeling, and Prediction, 405–11. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16268-3_51.
Zhang, Ling, Le Lu, Ronald M. Summers, Electron Kebebew, and Jianhua Yao. "Personalized Pancreatic Tumor Growth Prediction via Group Learning." In Lecture Notes in Computer Science, 424–32. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66185-8_48.
Fuerst, B., T. Mansi, Jianwen Zhang, P. Khurd, J. Declerck, T. Boettger, Nassir Navab, J. Bayouth, Dorin Comaniciu, and A. Kamen. "A Personalized Biomechanical Model for Respiratory Motion Prediction." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012, 566–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33454-2_70.
Zhang, Lei, Jian Tang, and Ming Zhang. "Integrating Temporal Usage Pattern into Personalized Tag Prediction." In Web Technologies and Applications, 354–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29253-8_30.
Wu, Yao, Hong Huang, and Hai Jin. "Information Diffusion Prediction with Personalized Graph Neural Networks." In Knowledge Science, Engineering and Management, 376–87. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55393-7_34.
Conference papers on the topic "Personalized prediction":
Jiang, Tian, Lin Tan, and Sunghun Kim. "Personalized defect prediction." In 2013 IEEE/ACM 28th International Conference on Automated Software Engineering (ASE). IEEE, 2013. http://dx.doi.org/10.1109/ase.2013.6693087.
Whitehill, Jacob, and Javier R. Movellan. "Personalized facial attractiveness prediction." In Gesture Recognition (FG). IEEE, 2008. http://dx.doi.org/10.1109/afgr.2008.4813332.
Suzuki, Masahiro, Shomu Furuta, and Yusuke Fukazawa. "Personalized human mobility prediction for HuMob challenge." In HuMob-Challenge '23: 1st International Workshop on the Human Mobility Prediction Challenge. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3615894.3628501.
Losing, Viktor, Barbara Hammer, and Heiko Wersing. "Personalized maneuver prediction at intersections." In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2017. http://dx.doi.org/10.1109/itsc.2017.8317760.
Wang, Chung-Che, Yu-Chun Lin, Yu-Teng Hsu, and Jyh-Shing Roger Jang. "Personalized Audio Quality Preference Prediction." In 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2023. http://dx.doi.org/10.1109/apsipaasc58517.2023.10317345.
Bhoi, Suman, Mong Li Lee, Wynne Hsu, Hao Sen Andrew Fang, and Ngiap Chuan Tan. "Chronic Disease Management with Personalized Lab Test Response Prediction." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/699.
Cheng, Haibin, and Erick Cantú-Paz. "Personalized click prediction in sponsored search." In the third ACM international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1718487.1718531.
Chen, Guangyi, Junlong Li, Nuoxing Zhou, Liangliang Ren, and Jiwen Lu. "Personalized Trajectory Prediction via Distribution Discrimination." In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.01529.
Khademi, Aria, Yasser El-Manzalawy, Orfeu M. Buxton, and Vasant Honavar. "Toward personalized sleep-wake prediction from actigraphy." In 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE, 2018. http://dx.doi.org/10.1109/bhi.2018.8333456.
Wenqi You, Alena Simalatsar, and Giovanni De Micheli. "Parameterized SVM for personalized drug concentration prediction." In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013. http://dx.doi.org/10.1109/embc.2013.6610867.
Reports on the topic "Personalized prediction":
Manski, Charles. Probabilistic Prediction for Binary Treatment Choice: with Focus on Personalized Medicine. Cambridge, MA: National Bureau of Economic Research, October 2021. http://dx.doi.org/10.3386/w29358.
Zhang, Yu, Chaoliang Sun, Hengxi Xu, Weiyang Shi, Luqi Cheng, Alain Dagher, Yuanchao Zhang, and Tianzi Jiang. Connectivity-Based Subtyping of De Novo Parkinson Disease: Biomarkers, Medication Effects and Longitudinal Progression. Progress in Neurobiology, April 2024. http://dx.doi.org/10.60124/j.pneuro.2024.10.04.
Making personalised predictions of poor functioning following negative childhood experiences. ACAMH, December 2020. http://dx.doi.org/10.13056/acamh.14059.