Relevant bibliographies by topics / Personalized prediction

Contents

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

Academic literature on the topic 'Personalized prediction'

Author: Grafiati
Published: 1 June 2024
Last updated: 19 July 2025

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Journal articles on the topic "Personalized prediction"

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Thoma, Clemens. "Personalized response prediction." Nature Reviews Gastroenterology & Hepatology 15, no. 11 (2018): 657. http://dx.doi.org/10.1038/s41575-018-0072-z.

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Liu, Jie, Bin Liu, Yanchi Liu, et al. "Personalized Air Travel Prediction." ACM Transactions on Intelligent Systems and Technology 9, no. 3 (2018): 1–26. http://dx.doi.org/10.1145/3078845.

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TAYEBI, MOHAMMAD A., UWE GLÄSSER, MARTIN ESTER, and PATRICIA L. BRANTINGHAM. "Personalized crime location prediction." European Journal of Applied Mathematics 27, no. 3 (2016): 422–50. http://dx.doi.org/10.1017/s0956792516000140.

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Crime reduction and prevention strategies are vital for policymakers and law enforcement to face inevitable increases in urban crime rates as a side effect of the projected growth of urban population by the year 2030. Studies conclude that crime does not occur uniformly across urban landscapes but concentrates in certain areas. This phenomenon has drawn attention to spatial crime analysis, primarily focusing on crime hotspots, areas with disproportionally higher crime density. In this paper, we present CrimeTracer1, a personalized random walk-based approach to spatial crime analysis and crime
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Galetzka, Wolfgang, Bernd Kowall, Cynthia Jusi, Eva-Maria Huessler, and Andreas Stang. "Distance-Metric Learning for Personalized Survival Analysis." Entropy 25, no. 10 (2023): 1404. http://dx.doi.org/10.3390/e25101404.

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Personalized time-to-event or survival prediction with right-censored outcomes is a pervasive challenge in healthcare research. Although various supervised machine learning methods, such as random survival forests or neural networks, have been adapted to handle such outcomes effectively, they do not provide explanations for their predictions, lacking interpretability. In this paper, an alternative method for survival prediction by weighted nearest neighbors is proposed. Fitting this model to data entails optimizing the weights by learning a metric. An individual prediction of this method can b
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M Durugkar, Sneha, and Saudagar S Barde. "Privacy Protection in Personalized Web Search Using Metric Prediction." International Journal of Scientific Engineering and Research 3, no. 9 (2015): 65–68. https://doi.org/10.70729/ijser15467.

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Localio, A. Russell, Cynthia D. Mulrow, and Michael E. Griswold. "Advancing Personalized Medicine Through Prediction." Annals of Internal Medicine 172, no. 1 (2019): 63. http://dx.doi.org/10.7326/m19-3010.

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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 (2019): 2975–89. http://dx.doi.org/10.1109/tpami.2018.2866563.

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Vassileva, Vessela. "Prostate cancer—personalized response prediction." Nature Reviews Clinical Oncology 6, no. 11 (2009): 618. http://dx.doi.org/10.1038/nrclinonc.2009.156.

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Lee, Chuan-Chun, Chia-Jui Yen, and Tsunglin Liu. "Prediction of personalized microRNA activity." Gene 518, no. 1 (2013): 101–6. http://dx.doi.org/10.1016/j.gene.2012.11.068.

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Kyrochristos, Ioannis D., Demosthenes E. Ziogas, and Dimitrios H. Roukos. "Precision in personalized prediction-based medicine." Personalized Medicine 15, no. 6 (2018): 467–70. http://dx.doi.org/10.2217/pme-2018-0079.

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Dissertations / Theses on the topic "Personalized prediction"

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Fernando, Warnakulasuriya Chandima. "Blood Glucose Prediction Models for Personalized Diabetes Management." Thesis, North Dakota State University, 2018. https://hdl.handle.net/10365/28179.

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Effective blood glucose (BG) control is essential for patients with diabetes. This calls for an immediate need to closely keep track of patients' BG level all the time. However, sometimes individual patients may not be able to monitor their BG level regularly due to all kinds of real-life interference. To address this issue, in this paper we propose machine-learning based prediction models that can automatically predict patients BG level based on their historical data and known current status. We take two approaches, one for predicting BG level only using individual's data and second is to use
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Shen, Yuanyuan. "Ordinal Outcome Prediction and Treatment Selection in Personalized Medicine." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:17463982.

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In personalized medicine, two important tasks are predicting disease risk and selecting appropriate treatments for individuals based on their baseline information. The dissertation focuses on providing improved risk prediction for ordinal outcome data and proposing score-based test to identify informative markers for treatment selection. In Chapter 1, we take up the first problem and propose a disease risk prediction model for ordinal outcomes. Traditional ordinal outcome models leave out intermediate models which may lead to suboptimal prediction performance; they also don't allow for non-lin
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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.

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The human genome is a source of information for researchers that study complex diseases with the perspective of a better understanding of the pathologies and the development of new therapeutic strategies. Starting from the beginning of the current century, a growing number of technologies devoted to DNA sequencing have emerged, generally referred to as Next Generation Sequencing (NGS) technologies. NGS gradually decreased the cost of sequencing a human genome to around US$1000, enabling the use of these technologies for clinical and research purposes, such as Genome-wide association studies (G
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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/.

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L’articolazione dell'anca è un'articolazione sinoviale sferica che costituisce la connessione primaria tra gli arti inferiori e lo scheletro della parte superiore del corpo. Durante le attività quotidiane di routine, carichi anormali ripetuti sull'anca possono portare alla danneggiamento della cartilagine articolare e conseguentemente , all’osteoartrite (OA). L'OA dell'anca è una condizione muscolo-scheletrica cronica e progressiva, il cui trattamento per i pazienti severi è l'artroplastica totale dell'anca (THA). Il centro dell'articolazione dell'anca (HJC) ha grande importanza nell’analisi
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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.

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La mort subite de l'adulte est définie comme une mort inattendue sans cause extracardiaque évidente, survenant avec un effondrement rapide en présence d'un témoin, ou en l'absence de témoin dans l'heure après le début des symptômes. Son incidence est estimée à 350,000 personnes par an en Europe et 300,000 personnes aux Etats-Unis, ce qui représente 10 à 20% des décès dans les pays industrialisés. Malgré les progrès réalisés dans la prise en charge, le pronostic demeure extrêmement sombre. Moins de 10% des patients sortent vivants de l'hôpital après la survenue d'une mort subite. Les défibrilla
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Bellón, Molina Víctor. "Prédiction personalisée des effets secondaires indésirables de médicaments." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEM023/document.

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Les effets indésirables médicamenteux (EIM) ont des répercussions considérables tant sur la santé que sur l'économie. De 1,9% à 2,3% des patients hospitalisés en sont victimes, et leur coût a récemment été estimé aux alentours de 400 millions d'euros pour la seule Allemagne. De plus, les EIM sont fréquemment la cause du retrait d'un médicament du marché, conduisant à des pertes pour l'industrie pharmaceutique se chiffrant parfois en millions d'euros.De multiples études suggèrent que des facteurs génétiques jouent un rôle non négligeable dans la réponse des patients à leur traitement. Cette rép
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Wood, Dawn Helaine. "Personality representation : predicting behaviour for personalised learning support." Thesis, University of Hull, 2010. http://hydra.hull.ac.uk/resources/hull:6862.

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The need for personalised support systems comes from the growing number of students that are being supported within institutions with shrinking resources. Over the last decade the use of computers and the Internet within education has become more predominant. This opens up a range of possibilities in regard to spreading that resource further and more effectively. Previous attempts to create automated systems such as intelligent tutoring systems and learning companions have been criticised for being pedagogically ineffective and relying on large knowledge sources which restrict their domain of
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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.

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Radioembolization (also called selective internal radiation therapy, SIRT) with yttrium-90 (90Y)-loaded microspheres has been broadly adopted as a locoregional therapy for primary and metastatic liver cancers. Although radioembolization is a well-established therapy, efforts to personalise and refine the planning and administration of therapy are ongoing. The ability to accurately predict, plan and deliver optimal doses to tumour and non-tumour tissues, including final validation of dose distribution, is essential for successful radiotherapy. Determining the true dose absorbed by tissue compar
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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.

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Clinical decision making is a ubiquitous and frequent task physicians make in their daily clinical practice. Conventionally, physicians adopt a cognitive predictive modelling process (i.e. knowledge and experience learnt from past lecture, research, literature, patients, etc.) for anticipating or ascertaining clinical problems based on clinical risk factors that they deemed to be most salient. However, with the inundation of health data and the confounding characteristics of diseases, more effective clinical prediction approaches are required to address these challenges. Approximately a few ce
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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.

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Books on the topic "Personalized prediction"

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Grech, Godfrey, and Iris Grossman, eds. Preventive and Predictive Genetics: Towards Personalised Medicine. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15344-5.

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Olga, Golubnitschaja, ed. Predictive diagnostics and personalized treatment: Dream or reality. Nova Science Publishers, 2009.

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Podbielska, Halina, and Marko Kapalla, eds. Predictive, Preventive, and Personalised Medicine: From Bench to Bedside. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34884-6.

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Chaari, Lotfi, ed. Digital Health Approach for Predictive, Preventive, Personalised and Participatory Medicine. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11800-6.

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Chaari, Lotfi, ed. Digital Health in Focus of Predictive, Preventive and Personalised Medicine. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49815-3.

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Berliner, Leonard, and Heinz U. Lemke, eds. An Information Technology Framework for Predictive, Preventive and Personalised Medicine. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12166-6.

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Precision Medicine: Prediction, Prevention with Personalization. Taylor & Francis Group, 2018.

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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.

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This anthology deals with the legal and ethical challenges regarding personalized (precision) medicine and healthcare. It can also be regarded as the final report of a research project on the legal and ethical aspects of personalized medicine. It complements the reported results of the consortium ‘Personalised medicine to predict and prevent Type 1 Diabetes (P4 Diabetes)’ which were briefly presented in the booklet entitled ‘Better, Smarter, Now: Personalised Health – From Genes to Society (pHealth)’, Academy of Finland, Helsinki 2019. The articles of this anthology are not limited to the aspe
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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.

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Scoring systems for critically-ill patients provide a measure of the severity of illness of patients admitted to intensive care units (ICUs). They are primarily based on patient characteristics, physiological derangement, and/or clinical assessments. Severity scores themselves allow for risk-adjusting outcomes, but they can also be used to provide a prediction of the overall risk of death, length of stay, or other outcome for critically ill patients. This allows for comparison of outcomes between different cohorts of patients or between observed and predicted ICU performance. There are a numbe
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Personalized Predictive Modelling in Type1 Diabetes. Elsevier Science & Technology Books, 2017.

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Book chapters on the topic "Personalized prediction"

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Spöring, Francesco. "Personalized Antidepressant Prescription." In Medical Ethics, Prediction, and Prognosis. Routledge, 2017. http://dx.doi.org/10.4324/9781315208084-11.

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Tayebi, Mohammad A., and Uwe Glässer. "Personalized Crime Location Prediction." In Social Network Analysis in Predictive Policing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41492-8_7.

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Emura, Takeshi, Shigeyuki Matsui, and Virginie Rondeau. "Personalized Dynamic Prediction of Survival." In Survival Analysis with Correlated Endpoints. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3516-7_5.

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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. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11922162_84.

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Bahi, Abderaouf, Ibtissem Gasmi, and Sassi Bentrad. "Personalized Movie Recommendation Prediction Using Reinforcement Learning." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43838-7_4.

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Venkata Krishnaveni, B., E. John Alex, P. Venkatapathi, S. Mahesh Reddy, Mohammed Yasmeen, and S. Samatha Goud. "Machine Learning Algorithms for Personalized Disease Prediction." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4391-2_32.

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Purushotham, Sanjay, and C. C. Jay Kuo. "Modeling Group Dynamics for Personalized Group-Event Recommendation." In Social Computing, Behavioral-Cultural Modeling, and Prediction. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16268-3_51.

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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. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66185-8_48.

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Fuerst, B., T. Mansi, Jianwen Zhang, et al. "A Personalized Biomechanical Model for Respiratory Motion Prediction." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33454-2_70.

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Zhang, Lei, Jian Tang, and Ming Zhang. "Integrating Temporal Usage Pattern into Personalized Tag Prediction." In Web Technologies and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29253-8_30.

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Conference papers on the topic "Personalized prediction"

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S, Jayashree, Livedha S, T. Ragupathi, Kumaran Hariharan, R. Sanjana, and Dr Ginni Arora. "Personalized Dementia Prediction Using Machine Learning Techniques." In 2025 International Conference on Pervasive Computational Technologies (ICPCT). IEEE, 2025. https://doi.org/10.1109/icpct64145.2025.10941155.

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Wu, Jiang, Xuezheng Liu, Miao Hu, et al. "GazeFed: Privacy-Aware Personalized Gaze Prediction for Virtual Reality." In 2024 IEEE/ACM 32nd International Symposium on Quality of Service (IWQoS). IEEE, 2024. http://dx.doi.org/10.1109/iwqos61813.2024.10682864.

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Lai, Haojie, Junxin Guo, Xinxin Feng, and Haifeng Zheng. "Communication-Efficient Personalized Federated Learning for Traffic Flow Prediction." In 2024 10th International Conference on Computer and Communications (ICCC). IEEE, 2024. https://doi.org/10.1109/iccc62609.2024.10942186.

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Rossi, Leonardo, and Bruno Rodrigues. "Beyond Training: A Personalized Holistic Injury Prediction in Triathletes." In 2025 IEEE International Conference on Smart Computing (SMARTCOMP). IEEE, 2025. https://doi.org/10.1109/smartcomp65954.2025.00037.

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Loganathan, V., S. SreeSubha, T. A. Mohanaprakash, S. Ilavarasan, and J. ShaliniPriya. "Data-Driven Personalized Pregnancy Care: Exploratory Data Analysis and Ensemble Learning or Personalized Prediction of Gestational Diabetes." In 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS). IEEE, 2024. https://doi.org/10.1109/icpects62210.2024.10780356.

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Daniel Onwuchekwa, Jennifer I., Beyza Cinar, Louisa van den Boom, and Maria Maleshkova. "Time to Hypoglycemia Prediction for Personalized Diabetes Care and Management." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10782855.

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Rao R, Sujan, Vidhu Niranjan R, Mitali Narayan Saraf, Ayushee Bansal, and Arvind Upreti. "Personalized Credit Score Prediction and Improvement model using Machine Learning." In 2024 Asia Pacific Conference on Innovation in Technology (APCIT). IEEE, 2024. http://dx.doi.org/10.1109/apcit62007.2024.10673722.

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Heydari, A. Ali, Naghmeh Rezaei, Javier L. Prieto, Shwetak N. Patel, and Ahmed A. Metwally. "Lifestyle-Informed Personalized Blood Biomarker Prediction via Novel Representation Learning." In 2024 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE, 2024. https://doi.org/10.1109/bhi62660.2024.10913871.

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George, Justin M., Emmanuel Joy, and Suganthi Evangeline. "End-to-End Personalized Medical Recommendation Framework for Disease Prediction." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933217.

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Kaur, Arshleen, and Vinay Kukreja. "Interpretable Clothing Size Prediction: CNN and XAI for Personalized Recommendations." In 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM). IEEE, 2025. https://doi.org/10.1109/ictmim65579.2025.10988397.

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Reports on the topic "Personalized prediction"

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Manski, Charles. Probabilistic Prediction for Binary Treatment Choice: with Focus on Personalized Medicine. National Bureau of Economic Research, 2021. http://dx.doi.org/10.3386/w29358.

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Zhang, Yu, Chaoliang Sun, Hengxi Xu, et al. Connectivity-Based Subtyping of De Novo Parkinson Disease: Biomarkers, Medication Effects and Longitudinal Progression. Progress in Neurobiology, 2024. http://dx.doi.org/10.60124/j.pneuro.2024.10.04.

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Parkinson's disease (PD) is characterized by divergent clinical symptoms and prognosis, suggesting the presence of distinct subtypes. Identifying these subtypes is crucial for understanding the underlying pathophysiology, predicting disease progression, and developing personalized treatments. In this study, we propose a connectivity-based subtyping approach, which measures each patient's deviation from the reference structural covariance networks established in healthy controls. Using data from the Parkinson's Progression Markers Initiative, we identified two distinct subtypes of de novo PD pa
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Pasupuleti, Murali Krishna. AI-Driven Marketing Innovations: Personalization and Ethics in the Digital Era. National Education Services, 2025. https://doi.org/10.62311/nesx/rr625.

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Abstract: This article explores the transformative impact of artificial intelligence (AI) on digital marketing, focusing on strategies for delivering personalized content and ensuring ethical advertising. By leveraging AI, marketers can now analyze consumer behavior with precision, enabling targeted content, automated ad placement, and real-time adjustments that enhance user engagement and conversions. The Article examines foundational AI techniques, such as recommendation engines, predictive analytics, and natural language processing, which drive personalization at scale. Additionally, it add
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Latorre, Lucía, Eduardo Rego, Lorenzo De Leo, and Mariana Gutierrez. Tech Report: Digital Twins. Inter-American Development Bank, 2024. http://dx.doi.org/10.18235/0013166.

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Digital twins are finding innovative applications in a wide range of industries. In manufacturing, they enable product design optimization, predictive machinery maintenance, and customized production. Healthcare will benefit from precise diagnostics, personalized treatments, and advanced surgical planning. In city planning, they support efficient urban design and complex situation management. Regarding energy, they promote the efficiency and sustainability of systems and infrastructure. Finally, in the agricultural sector, they improve crop management, resource use and animal welfare.
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Making personalised predictions of poor functioning following negative childhood experiences. ACAMH, 2020. http://dx.doi.org/10.13056/acamh.14059.

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Experiencing abuse, neglect, bullying, or domestic violence in childhood increases the likelihood of having poor functioning in young adulthood, but this is not the case for everyone. Being able to accurately predict which individuals are at high risk for poor outcomes following such negative childhood experiences could support professionals to effectively target interventions. Is it possible to make accurate personalised predictions?
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