1 resultado para predictive models
em WestminsterResearch - UK
Filtro por publicador
- Abertay Research Collections - Abertay University’s repository (1)
- Aberystwyth University Repository - Reino Unido (2)
- Adam Mickiewicz University Repository (1)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (4)
- Aquatic Commons (9)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (2)
- Aston University Research Archive (12)
- Avian Conservation and Ecology - Eletronic Cientific Hournal - Écologie et conservation des oiseaux: (1)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (14)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (5)
- Biblioteca Digital de Teses e Dissertações Eletrônicas da UERJ (3)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (11)
- Brock University, Canada (1)
- CaltechTHESIS (2)
- Cambridge University Engineering Department Publications Database (17)
- CentAUR: Central Archive University of Reading - UK (30)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (2)
- Coffee Science - Universidade Federal de Lavras (1)
- Collection Of Biostatistics Research Archive (2)
- CORA - Cork Open Research Archive - University College Cork - Ireland (4)
- Corvinus Research Archive - The institutional repository for the Corvinus University of Budapest (1)
- CUNY Academic Works (1)
- Dalarna University College Electronic Archive (2)
- Deakin Research Online - Australia (53)
- Digital Commons - Michigan Tech (6)
- Digital Commons at Florida International University (9)
- Digital Knowledge Repository of Central Drug Research Institute (1)
- DigitalCommons@The Texas Medical Center (8)
- DigitalCommons@University of Nebraska - Lincoln (1)
- Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland (1)
- DRUM (Digital Repository at the University of Maryland) (2)
- Duke University (6)
- eResearch Archive - Queensland Department of Agriculture; Fisheries and Forestry (9)
- FAUBA DIGITAL: Repositorio institucional científico y académico de la Facultad de Agronomia de la Universidad de Buenos Aires (1)
- Glasgow Theses Service (1)
- Greenwich Academic Literature Archive - UK (4)
- Helda - Digital Repository of University of Helsinki (3)
- Indian Institute of Science - Bangalore - Índia (6)
- Institutional Repository of Leibniz University Hannover (1)
- Instituto Superior de Psicologia Aplicada - Lisboa (1)
- Massachusetts Institute of Technology (1)
- Memorial University Research Repository (1)
- National Center for Biotechnology Information - NCBI (2)
- Nottingham eTheses (2)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (2)
- Publishing Network for Geoscientific & Environmental Data (11)
- QSpace: Queen's University - Canada (1)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (23)
- Queensland University of Technology - ePrints Archive (540)
- Repositório Alice (Acesso Livre à Informação Científica da Embrapa / Repository Open Access to Scientific Information from Embrapa) (3)
- Repositório Científico da Universidade de Évora - Portugal (7)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (2)
- Repositório digital da Fundação Getúlio Vargas - FGV (4)
- Repositório Digital da UNIVERSIDADE DA MADEIRA - Portugal (2)
- Repositório Institucional da Universidade de Aveiro - Portugal (1)
- Repositório Institucional da Universidade de Brasília (1)
- Repositório Institucional da Universidade Federal do Rio Grande do Norte (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (17)
- Repositorio Institucional Universidad EAFIT - Medelin - Colombia (1)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (1)
- SAPIENTIA - Universidade do Algarve - Portugal (4)
- Universidad de Alicante (3)
- Universidad del Rosario, Colombia (2)
- Universidad Politécnica de Madrid (17)
- Universidade Complutense de Madrid (1)
- Universidade de Madeira (1)
- Universidade dos Açores - Portugal (1)
- Universidade Federal do Pará (1)
- Universidade Federal do Rio Grande do Norte (UFRN) (4)
- Universidade Metodista de São Paulo (3)
- Universitat de Girona, Spain (4)
- Université de Lausanne, Switzerland (2)
- Université de Montréal (1)
- Université de Montréal, Canada (10)
- Université Laval Mémoires et thèses électroniques (2)
- University of Queensland eSpace - Australia (9)
- University of Southampton, United Kingdom (2)
- University of Washington (4)
- WestminsterResearch - UK (1)
- Worcester Research and Publications - Worcester Research and Publications - UK (2)
Resumo:
Coping with an ageing population is a major concern for healthcare organisations around the world. The average cost of hospital care is higher than social care for older and terminally ill patients. Moreover, the average cost of social care increases with the age of the patient. Therefore, it is important to make efficient and fair capacity planning which also incorporates patient centred outcomes. Predictive models can provide predictions which their accuracy can be understood and quantified. Predictive modelling can help patients and carers to get the appropriate support services, and allow clinical decision-makers to improve care quality and reduce the cost of inappropriate hospital and Accident and Emergency admissions. The aim of this study is to provide a review of modelling techniques and frameworks for predictive risk modelling of patients in hospital, based on routinely collected data such as the Hospital Episode Statistics database. A number of sub-problems can be considered such as Length-of-Stay and End-of-Life predictive modelling. The methodologies in the literature are mainly focused on addressing the problems using regression methods and Markov models, and the majority lack generalisability. In some cases, the robustness, accuracy and re-usability of predictive risk models have been shown to be improved using Machine Learning methods. Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution. The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling.