Open AccessEnsemble learning for poor prognosis predictions: A case study on SARS-CoV-2
Author(s) -
Honghan Wu,
Huayu Zhang,
Andreas Karwath,
Zina Ibrahim,
Ting Shi,
Xin Zhang,
Kun Wang,
Jiaxing Sun,
Kevin Dhaliwal,
Daniel Bean,
Victor Roth Cardoso,
Kezhi Li,
James Teo,
Amitava Banerjee,
Fang Gao Smith,
Tony Whitehouse,
Tonny Veenith,
Georgios V. Gkoutos,
Xiaodong Wu,
Richard Dobson,
Bruce Guthrie
Publication year - 2020
Publication title -
journal of the american medical informatics association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.614
H-Index - 150
eISSN - 1527-974X
pISSN - 1067-5027
DOI - 10.1093/jamia/ocaa295
Subject(s) - ensemble forecasting , predictive modelling , ensemble learning , computer science , machine learning , covid-19 , artificial intelligence , calibration , population , set (abstract data type) , pandemic , medicine , disease , statistics , mathematics , infectious disease (medical specialty) , environmental health , pathology , programming language
Risk prediction models are widely used to inform evidence-based clinical decision making. However, few models developed from single cohorts can perform consistently well at population level where diverse prognoses exist (such as the SARS-CoV-2 [severe acute respiratory syndrome coronavirus 2] pandemic). This study aims at tackling this challenge by synergizing prediction models from the literature using ensemble learning.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom