Prediction on critically ill patients: The role of "big data"

被引：10

作者：

Bulgarelli, Lucas ^{[1
,2
]}

Deliberato, Rodrigo Octavio ^{[1
,3
]}

Johnson, Alistair E. W. ^{[1
]}

机构：

[1] MIT, MIT Crit Data, Lab Computat Physiol, Harvard MIT Hlth Sci & Technol, 77 Massachusetts Ave, Cambridge, MA 02139 USA

[2] Hosp Israelita Albert Einstein, Big Data Analyt Dept, Sao Paulo, Brazil

[3] Endpoint Hlth Inc, Dept Clin Data Sci Res, Palo Alto, CA USA

来源：

JOURNAL OF CRITICAL CARE | 2020年 / 60卷

基金：

美国国家卫生研究院;

关键词：

Critical Care; Outcome prediction; Machine learning; ARTIFICIAL NEURAL-NETWORKS; CHRONIC HEALTH EVALUATION; ACUTE PHYSIOLOGY SCORE; INTENSIVE-CARE-UNIT; HOSPITAL MORTALITY; ORGAN DYSFUNCTION; RISK PREDICTION; SEPSIS; APACHE; MODEL;

D O I：

10.1016/j.jcrc.2020.07.017

中图分类号：

R4 [临床医学];

学科分类号：

1002 ; 100602 ;

摘要：

Accurate outcome prediction in Intensive Care Units (ICUs) would allow for better treatment planning, risk adjustment of study populations, and overall improvements in patient care. In the past, prognostic models have focused on mortality using simple ordinal severity of illness scores which could be tabulated manually by a human. With the improvements in computing power and proliferation of electronic medical records, entirely new approaches have become possible. Here we review the latest advances in outcome prediction, paying close attention to methods which are widely applicable and provide a high-level overview of the challenges the field currently faces. (C) 2020 Elsevier Inc. All rights reserved.

引用

页码：64 / 68

页数：5

共 77 条

[1]

Aczon M, 2017, ARXIV170106675CSMATH

[2] Discrimination and Calibration of Clinical Prediction Models Users' Guides to the Medical Literature [J].