Hospitalization prediction from the emergency department using computer vision AI with short patient video clips

被引：0

作者：

Ip, Wui ^{[1
]}

Xenochristou, Maria ^{[2
]}

Sui, Elaine ^{[3
]}

Ruan, Elyse ^{[4
]}

Ribeira, Ryan ^{[5
]}

Dash, Debadutta ^{[5
]}

Srinivasan, Malathi ^{[6
]}

Artandi, Maja ^{[6
]}

Omiye, Jesutofunmi A. ^{[2
,7
]}

Scoulios, Nicholas ^{[6
]}

Hofmann, Hayden L. ^{[2
]}

Mottaghi, Ali ^{[8
]}

Weng, Zhenzhen ^{[9
]}

Kumar, Abhinav ^{[3
]}

Ganesh, Ananya ^{[3
]}

Fries, Jason ^{[10
]}

Yeung-Levy, Serena ^{[2
,3
,8
,11
,12
]}

Hofmann, Lawrence V. ^{[4
,13
]}

机构：

[1] Stanford Univ, Sch Med, Dept Pediat, Palo Alto, CA 94303 USA

[2] Stanford Univ, Sch Med, Dept Biomed Data Sci, Palo Alto, CA USA

[3] Stanford Univ, Dept Comp Sci, Palo Alto, CA USA

[4] Stanford Hlth Care, Digital Hlth Care Integrat, Palo Alto, CA USA

[5] Stanford Univ, Sch Med, Dept Emergency Med, Palo Alto, CA USA

[6] Stanford Univ, Dept Med, Sch Med, Palo Alto, CA USA

[7] Stanford Univ, Dept Dermatol, Sch Med, Palo Alto, CA USA

[8] Stanford Univ, Dept Elect Engn, Palo Alto, CA USA

[9] Stanford Univ, Inst Computat & Math Engn, Palo Alto, CA USA

[10] Stanford Ctr Biomed Informat Res, Palo Alto, CA USA

[11] Stanford Univ, Clin Excellence Res Ctr, Sch Med, Palo Alto, CA USA

[12] Chan Zuckerberg Biohub San Francisco, San Francisco, CA USA

[13] Stanford Univ, Dept Radiol, Sch Med, Palo Alto, CA USA

来源：

NPJ DIGITAL MEDICINE | 2024年 / 7卷 / 01期

关键词：

SYSTEM; 1; DISPOSITION; ADMISSION; VARIABILITY; ACCURACY; DECISION; ACUITY; MODELS; TRIAGE;

D O I：

10.1038/s41746-024-01375-3

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

In this study, we investigate the performance of computer vision AI algorithms in predicting patient disposition from the emergency department (ED) using short video clips. Clinicians often use "eye-balling" or clinical gestalt to aid in triage, based on brief observations. We hypothesize that AI can similarly use patient appearance for disposition prediction. Data were collected from adult patients at an academic ED, with mobile phone videos capturing patients performing simple tasks. Our AI algorithm, using video alone, showed better performance in predicting hospital admissions (AUROC = 0.693 [95% CI 0.689, 0.696]) compared to models using triage clinical data (AUROC = 0.678 [95% CI 0.668, 0.687]). Combining video and triage data achieved the highest predictive performance (AUROC = 0.714 [95% CI 0.709, 0.719]). This study demonstrates the potential of video AI algorithms to support ED triage and alleviate healthcare capacity strains during periods of high demand.

引用

页数：8

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