Automating detection of diagnostic error of infectious diseases using machine learning

机器学习 人工智能 急诊科 杠杆(统计) 医学 传染病(医学专业) 急诊分诊台 公制(单位) 疾病 计算机科学 急诊医学 病理 运营管理 精神科 经济
作者
Kelly Peterson,Alec B. Chapman,Wathsala Widanagamaachchi,Jesse Sutton,Brennan Ochoa,Barbara E. Jones,Vanessa Stevens,David C. Classen,Makoto Jones
出处
期刊:PLOS digital health [Public Library of Science]
卷期号:3 (6): e0000528-e0000528
标识
DOI:10.1371/journal.pdig.0000528
摘要

Diagnostic error, a cause of substantial morbidity and mortality, is largely discovered and evaluated through self-report and manual review, which is costly and not suitable to real-time intervention. Opportunities exist to leverage electronic health record data for automated detection of potential misdiagnosis, executed at scale and generalized across diseases. We propose a novel automated approach to identifying diagnostic divergence considering both diagnosis and risk of mortality. Our objective was to identify cases of emergency department infectious disease misdiagnoses by measuring the deviation between predicted diagnosis and documented diagnosis, weighted by mortality. Two machine learning models were trained for prediction of infectious disease and mortality using the first 24h of data. Charts were manually reviewed by clinicians to determine whether there could have been a more correct or timely diagnosis. The proposed approach was validated against manual reviews and compared using the Spearman rank correlation. We analyzed 6.5 million ED visits and over 700 million associated clinical features from over one hundred emergency departments. The testing set performances of the infectious disease (Macro F1 = 86.7, AUROC 90.6 to 94.7) and mortality model (Macro F1 = 97.6, AUROC 89.1 to 89.1) were in expected ranges. Human reviews and the proposed automated metric demonstrated positive correlations ranging from 0.231 to 0.358. The proposed approach for diagnostic deviation shows promise as a potential tool for clinicians to find diagnostic errors. Given the vast number of clinical features used in this analysis, further improvements likely need to either take greater account of data structure (what occurs before when) or involve natural language processing. Further work is needed to explain the potential reasons for divergence and to refine and validate the approach for implementation in real-world settings.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
白熊完成签到 ,获得积分10
刚刚
1秒前
李健应助北齐冲浪的鱼采纳,获得10
2秒前
2秒前
王一鸣发布了新的文献求助10
3秒前
ikutovaya完成签到,获得积分10
3秒前
3秒前
奋斗的妙松完成签到,获得积分10
4秒前
老实莫言完成签到,获得积分10
4秒前
5秒前
量子星尘发布了新的文献求助150
5秒前
wop111应助morph采纳,获得20
5秒前
追寻的冬寒完成签到 ,获得积分10
6秒前
7秒前
吼吼吼吼发布了新的文献求助10
7秒前
善学以致用应助生动念烟采纳,获得10
7秒前
由天与发布了新的文献求助10
8秒前
wsy发布了新的文献求助10
9秒前
11秒前
13秒前
13秒前
14秒前
lllllll完成签到,获得积分10
15秒前
15秒前
王一鸣完成签到,获得积分10
16秒前
量子星尘发布了新的文献求助10
18秒前
心灵尔安完成签到 ,获得积分10
19秒前
你终硕发布了新的文献求助10
19秒前
科研通AI6应助满意的又蓝采纳,获得10
19秒前
jwj完成签到,获得积分10
20秒前
21秒前
大个应助gc529采纳,获得10
22秒前
23秒前
27秒前
所所应助你在烦恼什么采纳,获得10
28秒前
852应助你终硕采纳,获得10
28秒前
核桃发布了新的文献求助10
28秒前
29秒前
鲤鱼晓瑶完成签到 ,获得积分10
29秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
NMR in Plants and Soils: New Developments in Time-domain NMR and Imaging 600
Electrochemistry: Volume 17 600
Physical Chemistry: How Chemistry Works 500
SOLUTIONS Adhesive restoration techniques restorative and integrated surgical procedures 500
Energy-Size Reduction Relationships In Comminution 500
Principles Of Comminution, I-Size Distribution And Surface Calculations 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 内科学 生物化学 物理 计算机科学 纳米技术 遗传学 基因 复合材料 化学工程 物理化学 病理 催化作用 免疫学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 4950711
求助须知:如何正确求助?哪些是违规求助? 4213460
关于积分的说明 13104286
捐赠科研通 3995337
什么是DOI,文献DOI怎么找? 2186837
邀请新用户注册赠送积分活动 1202090
关于科研通互助平台的介绍 1115359