Accelerating Chart Review Using Automated Methods on Electronic Health Record Data for Postoperative Complications. Article Swipe
Zhen Hu
,
Genevieve B. Melton
,
Nathan D Moeller
,
Elliot G. Arsoniadis
,
Yan Wang
,
Mary R. Kwaan
,
Eric H. Jensen
,
György Simon
·
YOU?
·
· 2016
· Open Access
·
YOU?
·
· 2016
· Open Access
·
Manual Chart Review (MCR) is an important but labor-intensive task for clinical research and quality improvement. In this study, aiming to accelerate the process of extracting postoperative outcomes from medical charts, we developed an automated postoperative complications detection application by using structured electronic health record (EHR) data. We applied several machine learning methods to the detection of commonly occurring complications, including three subtypes of surgical site infection, pneumonia, urinary tract infection, sepsis, and septic shock. Particularly, we applied one single-task and five multi-task learning methods and compared their detection performance. The models demonstrated high detection performance, which ensures the feasibility of accelerating MCR. Specifically, one of the multi-task learning methods, propensity weighted observations (PWO) demonstrated the highest detection performance, with single-task learning being a close second.
Related Topics
Concepts
Task (project management)
Computer science
Chart
Electronic health record
Medical record
Process (computing)
Electronic medical record
Septic shock
Health records
Medicine
Machine learning
Sepsis
Artificial intelligence
Medical emergency
Surgery
Health care
Operating system
Engineering
Systems engineering
Economics
Statistics
Economic growth
Mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://pubmed.ncbi.nlm.nih.gov/28269941
- OA Status
- green
- Cited By
- 28
- References
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2620686777
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2620686777Canonical identifier for this work in OpenAlex
- Title
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Accelerating Chart Review Using Automated Methods on Electronic Health Record Data for Postoperative Complications.Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2016Year of publication
- Publication date
-
2016-01-01Full publication date if available
- Authors
-
Zhen Hu, Genevieve B. Melton, Nathan D Moeller, Elliot G. Arsoniadis, Yan Wang, Mary R. Kwaan, Eric H. Jensen, György SimonList of authors in order
- Landing page
-
https://pubmed.ncbi.nlm.nih.gov/28269941Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/5333220Direct OA link when available
- Concepts
-
Task (project management), Computer science, Chart, Electronic health record, Medical record, Process (computing), Electronic medical record, Septic shock, Health records, Medicine, Machine learning, Sepsis, Artificial intelligence, Medical emergency, Surgery, Health care, Operating system, Engineering, Systems engineering, Economics, Statistics, Economic growth, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
28Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2023: 1, 2022: 6, 2021: 3, 2020: 6Per-year citation counts (last 5 years)
- References (count)
-
14Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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