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Automatic infection detection based on electronic medical records
- Source :
- BMC Bioinformatics, Vol 19, Iss S5, Pp 55-63 (2018), BMC Bioinformatics
- Publication Year :
- 2018
- Publisher :
- BMC, 2018.
-
Abstract
- Background Making accurate patient care decision, as early as possible, is a constant challenge, especially for physicians in the emergency department. The increasing volumes of electronic medical records (EMRs) open new horizons for automatic diagnosis. In this paper, we propose to use machine learning approaches for automatic infection detection based on EMRs. Five categories of information are utilized for prediction, including personal information, admission note, vital signs, diagnose test results and medical image diagnose. Results Experimental results on a newly constructed EMRs dataset from emergency department show that machine learning models can achieve a decent performance for infection detection with area under the receiver operator characteristic curve (AUC) of 0.88. Out of all the five types of information, admission note in text form makes the most contribution with the AUC of 0.87. Conclusions This study provides a state-of-the-art EMRs processing system to automatically make medical decisions. It extracts five types of features associated with infection and achieves a decent performance on automatic infection detection based on machine learning models.
- Subjects :
- 0301 basic medicine
New horizons
Computer science
Vital signs
Machine learning
computer.software_genre
lcsh:Computer applications to medicine. Medical informatics
Communicable Diseases
Biochemistry
Automation
03 medical and health sciences
Structural Biology
Automatic disease detection
Electronic Health Records
Humans
Molecular Biology
Electronic medical records
lcsh:QH301-705.5
Receiver operating characteristic
business.industry
Research
Applied Mathematics
Medical record
Natural language processing
Age Factors
Emergency department
Computer Science Applications
Infection detection
030104 developmental biology
lcsh:Biology (General)
lcsh:R858-859.7
Artificial intelligence
business
Admission note
computer
Personally identifiable information
Algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 14712105
- Volume :
- 19
- Database :
- OpenAIRE
- Journal :
- BMC Bioinformatics
- Accession number :
- edsair.doi.dedup.....7db0c30d2204efb618741a05b4c7efb5