NDLI: Unbiased identification of risk factors for invasive Escherichia coli disease using machine learning

Content Provider	Springer Nature : BioMed Central
Author	Clarke, Erik Chehoud, Christel Khan, Najat Spiessens, Bart Poolman, Jan Geurtsen, Jeroen
Abstract	Background Invasive Escherichia coli disease (IED), also known as invasive extraintestinal pathogenic E. coli disease, is a leading cause of sepsis and bacteremia in older adults that can result in hospitalization and sometimes death and is frequently associated with antimicrobial resistance. Moreover, certain patient characteristics may increase the risk of developing IED. This study aimed to validate a machine learning approach for the unbiased identification of potential risk factors that correlate with an increased risk for IED. Methods Using electronic health records from 6.5 million people, an XGBoost model was trained to predict IED from 663 distinct patient features, and the most predictive features were identified as potential risk factors. Using Shapley Additive predictive values, the specific relationships between features and the outcome of developing IED were characterized. Results The model independently predicted that older age, a known risk factor for IED, increased the chance of developing IED. The model also predicted that a history of ≥ 1 urinary tract infection, as well as more frequent and/or more recent urinary tract infections, and ≥ 1 emergency department or inpatient visit increased the risk for IED. Outcomes were used to calculate risk ratios in selected subpopulations, demonstrating the impact of individual or combinations of features on the incidence of IED. Conclusion This study illustrates the viability and validity of using large electronic health records datasets and machine learning to identify correlating features and potential risk factors for infectious diseases, including IED. The next step is the independent validation of potential risk factors using conventional methods.
Related Links	https://bmcinfectdis.biomedcentral.com/counter/pdf/10.1186/s12879-024-09669-3.pdf
Ending Page	12
Page Count	12
Starting Page	1
File Format	HTM / HTML
ISSN	14712334
DOI	10.1186/s12879-024-09669-3
Journal	BMC Infectious Diseases
Issue Number	1
Volume Number	24
Language	English
Publisher	BioMed Central
Publisher Date	2024-08-08
Access Restriction	Open
Subject Keyword	Infectious Diseases Parasitology Medical Microbiology Tropical Medicine Internal Medicine Invasive E. coli disease Machine learning Electronic health records Disease burden per status today
Content Type	Text
Resource Type	Article
Subject	Infectious Diseases
Journal Impact Factor	3.4/2023
5-Year Journal Impact Factor	3.3/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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