NDLI: Risk assessment and prediction of nosocomial infections based on surveillance data using machine learning methods

Content Provider	Springer Nature : BioMed Central
Author	Chen, Ying Zhang, Yonghong Nie, Shuping Ning, Jie Wang, Qinjin Yuan, Hanmei Wu, Hui Li, Bin Hu, Wenbiao Wu, Chao
Abstract	Background Nosocomial infections with heavy disease burden are becoming a major threat to the health care system around the world. Through long-term, systematic, continuous data collection and analysis, Nosocomial infection surveillance (NIS) systems are constructed in each hospital; while these data are only used as real-time surveillance but fail to realize the prediction and early warning function. Study is to screen effective predictors from the routine NIS data, through integrating the multiple risk factors and Machine learning (ML) methods, and eventually realize the trend prediction and risk threshold of Incidence of Nosocomial infection (INI). Methods We selected two representative hospitals in southern and northern China, and collected NIS data from 2014 to 2021. Thirty-nine factors including hospital operation volume, nosocomial infection, antibacterial drug use and outdoor temperature data, etc. Five ML methods were used to fit the INI prediction model respectively, and to evaluate and compare their performance. Results Compared with other models, Random Forest showed the best performance (5-fold AUC = 0.983) in both hospitals, followed by Support Vector Machine. Among all the factors, 12 indicators were significantly different between high-risk and low-risk groups for INI (P < 0.05). After screening the effective predictors through importance analysis, prediction model of the time trend was successfully constructed (R2 = 0.473 and 0.780, BIC = -1.537 and -0.731). Conclusions The number of surgeries, antibiotics use density, critical disease rate and unreasonable prescription rate and other key indicators could be fitted to be the threshold predictions of INI and quantitative early warning.
Related Links	https://bmcpublichealth.biomedcentral.com/counter/pdf/10.1186/s12889-024-19096-3.pdf
Ending Page	9
Page Count	9
Starting Page	1
File Format	HTM / HTML
ISSN	14712458
DOI	10.1186/s12889-024-19096-3
Journal	BMC Public Health
Issue Number	1
Volume Number	24
Language	English
Publisher	BioMed Central
Publisher Date	2024-07-04
Access Restriction	Open
Subject Keyword	Public Health Medicine Epidemiology Biostatistics Vaccine Environmental Health Nosocomial infections Hospital-acquired infections (HAI) Prediction Machine learning Early warning Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Public Health, Environmental and Occupational Health
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	3.9/2023

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

Risk assessment of imported malaria in China: a machine learning perspective

Assessment of electronic disease early warning system for improved disease surveillance and outbreak response in Yemen

Population-based bloodstream infection surveillance in rural Thailand, 2007–2014

Comparing national infectious disease surveillance systems: China and the Netherlands

The role of medical equipment in the spread of nosocomial infections: a cross-sectional study in four tertiary public health facilities in Uganda

Constructing a fall risk prediction model for hospitalized patients using machine learning

Survival rate in patients with ICU-acquired infections and its related factors in Iran’s hospitals

Prediction of adolescent weight status by machine learning: a population-based study

Reviewing the progress of infectious disease early warning systems and planning for the future

Risk assessment and prediction of nosocomial infections based on surveillance data using machine learning methods

Similar Documents

Risk assessment of imported malaria in China: a machine learning perspective

Assessment of electronic disease early warning system for improved disease surveillance and outbreak response in Yemen

Population-based bloodstream infection surveillance in rural Thailand, 2007–2014

Comparing national infectious disease surveillance systems: China and the Netherlands

The role of medical equipment in the spread of nosocomial infections: a cross-sectional study in four tertiary public health facilities in Uganda

Constructing a fall risk prediction model for hospitalized patients using machine learning

Survival rate in patients with ICU-acquired infections and its related factors in Iran’s hospitals

Prediction of adolescent weight status by machine learning: a population-based study

Reviewing the progress of infectious disease early warning systems and planning for the future

Risk assessment and prediction of nosocomial infections based on surveillance data using machine learning methods