The Use of Big Data in Public Health Research and Practice

Topic Information

Dear Colleagues,

We are organizing a Topic on the use of big data to inform health research and practice. To enable decision-making, it is important to obtain timely data on the determinants of health and well-being. Big data can often be operational or “organic” data generated for non-research purposes, including social media, news feeds, Google Street View images, online reviews, blogs, electronic health records, pharmacy records, and billing records. This Topic is focused on innovative ways that big data are leveraged for health research and practice. Some possible submission ideas are listed below; however, submissions addressing other related topics are also welcomed:

• Use of electronic health records, billing data, and pharmacy data to understand individualized risk factors and treatment success; 
• Characterization of built environments with big data derived from various sources (e.g., Street View images and remote sensing imagery data) as well as their impact on health; 
• Using various user-generated content (e.g., GPS data, accelerometer data, users’ review data, social media data, and web search data) to study individual behaviors and social/cultural environments as well as their impacts on people’s health; 
• Development of new methods or tools (e.g., natural language processing, machine learning, database management, high-performance computing, data mining, cloud computing, computer vision, visualization, geographic information systems, and spatial analysis) for big-data-based health research; 
• Use of big data in COVID-19-related research; 
• Application or development of causal inference methods for big data;
• Investigating and addressing data quality and uncertainty issues;
• Blending and integration of big data from different sources.

Dr. Quynh C. Nguyen
Dr. Thu T. Nguyen
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Cancers cancers	4.5	8.0	2009	16.3 Days	CHF 2900	Submit
International Journal of Environmental Research and Public Health ijerph	-	7.3	2004	24.3 Days	CHF 2500	Submit
ISPRS International Journal of Geo-Information ijgi	2.8	6.9	2012	36.2 Days	CHF 1700	Submit
Machine Learning and Knowledge Extraction make	4.0	6.3	2019	27.1 Days	CHF 1800	Submit
Smart Cities smartcities	7.0	11.2	2018	25.8 Days	CHF 2000	Submit

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Published Papers (1 paper)

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All Cancers IJERPH IJGI MAKE Smart Cities

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Open AccessArticle

Global and Local Interpretable Machine Learning Allow Early Prediction of Unscheduled Hospital Readmission

by Rafael Ruiz de San Martín, Catalina Morales-Hernández, Carmen Barberá, Carlos Martínez-Cortés, Antonio Jesús Banegas-Luna, Francisco José Segura-Méndez, Horacio Pérez-Sánchez, Isabel Morales-Moreno and Juan José Hernández-Morante

Mach. Learn. Knowl. Extr. 2024, 6(3), 1653-1666; https://doi.org/10.3390/make6030080 - 17 Jul 2024

Viewed by 279

Abstract

Nowadays, most of the health expenditure is due to chronic patients who are readmitted several times for their pathologies. Personalized prevention strategies could be developed to improve the management of these patients. The aim of the present work was to develop local predictive [...] Read more.

Nowadays, most of the health expenditure is due to chronic patients who are readmitted several times for their pathologies. Personalized prevention strategies could be developed to improve the management of these patients. The aim of the present work was to develop local predictive models using interpretable machine learning techniques to early identify individual unscheduled hospital readmissions. To do this, a retrospective, case-control study, based on information regarding patient readmission in 2018–2019, was conducted. After curation of the initial dataset (n = 76,210), the final number of participants was n = 29,026. A machine learning analysis was performed following several algorithms using unscheduled hospital readmissions as dependent variable. Local model-agnostic interpretability methods were also performed. We observed a 13% rate of unscheduled hospital readmissions cases. There were statistically significant differences regarding age and days of stay (p < 0.001 in both cases). A logistic regression model revealed chronic therapy (odds ratio: 3.75), diabetes mellitus history (odds ratio: 1.14), and days of stay (odds ratio: 1.02) as relevant factors. Machine learning algorithms yielded better results regarding sensitivity and other metrics. Following, this procedure, days of stay and age were the most important factors to predict unscheduled hospital readmissions. Interestingly, other variables like allergies and adverse drug reaction antecedents were relevant. Individualized prediction models also revealed a high sensitivity. In conclusion, our study identified significant factors influencing unscheduled hospital readmissions, emphasizing the impact of age and length of stay. We introduced a personalized risk model for predicting hospital readmissions with notable accuracy. Future research should include more clinical variables to refine this model further. Full article

(This article belongs to the Topic The Use of Big Data in Public Health Research and Practice)

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