Submit to IJERPH Review for IJERPH Propose a Special Issue

Journal Menu

Journal Browser

New Insights from Big Data and Advanced Analytics in Health Care

Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Public Health Statistics and Risk Assessment".

Deadline for manuscript submissions: closed (28 March 2023) | Viewed by 12908

Share This Special Issue

Special Issue Editor

Dr. Mandana Rezaeiahari

E-Mail Website
Guest Editor

Department of Health Policy and Management, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
Interests: similarity analytics; predictive analytics; prescriptive analytics; clinical pathways; pathway visualization; treatment pathways; feature engineering; algorithm fairness; temporal event mining; health equity; deep learning; machine learning; artificial intelligence; bias

Special Issue Information

Dear Colleagues,

This issue calls for state-of-the-art advanced analytical methods, including explanatory, causal inference, similarity, predictive and prescriptive analytics with various applications in population health, clinical pathways, and optimization of healthcare delivery processes. Of special interest is the intersection of health equity and advance analytics through the leveraging of socioeconomic and demographic characteristics and modeling the interactions of social determinants of health with disease risk, progression, and clinical outcomes. Bias exists in a broad aspect of data generation and technology development in healthcare. Data bias may exist due to incomplete health data, such as missing data. Machine learning models developed without properly designed methods to handle bias may lead to exacerbations of health disparities. To reduce the potential harm caused by unfair machine learning models, algorithm fairness has become incredibly important. Modeling methodologies aimed at creating fair algorithm are needed for active prepromotion of health equity. Health equity and algorithm fairness, AI ethics, and the use of machine learning to model social determinants of health and health outcomes are topics of interest.

Dr. Mandana Rezaeiahari
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

similarity analytics
predictive analytics
prescriptive analytics
clinical pathways
pathway visualization
treatment pathways
feature engineering
algorithm fairness
temporal event mining
health equity
deep learning
machine learning
artificial intelligence
bias

Published Papers (3 papers)

Download All Papers

Research

Jump to: Review

18 pages, 5025 KiB

Open AccessArticle

An Integrated System of Braden Scale and Random Forest Using Real-Time Diagnoses to Predict When Hospital-Acquired Pressure Injuries (Bedsores) Occur

by Odai Y. Dweekat, Sarah S. Lam and Lindsay McGrath

Int. J. Environ. Res. Public Health 2023, 20(6), 4911; https://doi.org/10.3390/ijerph20064911 - 10 Mar 2023

Cited by 3 | Viewed by 3849

Abstract

Background and Objectives: Bedsores/Pressure Injuries (PIs) are the second most common diagnosis in healthcare system billing records in the United States and account for 60,000 deaths annually. Hospital-Acquired Pressure Injuries (HAPIs) are one classification of PIs and indicate injuries that occurred while the patient was cared for within the hospital. Until now, all studies have predicted who will develop HAPI using classic machine algorithms, which provides incomplete information for the clinical team. Knowing who will develop HAPI does not help differentiate at which point those predicted patients will develop HAPIs; no studies have investigated when HAPI develops for predicted at-risk patients. This research aims to develop a hybrid system of Random Forest (RF) and Braden Scale to predict HAPI time by considering the changes in patients’ diagnoses from admission until HAPI occurrence. Methods: Real-time diagnoses and risk factors were collected daily for 485 patients from admission until HAPI occurrence, which resulted in 4619 records. Then for each record, HAPI time was calculated from the day of diagnosis until HAPI occurrence. Recursive Feature Elimination (RFE) selected the best factors among the 60 factors. The dataset was separated into 80% training (10-fold cross-validation) and 20% testing. Grid Search (GS) with RF (GS-RF) was adopted to predict HAPI time using collected risk factors, including Braden Scale. Then, the proposed model was compared with the seven most common algorithms used to predict HAPI; each was replicated for 50 different experiments. Results: GS-RF achieved the best Area Under the Curve (AUC) (91.20 ± 0.26) and Geometric Mean (G-mean) (91.17 ± 0.26) compared to the seven algorithms. RFE selected 43 factors. The most dominant interactable risk factors in predicting HAPI time were visiting ICU during hospitalization, Braden subscales, BMI, Stimuli Anesthesia, patient refusal to change position, and another lab diagnosis. Conclusion: Identifying when the patient is likely to develop HAPI can target early intervention when it is needed most and reduces unnecessary burden on patients and care teams when patients are at lower risk, which further individualizes the plan of care. Full article

(This article belongs to the Special Issue New Insights from Big Data and Advanced Analytics in Health Care)

► Show Figures

Figure 1

19 pages, 4090 KiB

Open AccessArticle

An Integrated System of Multifaceted Machine Learning Models to Predict If and When Hospital-Acquired Pressure Injuries (Bedsores) Occur

by Odai Y. Dweekat, Sarah S. Lam and Lindsay McGrath

Int. J. Environ. Res. Public Health 2023, 20(1), 828; https://doi.org/10.3390/ijerph20010828 - 1 Jan 2023

Cited by 8 | Viewed by 2789

Abstract

Hospital-Acquired Pressure Injury (HAPI), known as bedsore or decubitus ulcer, is one of the most common health conditions in the United States. Machine learning has been used to predict HAPI. This is insufficient information for the clinical team because knowing who would develop HAPI in the future does not help differentiate the severity of those predicted cases. This research develops an integrated system of multifaceted machine learning models to predict if and when HAPI occurs. Phase 1 integrates Genetic Algorithm with Cost-Sensitive Support Vector Machine (GA-CS-SVM) to handle the high imbalance HAPI dataset to predict if patients will develop HAPI. Phase 2 adopts Grid Search with SVM (GS-SVM) to predict when HAPI will occur for at-risk patients. This helps to prioritize who is at the highest risk and when that risk will be highest. The performance of the developed models is compared with state-of-the-art models in the literature. GA-CS-SVM achieved the best Area Under the Curve (AUC) (75.79 ± 0.58) and G-mean (75.73 ± 0.59), while GS-SVM achieved the best AUC (75.06) and G-mean (75.06). The research outcomes will help prioritize at-risk patients, allocate targeted resources and aid with better medical staff planning to provide intervention to those patients. Full article

(This article belongs to the Special Issue New Insights from Big Data and Advanced Analytics in Health Care)

► Show Figures

Figure 1

Review

Jump to: Research

46 pages, 7763 KiB

Open AccessReview

Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review

by Odai Y. Dweekat, Sarah S. Lam and Lindsay McGrath

Int. J. Environ. Res. Public Health 2023, 20(1), 796; https://doi.org/10.3390/ijerph20010796 - 1 Jan 2023

Cited by 12 | Viewed by 5914

Abstract

Pressure Injuries (PI) are one of the most common health conditions in the United States. Most acute or long-term care patients are at risk of developing PI. Machine Learning (ML) has been utilized to manage patients with PI, in which one systematic review describes how ML is used in PI management in 32 studies. This research, different from the previous systematic review, summarizes the previous contributions of ML in PI from January 2007 to July 2022, categorizes the studies according to medical specialties, analyzes gaps, and identifies opportunities for future research directions. PRISMA guidelines were adopted using the four most common databases (PubMed, Web of Science, Scopus, and Science Direct) and other resources, which result in 90 eligible studies. The reviewed articles are divided into three categories based on PI time of occurrence: before occurrence (48%); at time of occurrence (16%); and after occurrence (36%). Each category is further broken down into sub-fields based on medical specialties, which result in sixteen specialties. Each specialty is analyzed in terms of methods, inputs, and outputs. The most relevant and potentially useful applications and methods in PI management are outlined and discussed. This includes deep learning techniques and hybrid models, integration of existing risk assessment tools with ML that leads to a partnership between provider assessment and patients’ Electronic Health Records (EHR). Full article

(This article belongs to the Special Issue New Insights from Big Data and Advanced Analytics in Health Care)

► Show Figures