Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.5
2.4
Journals
Healthcare
Special Issues
Application of Statistical Theory and Machine Learning in Health Services
share announcement

Application of Statistical Theory and Machine Learning in Health Services

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Health Informatics and Big Data".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 7506

Special Issue Editors

Dr. Sean Shao Wei Lam

E-Mail Website
Guest Editor
Health Services Research Centre, Duke-NUS Medical School, Singapore City 169857, Singapore
Interests: resolution of epidemiological; quality/service improvement and translational health services research problems; statistical theory and machine learning (reinforcement and supervised/unsupervised learning)
Dr. Kar Way Tan

E-Mail Website
Guest Editor
School of Computing and Information Systems, Singapore Management University, Singapore City 188065, Singapore
Interests: healthcare data science; data analytics; decision analytics; simulation; enhanced learning and pedagogy
Dr. Chun Fan Lee

E-Mail Website
Guest Editor
Centre for Quantitative Medicine, Duke-NUS Medical School, 169857 Singapore City, Singapore
Interests: clinical trials; patient-reported outcomes; health-related quality-of-life; epidemiology of infectious diseases

Special Issue Information

Dear Colleagues,

The digital revolution has brought about huge amounts of digitized information. Quantitative techniques founded upon rigorous statistical and mathematical theories have always been a key foundation in the advancement of health services. Statistical theory and modern machine learning are deeply intertwined. The potential for overcoming challenges confounding healthcare and health service delivery through these computational domains has been proven time and again across the entire value chain of health and healthcare. 

The Institute of Medicine (IOM) proposed the Quadruple Aims in looking at the complex challenges underpinning health services across the tradeoffs in cost effectiveness, population health, patient experience, and provider well-being. Healthcare being a complex human-centric service industry, there has always been an emphasis on a holistic understanding of the systems implications in any proposed new interventions. The voluminous and rapid generation of the data from digitized systems has introduced immense possibilities in the advancement of health services using statistical theory and machine learning. 

Health-related data could come from electronic health records (EHRs), procurement systems, social media, public health reports, non-profit and global organizations (e.g., WHO, UN), scientific publications, and insurance organizations, among others. Although the ubiquity of data is indisputable, the availability of quality data that contain the necessary depth and breadth of coverage to positively impact care is still frequently an issue for researchers and practitioners in the health services research domain. Data from different sources must usually be pooled together in a meaningful and rigorous way to generate insightful analysis and impactful results. Consequently, numerous data consortiums and ground-up multi-site research and data collaboratives have been established to realize the immense opportunity present in large-scale databases that cut across borders, peoples, and health systems. 

This Special Issue will cover topics that span the entire spectrum from data curation, extraction, wrangling, analysis, and insight generation to implementation and deployment of data-centric interventions or policy recommendations founded upon rigorous statistical theory and machine learning techniques. Research areas may include (but are not limited to) the following:

  • Data science and artificial intelligence in health services and medicine;
  • Statistical theory and applications in health services;
  • Applications of machine learning in healthcare;
  • Application of survival analysis and stochastic models in healthcare;
  • Statistical theory/machine learning for decision making in healthcare;
  • Statistical and machine learning innovations for clinical decision support systems;
  • Epidemiology and public health;
  • Medical informatics;
  • Value-based healthcare;
  • Data-driven innovations in cloud computing for health services;
  • Big data analytics for health services and population health;
  • Data-driven methods and models for health service improvement;
  • Security and privacy innovations for health data;
  • Innovations in federated learning for multi-site collaborations;
  • Predictive modeling and risk scores;
  • Innovations in data wrangling and feature engineering for big data modeling;
  • Multi-model data analytics for health services;
  • Patient safety and quality.

Dr. Sean Shao Wei Lam
Dr. Kar Way Tan
Dr. Chun Fan Lee
Guest Editors

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. Healthcare is an international peer-reviewed open access semimonthly 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 2700 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

  • data science
  • analytics
  • artificial intelligence
  • statistical learning
  • epidemiology
  • public health
  • population health
  • medical informatics
  • value-based healthcare
  • federated learning
  • statistical theory

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 9720 KiB  
Article
Enhancing Emergency Department Management: A Data-Driven Approach to Detect and Predict Surge Persistence
by Kang Heng Lim, Francis Ngoc Hoang Long Nguyen, Ronald Wen Li Cheong, Xaver Ghim Yong Tan, Yogeswary Pasupathy, Ser Chye Toh, Marcus Eng Hock Ong and Sean Shao Wei Lam
Healthcare 2024, 12(17), 1751; https://doi.org/10.3390/healthcare12171751 - 2 Sep 2024
Viewed by 1173
Abstract
The prediction of patient attendance in emergency departments (ED) is crucial for effective healthcare planning and resource allocation. This paper proposes an early warning system that can detect emerging trends in ED attendance, offering timely alerts for proactive operational planning. Over 13 years [...] Read more.
The prediction of patient attendance in emergency departments (ED) is crucial for effective healthcare planning and resource allocation. This paper proposes an early warning system that can detect emerging trends in ED attendance, offering timely alerts for proactive operational planning. Over 13 years of historical ED attendance data (from January 2010 till December 2022) with 1,700,887 data points were used to develop and validate: (1) a Seasonal Autoregressive Integrated Moving Average with eXogenous factors (SARIMAX) forecasting model; (2) an Exponentially Weighted Moving Average (EWMA) surge prediction model, and (3) a trend persistence prediction model. Drift detection was achieved with the EWMA control chart, and the slopes of a kernel-regressed ED attendance curve were used to train various machine learning (ML) models to predict trend persistence. The EWMA control chart effectively detected significant COVID-19 events in Singapore. The surge prediction model generated preemptive signals on changes in the trends of ED attendance over the COVID-19 pandemic period from January 2020 until December 2022. The persistence of novel trends was further estimated using the trend persistence model, with a mean absolute error of 7.54 (95% CI: 6.77–8.79) days. This study advanced emergency healthcare management by introducing a proactive surge detection framework, which is vital for bolstering the preparedness and agility of emergency departments amid unforeseen health crises. Full article
(This article belongs to the Special Issue Application of Statistical Theory and Machine Learning in Health Services)
►▼ Show Figures

Figure 1

27 pages, 2005 KiB  
Article
Vertebral Column Pathology Diagnosis Using Ensemble Strategies Based on Supervised Machine Learning Techniques
by Alam Gabriel Rojas-López, Alejandro Rodríguez-Molina, Abril Valeria Uriarte-Arcia and Miguel Gabriel Villarreal-Cervantes
Healthcare 2024, 12(13), 1324; https://doi.org/10.3390/healthcare12131324 - 2 Jul 2024
Viewed by 1133
Abstract
One expanding area of bioinformatics is medical diagnosis through the categorization of biomedical characteristics. Automatic medical strategies to boost the diagnostic through machine learning (ML) methods are challenging. They require a formal examination of their performance to identify the best conditions that enhance [...] Read more.
One expanding area of bioinformatics is medical diagnosis through the categorization of biomedical characteristics. Automatic medical strategies to boost the diagnostic through machine learning (ML) methods are challenging. They require a formal examination of their performance to identify the best conditions that enhance the ML method. This work proposes variants of the Voting and Stacking (VC and SC) ensemble strategies based on diverse auto-tuning supervised machine learning techniques to increase the efficacy of traditional baseline classifiers for the automatic diagnosis of vertebral column orthopedic illnesses. The ensemble strategies are created by first combining a complete set of auto-tuned baseline classifiers based on different processes, such as geometric, probabilistic, logic, and optimization. Next, the three most promising classifiers are selected among k-Nearest Neighbors (kNN), Naïve Bayes (NB), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Decision Tree (DT). The grid-search K-Fold cross-validation strategy is applied to auto-tune the baseline classifier hyperparameters. The performances of the proposed ensemble strategies are independently compared with the auto-tuned baseline classifiers. A concise analysis evaluates accuracy, precision, recall, F1-score, and ROC-ACU metrics. The analysis also examines the misclassified disease elements to find the most and least reliable classifiers for this specific medical problem. The results show that the VC ensemble strategy provides an improvement comparable to that of the best baseline classifier (the kNN). Meanwhile, when all baseline classifiers are included in the SC ensemble, this strategy surpasses 95% in all the evaluated metrics, standing out as the most suitable option for classifying vertebral column diseases. Full article
(This article belongs to the Special Issue Application of Statistical Theory and Machine Learning in Health Services)
►▼ Show Figures

Figure 1

24 pages, 5139 KiB  
Article
Machine Learning Techniques to Predict Timeliness of Care among Lung Cancer Patients
by Arul Earnest, Getayeneh Antehunegn Tesema and Robert G. Stirling
Healthcare 2023, 11(20), 2756; https://doi.org/10.3390/healthcare11202756 - 18 Oct 2023
Cited by 1 | Viewed by 2362
Abstract
Delays in the assessment, management, and treatment of lung cancer patients may adversely impact prognosis and survival. This study is the first to use machine learning techniques to predict the quality and timeliness of care among lung cancer patients, utilising data from the [...] Read more.
Delays in the assessment, management, and treatment of lung cancer patients may adversely impact prognosis and survival. This study is the first to use machine learning techniques to predict the quality and timeliness of care among lung cancer patients, utilising data from the Victorian Lung Cancer Registry (VLCR) between 2011 and 2022, in Victoria, Australia. Predictor variables included demographic, clinical, hospital, and geographical socio-economic indices. Machine learning methods such as random forests, k-nearest neighbour, neural networks, and support vector machines were implemented and evaluated using 20% out-of-sample cross validations via the area under the curve (AUC). Optimal model parameters were selected based on 10-fold cross validation. There were 11,602 patients included in the analysis. Evaluated quality indicators included, primarily, overall proportion achieving “time from referral date to diagnosis date ≤ 28 days” and proportion achieving “time from diagnosis date to first treatment date (any intent) ≤ 14 days”. Results showed that the support vector machine learning methods performed well, followed by nearest neighbour, based on out-of-sample AUCs of 0.89 (in-sample = 0.99) and 0.85 (in-sample = 0.99) for the first indicator, respectively. These models can be implemented in the registry databases to help healthcare workers identify patients who may not meet these indicators prospectively and enable timely interventions. Full article
(This article belongs to the Special Issue Application of Statistical Theory and Machine Learning in Health Services)
►▼ Show Figures

Figure 1

20 pages, 800 KiB  
Article
Managing Mortality and Aging Risks with a Time-Varying Lee–Carter Model
by Zhongwen Chen, Yanlin Shi and Ao Shu
Healthcare 2023, 11(5), 743; https://doi.org/10.3390/healthcare11050743 - 3 Mar 2023
Cited by 1 | Viewed by 1851
Abstract
Influential existing research has suggested that rather than being static, mortality declines decelerate at young ages and accelerate at old ages. Without accounting for this feature, the forecast mortality rates of the popular Lee–Carter (LC) model are less reliable in the long run. [...] Read more.
Influential existing research has suggested that rather than being static, mortality declines decelerate at young ages and accelerate at old ages. Without accounting for this feature, the forecast mortality rates of the popular Lee–Carter (LC) model are less reliable in the long run. To provide more accurate mortality forecasting, we introduce a time-varying coefficients extension of the LC model by adopting the effective kernel methods. With two frequently used kernel functions, Epanechnikov (LC-E) and Gaussian (LC-G), we demonstrate that the proposed extension is easy to implement, incorporates the rotating patterns of mortality decline and is straightforwardly extensible to multi-population cases. Using a large sample of 15 countries over 1950–2019, we show that LC-E and LC-G, as well as their multi-population counterparts, can consistently improve the forecasting accuracy of the competing LC and Li–Lee models in both single- and multi-population scenarios. Full article
(This article belongs to the Special Issue Application of Statistical Theory and Machine Learning in Health Services)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop