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 3957
Special Issue Editors
Interests: resolution of epidemiological; quality/service improvement and translational health services research problems; statistical theory and machine learning (reinforcement and supervised/unsupervised learning)
Interests: healthcare data science; data analytics; decision analytics; simulation; enhanced learning and pedagogy
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
