Machine Learning for Healthcare Analytics
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (30 September 2025) | Viewed by 2719
Special Issue Editors
Interests: patient pathways; hospital organizational (re)engineering; modeling; simulation; digital twin
Special Issue Information
Dear Colleagues,
This special issue focuses on Machine Learning for Healthcare Analytics (ML4HA) which aims at leveraging knowledge hidden and therefore embedded within healthcare data thanks to algorithm and machine learning toolsets. Healthcare analytics rely on enabling insight and hindsight based on complex data processing using usual analytics toolboxes, techniques and knowledge. Healthcare data are complex because they are siloed, unstructured, and variable, raising concerns about strong regulatory laws, privacy, and ethics. As a subset of artificial intelligence, machine learning is a key player tool to extract meaningful patterns, classifiers, predictions, and knowledge from healthcare data. Data can be structured or unstructured, provided by electronic medical records, sensors, biometrics, social media, etc.
We can consider 3 main purposes of ML4HA divided into 4 analytics categories:
- Analyzing historical data and extracting hindsight about the past:
- Descriptive analytics: what happened?
- Diagnostic analytics: why did it happen?
- Using the findings of descriptive and diagnostic analytics and giving the foresight for the future:
- Predictive analytics: what will happen?
- Using the findings of descriptive and diagnostic analytics and giving the prescription for the future to eliminate a problem or take advantage of a promising trend:
- Prescriptive analytics: what to do?
You are kindly invited to submit papers that match all these purposes and categories related to your actual research topics. Experimental studies are expected, as well as theoretical concepts, comprehensive reviews and survey papers. Analytics of clinical cases are welcome. A special interest will be given to analytics from and for the area of healthcare administration cases like patient pathways management (patient arrivals, length of stay, readmission, surgery durations, etc.) or hospital logistics (medication-use process, reprocessing of reusable medical devices, etc.).
Dr. Franck Fontanili
Dr. Xavier Alacoque
Guest Editors
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Keywords
- healthcare IoT
- healthcare data and process mining
- electronic health records (EHR)
- natural language processing (NLP) in healthcare
- disease diagnosis
- medical imaging analysis
- patient outcome prediction
- genomic data analysis
- time series analysis in medicine
- healthcare recommender systems
- patterns identification
- risk score calculation
- prediction
- classification
- decision support system
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