Machine Learning for Healthcare Analytics

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

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. Applied Sciences 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 2400 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.

• 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