Call for Papers

Machine Learning Driven Decision Systems in Biomedicine

Guest Editors:

Enrico Capobianco, PhD

University of Miami

Miami, FL


Tavpritesh Sethi, MBBS, PhD

Indraprastha Institute of Information Technology

Delhi, India

Visiting Assistant Professor

Biomedical Informatics Research

Stanford University

Manuscript Submission Deadline: November 15, 2018

Machine learning is being increasingly applied to complex medical and biological problems. The unprecedented volume of biomedical data has created an opportunity for better insights, predictions and decisions at the bedside and in the population. However, this opportunity has still remained largely untranslated. This Special Issue on Machine Learning Driven Decision Systems in Biomedicine invites high-quality research on integrating data, methods and systems for better diagnostic, prognostic and therapeutic decisions using machine learning. The issue is posited to address the dual need for new-development and value-generation using machine learning in healthcare scenarios.

Submissions to this Special Issue of Systems Medicine should highlight research at the interface of machine learning and clinical, molecular or policy levels. Broad themes include clinically meaningful models, novel methods, policy insights, educational strategies and tools that accelerate clinical adoption of machine learning. Submissions catering to these broad themes will be considered from researchers across the spectrum of Academia, Government and Industry, such as:

  • Researchers/clinicians working at the interface of medicine and machine learning/artificial intelligence
  • Researchers/clinicians working at the interface of Omics and machine learning/artificial intelligence
  • Engaged with big (and small) data analytics with demonstration of value
  • Integrating heterogeneous and noisy biomedical data for meaningful insights and decisions
  • Population health researchers using Machine learning/AI for policy decisions
  • Academic/industry professionals creating usable tools and interfaces for facilitating adoption of machine learning in Medicine and Biology
  • Decision scientists working on healthcare economics support models
  • Machine learning researchers collaborating with Clinicians, Educators and Policy scientists at the healthcare interface

Systems Medicine will consider articles from the full breadth of evidence: from case reports and case series to original research papers and reviews.

Areas of interest include, but are not limited to:

  • Supervised, unsupervised and reinforcement learning applications to healthcare
  • Individual level predictions.
  • Population level predictions and policy
  • Longitudinal predictions using machine learning
  • Interpretable models
  • Causal and generative models from observational data
  • Comparison and evaluation of machine learning methods with standard statistical approaches
  • Interactive interfaces for biomedical informatics, clinicians and biologists
  • Novel methods and algorithms
  • Multi-type Networks for multi-omics applications
  • Active Learning in real-time assessment scenarios
  • Population Health Screening
  • Risk scoring and calibration
  • Diagnostic innovation
  • Therapy combination strategies
  • Drug repurposing

Contributions will receive prompt and thorough peer review. Please refer to our Instructions for Authors before submitting your manuscript for consideration. Authors are encouraged to vet ideas directly with Professors Capobianco and Sethi via e-mail before submitting.

Systems Medicine is the premier open access, peer reviewed journal focused on interdisciplinary approaches to exploiting the power of big data by applying systems biology and network medicine. Systems Medicine yields major breakthroughs towards mechanism-based re-definitions of diseases for high-precision diagnostics and treatments.

Manuscript Submission Deadline: November 15, 2018

Learn More about this journal

Deadline for Manuscript Submission:
November 15, 2018


Editorial Questions?

Contact Dr. Enrico Capobianco (

and Dr. Tavpritesh Sethi (,