International Journal in Medical Informatics Special Issue: Collaborative Data Science in Healthcare

Guest Editors:

Leo Anthony Celi MD MS MPH

Massachusetts Institute of Technology

Cambridge, MA, USA

Rodrigo Deliberato MD PhD

Hospital Israelita Albert Einstein

Sao Paolo, Brazil

Susana Vieira PhD

University of Lisbon

Lisbon, Portugal


While clinical trials are best in inferring causality, they are not adept at demonstrating small effect size across a population, which is typical given heterogeneity of treatment effect. Moreover, clinical trials typically exclude important subgroups (older patients, those with comorbidities): findings may not be generalizable to the real-world. Because of the limitations of clinical trials including cost, many practice guidelines are supported by low-quality evidence. To make matters worse, these guidelines are often adopted in countries where funding for research is limited.

Digitalization of healthcare data may provide an opportunity to develop locally relevant practice guidelines rather than adopting those from other countries. Digital data is proliferating in diverse forms within the healthcare field, not only because of the adoption of electronic health records, but also because of the growing use of wireless technologies for ambulatory monitoring. Since clinical trials may be too expensive to perform in most countries, digital health data provides an opportunity to conduct locally relevant research. Rigorous observational studies have been shown to correlate well with clinical trials across the medical literature in terms of estimates of risk and effect size.

The world is abuzz with applications of machine learning in almost every field – commerce, transportation, banking, and more recently, healthcare. These breakthroughs are due to rediscovered algorithms, powerful computers to run them, and most importantly, the availability of bigger and better data to train the algorithms.

This special issue, entitled Collaborative Data Science in Healthcare, will focus on secondary analysis of clinical data that is routinely collected in the process of care. We would like to highlight the following key messages in the special issue:

  1. Leveraging advances in machine learning in healthcare requires collaboration between clinicians and data scientists.
  2. High-resolution clinical data offers a distinct advantage over typical health registry data or quality reports for machine learning.
  3. As the number of candidate features expands, the size of the dataset needs to be an order of magnitude larger to avoid model over-fitting. This is the impetus behind the call for data sharing, or more aptly, data merging, and the creation of federated clinical databases.

We invite the research community to submit original research papers in the following topics:

  1. Building of federated clinical databases
  2. New research models in health data science
  3. Educational curricula that promote multi-disciplinary approach to machine learning in healthcare
  4. Linking of data captured by wireless technologies with health outcomes data
  5. Linking of publicly available datasets (e.g. environmental, municipal) with patient-level clinical data
  6. Implementation of decision support tools developed using real-world patient data

International Journal of Medical Informatics provides an international medium for dissemination of original results and interpretative reviews concerning the field of medical informatics. The Journal emphasizes the evaluation of systems in healthcare settings. The guide for authors, which has author guidelines for this journal could be found here:

When the manuscript is ready, please click the submission portal:

and choose the title of the special issue as SI: DataScience_Healthcare

Important Dates:

Paper submission deadline

1 October 2017

Revision submission deadline

1 February 2018


1 April 2018

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