Building Trust: Lessons from the Technion-Rambam Machine Learning in Healthcare Datathon Event

TL;DR

This paper reflects on a healthcare datathon involving interdisciplinary collaboration, highlighting opportunities, limitations, and training needs in medical data science within Israel.

Contribution

It provides insights into the outcomes of a healthcare datathon, emphasizing interdisciplinary collaboration and identifying training needs in Israeli medical data science.

Findings

Participants identified key skill gaps in medical machine learning.

The event fostered collaboration between engineers, biologists, and physicians.

Opportunities for advancing medical data analysis in Israel were highlighted.

Abstract

A datathon is a time-constrained competition involving data science applied to a specific problem. In the past decade, datathons have been shown to be a valuable bridge between fields and expertise . Biomedical data analysis represents a challenging area requiring collaboration between engineers, biologists and physicians to gain a better understanding of patient physiology and of guide decision processes for diagnosis, prognosis and therapeutic interventions to improve care practice. Here, we reflect on the outcomes of an event that we organized in Israel at the end of March 2022 between the MIT Critical Data group, Rambam Health Care Campus (Rambam) and the Technion Israel Institute of Technology (Technion) in Haifa. Participants were asked to complete a survey about their skills and interests, which enabled us to identify current needs in machine learning training for medical problem applications. This work describes opportunities and limitations in medical data science in the Israeli context.