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

arXiv 2207.14638 ↗First posted 16 Jul 2022cs.DB cs.LG stat.APcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 29 references that are in the map. First posted on arXiv 16 Jul 2022.

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2 of 29 references.

References · 2

  1. 2021Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Datacited by 28
  2. 2020Remote atrial fibrillation burden estimation using deep recurrent neural networkcited by 5

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