News

• Accepted paper list published. See Papers.
  Program announced - KDD registration required to access the platform.
• Paper submission deadline extended to Jun 01, 2021. See updated dates
• KDD Health Day and DSHealth will be held jointly this year
• Selected papers will be invited to publish in a special issue of Artificial Intelligence in Medicine journal. Please see updated CFP
• Invited Speaker lineup published.

Overview

Healthcare is, traditionally, a knowledge-driven enterprise with an enormous amount of data - both structured and unstructured. These data can impact positively on the development of data-driven health care including precision medicine and precision public health. In recent years, large scale medical/clinical datasets, such as “omics” data and radiology reports are increasingly available. We have also witnessed an increasing number of successful AI/ML applications using such datasets to address problems such as drug repurposing and preliminary screening of radiology reports. To facilitate the adoption of such AI/ML in practice, we have simultaneously witnessed an increasing adoption/innovation of using explainability methods to analyze/present AI for Health. In this deep learning era, What is the current status of AI/ML applications in healthcare? What are the standard methods of explaining such AI models for healthcare? What are the roles of causality in AI/ML practices? What are the state-of-the-art developments in causal AI in health and health care domains? What are the limitations and how are the different facets of trust and explanations (see figure 1 below) being addressed in practice? Can knowledge-backed AI lead to more robust and interpretable models? How do data scientists and physicians apply this knowledge in collaboration and via human-centered AI approaches to further the field and improve healthcare? How are regulatory requirements for transparency and trustworthiness of models and data privacy being defined and how can they be fulfilled? After witnessing so many great achievements from deep learning lately, we propose to invite world-leading experts from both data science and healthcare to discuss and debate the path forward for practical applications of AI/ML in healthcare, including demos, early work, and critiques on the current state and the path forward for explainability and trustworthiness in AI. More specifically, we plan to attract high-quality original research from emerging areas with significant implications in healthcare and invite open discussions on controversial yet crucial topics regarding healthcare transformation

Previous Iterations

• DSHealth 2020: 2020 KDD Workshop on Applied Data Science for Healthcare: Trustable and Actionable AI for Healthcare
• DSHealth 2019: 2019 KDD Workshop on Applied Data Science for Healthcare: Bridging the Gap between Data and Knowledge
• MLMH 2018: 2018 KDD Workshop on Machine Learning for Medicine and Healthcare

Invited Speakers

Mihaela Van Der Schaar
John Humphrey Plummer Professor of Machine Learning, AI, and Medicine at University of Cambridge

Alexej Gossmann
Staff Fellow (Mathematical Statistician) at FDA

Fernando Schwartz
Global Head of Data Science, Merck

Leo Celi
Principal Research Scientist, MIT; Co-Director, MIT Sana; Staff Physician, Beth Israel Deaconess Medical Center; Assoc. Prof, Harvard Medical School

Gunther Jansen
Head of PHC analytics, Roche

William Kassler
CMO, Palantir Technology Inc.

Gretchen Purcell Jackson
Vice President and Chief Science Officer, IBM, Watson Health

Dave DeCaprio
CTO and Co-founder at ClosedLoop.ai