van der Schaar Lab
Hub for Healthcare

Hub for Healthcare

The Hub for Healthcare matches medical problems to canonical formalisms and solutions via AI, machine learning, and operations research

Healthcare abounds with complex and difficult problems. Many of these are apparent to the clinician in the challenges they face every day in the diagnosis, treatment and management of patients, and are often defined descriptively in terms that are suitable for the profession. Our lab’s objective is to work closely with clinicians to understand these problems and create canonical formalisms from which AI, machine learning and operations research methods can provide rigorous solutions.

This page, known as the Hub for Healthcare, contains a classification of some medical problems and associated examples, and then provides formalisms and methods by which they can be solved. Many medical problems that are traditionally described in terms of clinical domain or disease can thus be reduced to a few common, canonical formalisms and, through these formalisms, solved. The intent here is to help move medicine from art to science.

The Hub for Healthcare is intended as a support resource for the clinical, AI, machine learning and operations research communities to help many of us who struggle to convert the narratives of medicine into strict formalisms that can be solved.

The table below consists of medical problems submitted by clinicians and organized by clinical domain and clinical problem type. For each of these problems, we show the  canonical formalism(s) through which these problems should be viewed and solutions using AI, machine learning and operations research that use these formalisms.

In this short video, Mihaela van der Schaar explains what we mean by “formalisms,” and why they’re an essential part of the process of solving healthcare problems through the use of AI, machine learning, and operations research.

Using the Hub for Healthcare

The Hub for Healthcare maps problems or questions raised by clinicians to the formalisms that can be used to understand and define these problems, and then to machine learning solutions. These are color-coded, allowing users to see the parts of questions that correspond to specific formalisms.

The table below is fully searchable, filterable, and sortable: you can either browse it, or—if you already know what you’re looking for—use specific keywords and phrases to find problems and discussions related to diseases, medical scenarios, or even by machine learning sub-fields. To search for a particular phrase, use the “Search” box directly above the table.

In this short video, Nick Maxfield explains how the Hub for Healthcare ties real-world medical problems and questions explicitly to formalisms, and then to solutions based on AI, machine learning, and operations research.

As of December 2020, the Hub contains problems, formalisms, and solutions primarily related to the acute care medical domain. As this project progresses (in tandem with our lab’s engagement sessions for the clinical community), the Hub will expand to cover medical domains including cancer, cardiovascular disease, respiratory disease, organ transplantation, and many more.

Note: many of the machine learning algorithms referenced in this table are available as software packages that can be found in our BitBucket repository.

Our Clinical Advisory Group

Machine learning for medicine is an inherently interdisciplinary area, and cannot be successfully approached unilaterally from the perspective of machine learning.

Our lab has been fortunate enough to benefit from expert guidance and collaborative support from an international network of doctors who are leaders in their respective domains of specialization. Some of our collaborators have kindly agreed to serve as members of a Clinical Advisory Group for the Hub for Healthcare.

Each Clinical Advisory Group member helps us to define the scope and boundaries of their area of specialization, and advises us regarding the problems encountered by clinicians working in their domain.

– Affiliations are accurate at the time of writing, but may change; some individuals may have multiple affiliations that are not listed.
– This list generally follows the title/suffix/prefix conventions used by each individual’s affiliated organization, although the “MD” suffix has been homogenized to the “Dr.” prefix where it occurs.

Clinical Advisory Group

Dr. Alexander Gimson

Consultant transplant hepatologist, Cambridge University Hospitals NHS Foundation Trust

Dr. Eoin McKinney

University lecturer in renal medicine, University of Cambridge; Honorary consultant in nephrology and transplantation, Cambridge University Hospitals NHS Foundation Trust

Dr. Maxime Cannesson

Ronald L. Katz, MD, Chair of the UCLA Department of Anesthesiology and Perioperative Medicine

Dr. Paul Elbers

Intensivist at VU medical center, Amsterda; Organizer, Amsterdam Medical Data Science Group

Dr. Ari Ercole

Consultant in anesthesia & intensive care; deputy chief clinical informatics officer, Cambridge University Hospitals NHS Foundation Trust

Dr. Brent Ershoff

Assistant Professor-In-Residence, Department of Anesthesiology
David Geffen School of Medicine, University of California, Los Angeles, USA

Cardiovascular Disease Clinical Advisory Group

Cancer Clinical Advisory Group