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.
Why do we need 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.
Hub for Healthcare video tutorial
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 clinicical community), the Hub will expand to cover medical domains including cancer, cardiovascular disease, respiratory disease, organ transplantation, and many more.
|Medical domain(s)||Problem type(s)||ML for healthcare framework||Canonical formalism(s)|
|Sort||wdt_ID||Medical domain(s)||Problem type(s)||Raised by||Description of problem||ML for healthcare framework||Canonical formalism(s)||Solution(s)|
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 Groups
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 Clinical Advisory Groups for medical subdomains covered in the Hub for Healthcare.
Each Clinical Advisory Group member helps us to define the scope and boundaries of their subdomain (as well as medical problem types within that domain), and advises us regarding the problems encountered by clinicians working in their domain.
Acute Care Clinical Advisory Group
Dr. Maxime Cannesson
Chair, UCLA Department of Anesthesiology & Perioperative Medicine
University of California, Los Angeles, USA
Dr. Maxime Cannesson is the Ronald L. Katz, MD, Chair of the UCLA Department of Anesthesiology and Perioperative Medicine. His department engages in research, teaching, and patient care activities across Los Angeles.
Sites include Ronald Reagan UCLA Medical Center in Westwood, UCLA Medical Center in Santa Monica, the Resnick Neuropsychiatric Hospital, Mattel Children’s Hospital, and the UCLA Medical Group with its extensive system of primary-care and specialty-care offices throughout the region. Consistently ranked one of the top ten hospitals in the nation – and the best in Los Angeles, UCLA is at the leading edge of medical care and biomedical research.
Dr. Cannesson’s department currently has four main research themes, each of which is supported by a strong foundation of basic and clinical research infrastructure: cardiovascular research; neuroscience, mechanism of anesthesia and pain; organ-protection research; and biocomputing and health informatics.
Dr. Paul Elbers
Intensivist at Amsterdam UMC, the Netherlands
Dr. Paul WG Elbers is an intensivist at VU medical center, Amsterdam, The Netherlands.
Special interests include quantitative acid base analysis, fluid therapy, ultrasound, pharmacokinetics, medical information technology, and human microcirculation.
The latter was the topic of his PhD thesis entitled “Focus on Flow, Imaging the Human Microcirculation in Periopera-tive and Intensive Care Medicine”.
Together with Prof. John A Kellum he edited the second edition of “Stewart’s Textbook of Acid-Base”. Together with Rainer Gatz, MD, he is the editor of www.acidbase.org, an online community for quantitative acid base analysis including clinical decision support tools.
Paul is one of the organizers of the Amsterdam Medical Data Science Group, which plays a leading role in trying to solve the ethical and moral dilemma of data sharing by providing guidance for European Intensive Care Units to share their data in a responsible way. This work is being done with the European Society of Intensive Care Medicine.
Dr. Ari Ercole
Consultant in Neurointensive Care,
Cambridge University Hospitals NHS Foundation Trust, UK
Dr. Ari Ercole became an anaesthetist after completing a PhD in physics at the University of Cambridge. He divides his time between intensive care, anaesthesia and research.
His particular focus on data-driven research: Developing novel analytical techniques including machine learning and feature discovery for intensive-, acute- and perioperative care.
Dr. Ercole has an extensive background in physical and statistical modelling as well as data science and computing. His research involves development of novel medical technology (in particular data analysis techniques but also sensor development and physiological measurement) and its application to the treatment of critically ill patients in particular. He is interested in physiological signal complexity (fractal dynamics, signal entropy) as an emergent behaviour of networks of non-linear dynamical systems or techniques from machine learning and time-series statistics to neurophysiological and clinical data to detect and characterise novel multidimensional interrelationships.
Dr. Brent Ershoff
Assistant Professor-In-Residence, Department of Anesthesiology
David Geffen School of Medicine, University of California, Los Angeles, USA
Following a BS and MS in Biological Sciences, both at Stanford University, Dr. Ershoff received his MD from University of California, San Francisco. This was followed by an anasthesia residency at UCLA Health.
Dr. Ershoff completed a liver transplant anesthesiology fellowship at UCLA Health in 2015, and is now Assistant Professor-In-Residence at UCLA’s David Geffen School of Medicine.