van der Schaar Lab

A Foundational Framework for Personalized Early and Timely Diagnosis

Early diagnosis and treatment promise deep transformation in healthcare by enabling better treatment options, improving long-term survival and quality of life, and reducing overall cost. However, ED&D research often focuses on novel technologies or risk stratification while neglecting the potential for optimising individual patients’ diagnostic paths by improving medical decision-making. To enable personalised early and timely diagnosis, a foundational framework is needed that delineates the diagnosis process and systematically identifies the time-dependent value of various diagnostic decisions for an individual patient given their unique characteristics and history.

In a recent pre-print, we propose a foundational framework for early and timely diagnosis. The framework is grounded in decision-theoretic approaches and integrates machine learning and statistical methodology to estimate the optimal personalised diagnostic path. 

This framework provides:

– formalism and thus clarity for the development of decision support tools

– a holistic view on complementing observed patient information from present and past with estimates of the patient’s future trajectory

– a rationale to estimate the net benefit of counterfactual diagnostic paths and the associated uncertainties

– fundamental definitions for describing this and other frameworks, including a clear differentiation between “early” and “timely” diagnosis

– a systematic mechanism for assessing the value of technologies, including new and existing diagnostic tests in terms of their impact on personalised early diagnosis, resulting health outcomes and incurred costs for individuals, populations and healthcare systems.

This work informs early diagnosis research on all 4 levels of our framework defining the impact of ML on healthcare.

Mihaela van der Schaar

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute in London.

Mihaela has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise span signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI.

Mihaela’s research focus is on machine learning, AI and operations research for healthcare and medicine.