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Frontiers of Digital Health: Computerised Decision Support in Cancer Multid...

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Room G01

The Farr Institute

UCL

London

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Computerised Decision Support in Cancer Multidisciplinary Meetings: Principles and Practice

I will describe an AI argumentation-based decision support system for breast cancer multidisciplinary meeting (MDM) which was successfully trialled at the Royal Free Hospital, Breast Unit. The system models the patient care pathway, automatically aggregating 16 best practice guidelines, and using routinely collected data recommends individualised diagnostic and treatment actions. The system was evaluated in a real setting and demonstrated excellent agreement with the MDM team decisions. One of the system's most important strengths is the ability to provide real-time audit. I will explain the decision-making modelling approach and I will discuss a range of strengths and weaknesses of the system, from knowledge representation to clinical service delivery. I will end the presentation discussing how to build on this research to learn clinical pathways from EHR data and perform automatic computation of individualised care plans using AI decision-theoretic planning.

Speaker: Dionisio Acosta, CHIME, UCL Institute of Health Informatics

Bio
Dionisio Acosta is a Senior Research Associate in Health Informatics and Director of the Graduate Programme in Health Informatics at CHIME, Institute of Health Informatics,
University College London (UCL). He has published on the design, implementation and evaluation of clinical decision support systems for breast cancer multi-disciplinary meetings and for brain tumour diagnosis using magnetic resonance imaging and spectroscopy, using AI argumentation, statistical and machine learning approaches. His most recent research endeavour was the EU IMI EHR4CR project (http://www.ehr4cr.eu), that aims to develop adaptable, reusable and scalable solutions (tools and services) for reusing data from Electronic Health Record systems for Clinical Research. He lectures on Clinical Decision Support Systems, Electronic Health Records and Machine Learning in Healthcare.


If you have any questions please contact Dr Delmiro Fernandez-Reyes (delmiro.fernandez-reyes@ucl.ac.uk)

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Room G01

The Farr Institute

UCL

London

United Kingdom

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