LIDA Seminar Series 29th November 2018

The Leeds Institute for Data Analytics is pleased to present the next seminar in our series showcasing data analytics.

The seminar will be held in 9.87, Worsley Building.

Abstract

In many real-world data-driven problems, the evidence to support decisions is gathered sequentially and not all measurements are available immediately. For instance, in medical diagnosis, a clinician may order a series of tests and, based on their outcomes, order further tests to determine the disease state of a patient. Each patient disease classification is therefore associated with a "diagnostic trajectory" charting the series of measurements that were recorded to reach their diagnostic conclusion. Whilst much focus in recent developments in medical artificial intelligence have focused on predictive modelling and automation of decision making processes, in the context of complete data, there has been little attention paid to the sequential data acquisition processes that operate in reality. In this talk, I will describe a novel and generic Bayesian optimisation approach that we have developed to integrate sequential feature acquisition processes into predictive models. I will demonstrate the optimality properties of this algorithm and demonstrate its use on critical care data from the publicly available MIMIC-III database and finally discuss how the framework can be used to construct personal machine learning-based diagnostic tools.

About the speaker

Christopher Yau is a Reader in Computational Biology at the Institute of Cancer and Genomic Sciences where he is based at the Centre for Computational Biology and leads the Statistical Machine Learning BioHealth group. He is an expert in statistical methodologies for machine learning and data science and specialises in genomic science particularly cancer. His research ranges from mathematical and statistical algorithm development to collaborations with experimental scientists and clinicians involving modelling real world biomedical data sets. He leads a diverse group of researchers who specialise in both experimental and computational modelling and regularly gives talks and lectures around the world on data science.

Christopher is playing a leading role in the development of Statistical Machine Learning for the Genomics England 100,000 Genomes project as sub-domain lead in Machine Learning for the Quantitative Methods, Machine Learning and Functional Genomics Clinical Interpretation Partnership. He sits on the committee of the Statistical Computing Section for the Royal Statistical Society and currently serves as a Career Development Task Force member for the Academy of Medical Sciences.

Agenda

15.30-15.45: Construction of Disease Trajectories from Electronic Health Records-  Dr Chris Smith, Leeds Institute for Health Sciences

15.45-17.00: Probabilistic approaches for optimal sequential feature acquisition- Dr Christopher Yau

17:00-18:00: Networking reception with drinks and nibbles hosted in the LIDA staff kitchen.

To book please email Hayley Irving with your name, occupation and faculty/organisation.