Imperial College London

DrFinneasCatling

Faculty of MedicineDepartment of Surgery & Cancer

Clinical Research Fellow
 
 
 
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Contact

 

f.catling

 
 
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Location

 

Commonwealth BuildingHammersmith Campus

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Summary

 

Summary

I am an academic Anaesthetic and Critical Care doctor, and a Wellcome Trust 4i Clinical PhD Fellow at Imperial.

My research aims at early diagnosis and improved treatment of critical illness, using methods from Bayesian statistics and machine learning. I am particularly interested in merging these methods with physiological models to provide bedside decision support, in disease heterogeneity, and in the role of uncertainty in clinical decision-making.

My recent work includes representing ICU patients’ status using time-series data to allow prediction of clinical events, exploiting structured knowledge for automated clinical coding, mortality risk prediction for patients undergoing emergency laparotomy, and phenotyping of ventilator-associated pneumonia in electronic health records.

I am an NHS England Clinical Entrepreneurship Fellow, a fellow of the Faculty of Clinical Informatics and a member of the core advisory group for the Academic Health Science Networks Artificial Intelligence Programme. I co-founded and lecture on the Data Science for Doctors courses, and previously co-founded the medical education startup T-Log.

See my ResearchGateTwitter or LinkedIn profiles for more information, or get in touch via f.catling@imperial.ac.uk

Selected publications


Catling F, Wolff AH. Temporal convolutional networks allow early prediction of events in critical care. J Am Med Inform Assoc 2020;27:355–65.

Catling F, Spithourakis GP, Riedel S. Towards automated clinical coding. Int J Med Inform 2018;120:50–61.