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FORESIGHT: A novel GPT-based pipeline trained on NHS data
February 7, 2023
Zeljko Kraljevic
outlines how these foundation models for medicine can provide the potential for a diverse integration of medical data that includes electronic health records, images, lab values, biologic layers such as the genome and gut microbiome...
Over the past four years, the AI world has surged ahead with large language models (LLMs), also known as “foundation models” which can be adapted to achieve many linguistic tasks. You’ve probably seen a plethora of articles in the media recently about some of these models (ChatGPT, Dalle-2), that can write coherent essays, write code, but also generate art and films, and many other capabilities.
With the NHS at breaking point, a critical question is whether these AI approaches could be used to improve care. Hospital records hold detailed information about each patient's health status and general clinical history, a large portion of which is stored within the unstructured text. Temporal modelling of this medical history, which considers the sequence of events, could be used to forecast and simulate future events, estimate risk, suggest alternative diagnoses or forecast complications.
I have developed
Foresight
as part of the
CogStack
platform, a novel GPT-based pipeline that is trained on NHS data to forecast future medical events such as disorders, medications, symptoms and interventions.
On tests in two large King’s Health Partner hospitals (King’s College Hospital, South London and Maudsley) and the US MIMIC-III dataset Foresight performed well when set challenges by clinicians. The model is being used for many uses including real-world risk estimation, virtual clinical trials and clinical research to study the progression of diseases, simulate interventions and counterfactuals, and for educational purposes.
Preprint:
https://arxiv.org/abs/2212.08072
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We’re thrilled to announce that John Jumper, PhD , will kick-off our 2024/2025 Seminar Series with his talk, "Extending AlphaFold to make predictions across the universe of biomolecular interactions" . John is one of the key pioneers behind the development of Google’s DeepMind AlphaFold - an artificial intelligence model to predict protein structures from their amino acid sequence with high accuracy. This in-person event promises to be an incredible opportunity to hear from one of the foremost innovators in AI and biology. Seminar Series Event: Extending AlphaFold to make predictions across the universe of biomolecular interactions Date and Time: 14:00 – 15.00, Thursday 10 October 2024 Location: The Council Room, 2nd floor, The King’s Building, Strand Campus Registration: Limited to EPSRC DRIVE-Health students in the first instance. Please email drive-health-cdt@kcl.ac.uk to check availability. Abstract: The high accuracy of AlphaFold 2 in predicting protein structures and protein-protein interactions raises the question of how to extend the success of AlphaFold to general biomolecular modeling, including protein-nucleic and protein-small molecule structure predictions as well as the effects of post-translational modification. In this talk, I will discuss our latest work on AlphaFold 3 to develop a single deep learning system that makes accurate predictions across these interaction types, as well as examine some of the remaining challenges in predicting the universe of biologically-relevant protein interactions.
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