Wijeratne, P;
Alexander, D;
(2020)
Learning transition times in event sequences: the Event-Based Hidden Markov Model of disease progression.
Presented at: Machine Learning for Health (ML4H) at NeurIPS 2020, Vertual.
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Abstract
Progressive diseases worsen over time and are characterised by monotonic change in features that track disease progression. Here we connect ideas from two formerly separate methodologies – event-based and hidden Markov modelling – to derive a new generative model of disease progression. Our model can uniquely infer the most likely group-level sequence and timing of events (natural history) from limited datasets. Moreover, it can infer and predict individual-level trajectories (prognosis) even when data are missing, giving it high clinical utility. Here we derive the model and provide an inference scheme based on the expectation maximisation algorithm. We use clinical, imaging and biofluid data from the Alzheimer’s Disease Neuroimaging Initiative to demonstrate the validity and utility of our model. First, we train our model to uncover a new grouplevel sequence of feature changes in Alzheimer’s disease over a period of ∼17.3 years. Next, we demonstrate that our model provides improved utility over a continuous time hidden Markov model by area under the receiver operator characteristic curve ∼0.23. Finally, we demonstrate that our model maintains predictive accuracy with up to 50% missing data. These results support the clinical validity of our model and its broader utility in resourcelimited medical applications.
| Type: | Conference item (Presentation) |
|---|---|
| Title: | Learning transition times in event sequences: the Event-Based Hidden Markov Model of disease progression |
| Event: | Machine Learning for Health (ML4H) at NeurIPS 2020 |
| Location: | Vertual |
| Dates: | 11-12-2020 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://ml4health.github.io/2020/pages/extended-ab... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10113883 |
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