Ibrahim, Z;
Bean, D;
Searle, T;
Qian, L;
Wu, H;
Shek, A;
Kraljevic, Z;
... Dobson, RJ; + view all
(2021)
A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data.
IEEE Journal of Biomedical and Health Informatics
10.1109/jbhi.2021.3089287.
(In press).
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Abstract
The ability to perform accurate prognosis of patients is crucial for proactive clinical decision making, informed resource management and personalised care. Existing outcome prediction models suffer from a low recall of infrequent positive outcomes. We present a highly-scalable and robust machine learning framework to automatically predict adversity represented by mortality and ICU admission from time-series vital signs and laboratory results obtained within the first 24 hours of hospital admission. The stacked platform comprises two components: a) an unsupervised LSTM Autoencoder that learns an optimal representation of the time-series, using it to differentiate the less frequent patterns which conclude with an adverse event from the majority patterns that do not, and b) a gradient boosting model, which relies on the constructed representation to refine the prediction, incorporating static features of demographics, admission details and clinical summaries. The model is used to assess a patient's risk of adversity over time and provides visual justifications of its prediction, based on the patient's static features and dynamic signals. Results of three case studies for predicting mortality and ICU admission show that the model outperforms all existing outcome prediction models, achieving average Precision-Recall Areas Under the Curve (PR-AUCs) of 0.93 (95% CI: 0.878 - 0.969) in predicting mortality in ICU and general ward settings and 0.987 (95% CI: 0.985-0.995) in predicting ICU admission.
| Type: | Article |
|---|---|
| Title: | A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/jbhi.2021.3089287 |
| Publisher version: | http://dx.doi.org/10.1109/jbhi.2021.3089287 |
| 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. |
| Keywords: | Predictive models, Physiology, Biological system modeling, Hospitals, Oxygen, Data models, Tools |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10130158 |
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