Kunz, H;
Pasea, L;
Denaxas, S;
(2019)
Classification of atherothrombotic events in myocardial infarctions survivors with supervised machine learning using data from an electronic health record system.
Presented at: International Conference on Informatics, Management and Technology in Healthcare, ICIMTH 2019, Athens, Greece.
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Abstract
The aim was to build a prediction model for subsequent atherothrombotic events for patients who survived a myocardial infarction. The dataset contained 7,582 patients from a national Electronic Health Record. The prediction is a binary outcome (event and no event) in a period of five years after a myocardial infarction. Different classifiers were tested and XGBoost achieved the best F1-score=0.76. Top features are: imd_score, age_at_entry, egfr_ckdepi_base, height, and SBP_base.
| Type: | Conference item (Presentation) |
|---|---|
| Title: | Classification of atherothrombotic events in myocardial infarctions survivors with supervised machine learning using data from an electronic health record system |
| Event: | International Conference on Informatics, Management and Technology in Healthcare, ICIMTH 2019 |
| Location: | Athens, Greece |
| Dates: | 05 July 2019 - 07 July 2019 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://www.iospress.nl/book/health-informatics-vi... |
| 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: | classification, atherothrombosis, supervised machine learning, prognosis |
| 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/10076234 |
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