Evaluation of the impact of expert knowledge: How decision support scores impact the effectiveness of automatic knowledge-driven feature engineering (aKDFE)
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913863056752640 |
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| author | Björneld, Olof Hammar, Tora Nilsson, Daniel Lincke, Alisa Löwe, Welf |
| author_facet | Björneld, Olof Hammar, Tora Nilsson, Daniel Lincke, Alisa Löwe, Welf |
| contents | Adverse Drug Events (ADEs), harmful medication effects, pose significant healthcare challenges, impacting patient safety and costs. This study evaluates automatic Knowledge-Driven Feature Engineering (aKDFE) for improved ADE prediction from Electronic Health Record (EHR) data, comparing it with automated event-based Knowledge Discovery in Databases (KDD). We investigated how incorporating domain-specific ADE risk scores for prolonged heart QT interval, extracted from the Janusmed Riskprofile (Janusmed) Clinical Decision Support System (CDSS), affects prediction performance using EHR data and medication handling events. Results indicate that, while aKDFE step 1 (event-based feature generation) alone did not significantly improve ADE prediction performance, aKDFE step 2 (patient-centric transformation) enhances the prediction performance. High Area Under the Receiver Operating Characteristic curve (AUROC) values suggest strong feature correlations to the outcome, aligning with the predictive power of patients' prior healthcare history for ADEs. Statistical analysis did not confirm that incorporating the Janusmed information (i) risk scores and (ii) medication route of administration into the model's feature set enhanced predictive performance. However, the patient-centric transformation applied by aKDFE proved to be a highly effective feature engineering approach. Limitations include a single-project focus, potential bias from machine learning pipeline methods, and reliance on AUROC. In conclusion, aKDFE, particularly with patient-centric transformation, improves ADE prediction from EHR data. Future work will explore attention-based models, event feature sequences, and automatic methods for incorporating domain knowledge into the aKDFE framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_05928 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluation of the impact of expert knowledge: How decision support scores impact the effectiveness of automatic knowledge-driven feature engineering (aKDFE) Björneld, Olof Hammar, Tora Nilsson, Daniel Lincke, Alisa Löwe, Welf Machine Learning 62R01, 68T05 I.2.6 Adverse Drug Events (ADEs), harmful medication effects, pose significant healthcare challenges, impacting patient safety and costs. This study evaluates automatic Knowledge-Driven Feature Engineering (aKDFE) for improved ADE prediction from Electronic Health Record (EHR) data, comparing it with automated event-based Knowledge Discovery in Databases (KDD). We investigated how incorporating domain-specific ADE risk scores for prolonged heart QT interval, extracted from the Janusmed Riskprofile (Janusmed) Clinical Decision Support System (CDSS), affects prediction performance using EHR data and medication handling events. Results indicate that, while aKDFE step 1 (event-based feature generation) alone did not significantly improve ADE prediction performance, aKDFE step 2 (patient-centric transformation) enhances the prediction performance. High Area Under the Receiver Operating Characteristic curve (AUROC) values suggest strong feature correlations to the outcome, aligning with the predictive power of patients' prior healthcare history for ADEs. Statistical analysis did not confirm that incorporating the Janusmed information (i) risk scores and (ii) medication route of administration into the model's feature set enhanced predictive performance. However, the patient-centric transformation applied by aKDFE proved to be a highly effective feature engineering approach. Limitations include a single-project focus, potential bias from machine learning pipeline methods, and reliance on AUROC. In conclusion, aKDFE, particularly with patient-centric transformation, improves ADE prediction from EHR data. Future work will explore attention-based models, event feature sequences, and automatic methods for incorporating domain knowledge into the aKDFE framework. |
| title | Evaluation of the impact of expert knowledge: How decision support scores impact the effectiveness of automatic knowledge-driven feature engineering (aKDFE) |
| topic | Machine Learning 62R01, 68T05 I.2.6 |
| url | https://arxiv.org/abs/2504.05928 |