Global Deep Forecasting with Patient-Specific Pharmacokinetics
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Acceso en línea: | |
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| _version_ | 1866908713450733568 |
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| author | Potosnak, Willa Challu, Cristian Olivares, Kin G. Dufendach, Keith A. Dubrawski, Artur |
| author_facet | Potosnak, Willa Challu, Cristian Olivares, Kin G. Dufendach, Keith A. Dubrawski, Artur |
| contents | Forecasting healthcare time series data is vital for early detection of adverse outcomes and patient monitoring. However, it can be challenging in practice due to variable medication administration and unique pharmacokinetic (PK) properties of each patient. To address these challenges, we propose a novel hybrid global-local architecture and a PK encoder that informs deep learning models of patient-specific treatment effects. We showcase the efficacy of our approach in achieving significant accuracy gains in a blood glucose forecasting task using both realistically simulated and real-world data. Our PK encoder surpasses baselines by up to 16.4% on simulated data and 4.9% on real-world data for individual patients during critical events of severely high and low glucose levels. Furthermore, our proposed hybrid global-local architecture outperforms patient-specific PK models by 15.8%, on average. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_13135 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Global Deep Forecasting with Patient-Specific Pharmacokinetics Potosnak, Willa Challu, Cristian Olivares, Kin G. Dufendach, Keith A. Dubrawski, Artur Machine Learning Quantitative Methods Forecasting healthcare time series data is vital for early detection of adverse outcomes and patient monitoring. However, it can be challenging in practice due to variable medication administration and unique pharmacokinetic (PK) properties of each patient. To address these challenges, we propose a novel hybrid global-local architecture and a PK encoder that informs deep learning models of patient-specific treatment effects. We showcase the efficacy of our approach in achieving significant accuracy gains in a blood glucose forecasting task using both realistically simulated and real-world data. Our PK encoder surpasses baselines by up to 16.4% on simulated data and 4.9% on real-world data for individual patients during critical events of severely high and low glucose levels. Furthermore, our proposed hybrid global-local architecture outperforms patient-specific PK models by 15.8%, on average. |
| title | Global Deep Forecasting with Patient-Specific Pharmacokinetics |
| topic | Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2309.13135 |