Predicting Clinical Outcomes with Waveform LSTMs
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915197530144768 |
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| author | Albada, Michael |
| author_facet | Albada, Michael |
| contents | Data mining and machine learning hold great potential to enable health systems to systematically use data and analytics to identify inefficiencies and best practices that improve care and reduce costs. Waveform data offers particularly detailed information on how patient health evolves over time and has the potential to significantly improve prediction accuracy on multiple benchmarks, but has been widely under-utilized, largely because of the challenges in working with these large and complex datasets. This study evaluates the potential of leveraging clinical waveform data to improve prediction accuracy on a single benchmark task: the risk of mortality in the intensive care unit. We identify significant potential from this data, beating the existing baselines for both logistic regression and deep learning models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10925 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting Clinical Outcomes with Waveform LSTMs Albada, Michael Machine Learning Artificial Intelligence Data mining and machine learning hold great potential to enable health systems to systematically use data and analytics to identify inefficiencies and best practices that improve care and reduce costs. Waveform data offers particularly detailed information on how patient health evolves over time and has the potential to significantly improve prediction accuracy on multiple benchmarks, but has been widely under-utilized, largely because of the challenges in working with these large and complex datasets. This study evaluates the potential of leveraging clinical waveform data to improve prediction accuracy on a single benchmark task: the risk of mortality in the intensive care unit. We identify significant potential from this data, beating the existing baselines for both logistic regression and deep learning models. |
| title | Predicting Clinical Outcomes with Waveform LSTMs |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2503.10925 |