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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2510.24217 |
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| _version_ | 1866914118124961792 |
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| author | Turubayev, Alisher Shopova, Anna Lange, Fabian Kamalak, Mahmut Mattes, Paul Ayvasky, Victoria Arnrich, Bert Pfitzner, Bjarne van de Water, Robin P. |
| author_facet | Turubayev, Alisher Shopova, Anna Lange, Fabian Kamalak, Mahmut Mattes, Paul Ayvasky, Victoria Arnrich, Bert Pfitzner, Bjarne van de Water, Robin P. |
| contents | As more Intensive Care Unit (ICU) data becomes available, the interest in developing clinical prediction models to improve healthcare protocols increases. However, the lack of data quality still hinders clinical prediction using Machine Learning (ML). Many vital sign measurements, such as heart rate, contain sizeable missing segments, leaving gaps in the data that could negatively impact prediction performance. Previous works have introduced numerous time-series imputation techniques. Nevertheless, more comprehensive work is needed to compare a representative set of methods for imputing ICU vital signs and determine the best practice. In reality, ad-hoc imputation techniques that could decrease prediction accuracy, like zero imputation, are still used. In this work, we compare established imputation techniques to guide researchers in improving the performance of clinical prediction models by selecting the most accurate imputation technique. We introduce an extensible and reusable benchmark with currently 15 imputation and 4 amputation methods, created for benchmarking on major ICU datasets. We hope to provide a comparative basis and facilitate further ML development to bring more models into clinical practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_24217 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Closing Gaps: An Imputation Analysis of ICU Vital Signs Turubayev, Alisher Shopova, Anna Lange, Fabian Kamalak, Mahmut Mattes, Paul Ayvasky, Victoria Arnrich, Bert Pfitzner, Bjarne van de Water, Robin P. Machine Learning Artificial Intelligence As more Intensive Care Unit (ICU) data becomes available, the interest in developing clinical prediction models to improve healthcare protocols increases. However, the lack of data quality still hinders clinical prediction using Machine Learning (ML). Many vital sign measurements, such as heart rate, contain sizeable missing segments, leaving gaps in the data that could negatively impact prediction performance. Previous works have introduced numerous time-series imputation techniques. Nevertheless, more comprehensive work is needed to compare a representative set of methods for imputing ICU vital signs and determine the best practice. In reality, ad-hoc imputation techniques that could decrease prediction accuracy, like zero imputation, are still used. In this work, we compare established imputation techniques to guide researchers in improving the performance of clinical prediction models by selecting the most accurate imputation technique. We introduce an extensible and reusable benchmark with currently 15 imputation and 4 amputation methods, created for benchmarking on major ICU datasets. We hope to provide a comparative basis and facilitate further ML development to bring more models into clinical practice. |
| title | Closing Gaps: An Imputation Analysis of ICU Vital Signs |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2510.24217 |