Feature importance analysis for patient management decisions
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Accesso online: | |
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| _version_ | 1866909017203277824 |
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| author | Valko, Michal Hauskrecht, Milos |
| author_facet | Valko, Michal Hauskrecht, Milos |
| contents | The objective of this paper is to understand what characteristics and features of clinical data influence physician's decision about ordering laboratory tests or prescribing medications the most. We conduct our analysis on data and decisions extracted from electronic health records of 4486 post-surgical cardiac patients. The summary statistics for 335 different lab order decisions and 407 medication decisions are reported. We show that in many cases, physician's lab-order and medication decisions can be well predicted from a small subset of all features. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_04666 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Feature importance analysis for patient management decisions Valko, Michal Hauskrecht, Milos Machine Learning The objective of this paper is to understand what characteristics and features of clinical data influence physician's decision about ordering laboratory tests or prescribing medications the most. We conduct our analysis on data and decisions extracted from electronic health records of 4486 post-surgical cardiac patients. The summary statistics for 335 different lab order decisions and 407 medication decisions are reported. We show that in many cases, physician's lab-order and medication decisions can be well predicted from a small subset of all features. |
| title | Feature importance analysis for patient management decisions |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.04666 |