Social Determinants of Health Prediction for ICD-9 Code with Reasoning Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914255979151360 |
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| author | Khan, Sharim Landes, Paul Cross, Adam Sun, Jimeng |
| author_facet | Khan, Sharim Landes, Paul Cross, Adam Sun, Jimeng |
| contents | Social Determinants of Health correlate with patient outcomes but are rarely captured in structured data. Recent attention has been given to automatically extracting these markers from clinical text to supplement diagnostic systems with knowledge of patients' social circumstances. Large language models demonstrate strong performance in identifying Social Determinants of Health labels from sentences. However, prediction in large admissions or longitudinal notes is challenging given long distance dependencies. In this paper, we explore hospital admission multi-label Social Determinants of Health ICD-9 code classification on the MIMIC-III dataset using reasoning models and traditional large language models. We exploit existing ICD-9 codes for prediction on admissions, which achieved an 89% F1. Our contributions include our findings, missing SDoH codes in 139 admissions, and code to reproduce the results. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_09709 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Social Determinants of Health Prediction for ICD-9 Code with Reasoning Models Khan, Sharim Landes, Paul Cross, Adam Sun, Jimeng Machine Learning Computation and Language Computers and Society Social Determinants of Health correlate with patient outcomes but are rarely captured in structured data. Recent attention has been given to automatically extracting these markers from clinical text to supplement diagnostic systems with knowledge of patients' social circumstances. Large language models demonstrate strong performance in identifying Social Determinants of Health labels from sentences. However, prediction in large admissions or longitudinal notes is challenging given long distance dependencies. In this paper, we explore hospital admission multi-label Social Determinants of Health ICD-9 code classification on the MIMIC-III dataset using reasoning models and traditional large language models. We exploit existing ICD-9 codes for prediction on admissions, which achieved an 89% F1. Our contributions include our findings, missing SDoH codes in 139 admissions, and code to reproduce the results. |
| title | Social Determinants of Health Prediction for ICD-9 Code with Reasoning Models |
| topic | Machine Learning Computation and Language Computers and Society |
| url | https://arxiv.org/abs/2601.09709 |