Annotation dataset of social determinants of health from MIMIC-III Clinical Care Database
Bibliographic record
Abstract
Social determinants of health (SDoH) have an important impact on patient outcomes but are incompletely collected from the electronic health records (EHR). This study researched the ability of large language models to extract SDoH from free text in EHRs, where they are most commonly documented, and explored the role of synthetic clinical text for improving the extraction of these scarcely documented, yet extremely valuable, clinical data. We developed annotation guidelines for sentence-level annotation of SDoH that are not reliably available as structured data in the EHR: employment, housing, transportation, parental status, relationship, and social support. Sentences were labeled for both the presence of an SDoH mention and the presence of an adverse SDoH mention. After finalizing the annotation guidelines, two annotators manually annotated a separate corpus, which cannot be released due to PHI. A total of 300/800 (37.5%) of these notes underwent dual annotation. Before adjudication, dually-annotated notes had a Krippendorf's alpha agreement of 0.86 and Cohen's Kappa of 0.86 for any SDoH mention categories. For adverse SDoH mentions, notes had a Krippendorf's alpha agreement of 0.76 and Cohen's Kappa of 0.76. As an external validation, 200 notes from MIMIC-III written by physicians, social workers, and nurses were manually annotated by a single annotator. Here, we release this manually annotated corpus of 200 MIMC- III notes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".