ICD-10 code embedding for patient matching: a first step toward applications in causal inference from hospital discharge data
Bibliographic record
Abstract
Abstract We present WEMatch, a Word2vec Enhanced ICD-10 Matching algorithm, for patient matching based on ICD-10 codes and make a first step toward evaluating its potential use in causal inference contexts by comparing it to several propensity score–based patient matching approaches. We use hospital discharge data from the Insel Group in the canton of Bern, Switzerland, from 2014 to 2023, including ICD-10 codes, age, sex, and BMI for 386 842 hospitalizations. In the null-effect matched cohort design, patients are matched to a randomly selected “treatment group” (selected from 1 of 4 selected primary ICD-10 diagnoses) using 4 algorithms: WEMatch and 3 propensity score matching (PSM)–based models. Performance is assessed by comparing the balance of key variables, hazard ratios of mortality, and length of hospital stay between the treatment and matched control groups. WEMatch and PSM methods perform similarly for 3 out of 4 tested ICD-10 codes. For one ICD-10 code, WEMatch outperforms PSM methods, because the patient population is especially diverse. This study establishes a robust framework for systematically comparing patient matching algorithms. The groundwork is set for applying WEMatch to clinically relevant questions. By distributing this as an R package, we promote its integration into clinical research, potentially supporting future causal analyses using observational data.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".