Natural language processing: algorithms and tools to extract computable information from EHRs and from the biomedical literature
Bibliographic record
Abstract
The increasing adoption of electronic health records (EHRs) and the corresponding interest in using these data for quality improvement and research have made it clear that the interpretation of narrative text contained in the records is a critical step. The biomedical literature is another important information source that can benefit from approaches requiring structuring of data contained in narrative text. For the first time, we dedicate an entire issue of JAMIA to biomedical natural language processing (NLP), a topic that has been among the most cited in this journal for the past few years. We start with a description of a contest to select the best performing algorithms for detection of temporal relationships in clinical documents (see page 806), followed by a general review of significance and brief description of commonly used methods to address this task (see page 814). Top performing approaches are featured in seven articles from five different countries—Canada (see page 843), China (see page 849), France (see page 820), Serbia (see page 859), and the US (see page 828, 836, 867).
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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 source (direct Gemma or distilled Codex), 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".