Engineering Natural Language Processing Solutions for Structured Information from Clinical Text: Extracting Sentinel Events from Palliative Care Consult Letters
Bibliographic record
Abstract
Despite a trend to formalize and codify medical information, natural language communications still play a prominent role in health care workflows, in particular when it comes to hand-overs between providers. Natural language processing (NLP) attempts to bridge the gap between informal, natural language information and coded, machine-interpretable data. This paper reports on a study that applies an advanced NLP method for the extraction of sentinel events in palliative care consult letters. Sentinel events are of interest to predict survival and trajectory for patients with acute palliative conditions. Our NLP method combines several novel characteristics, e.g., the consideration of topological knowledge structures sourced from an ontological terminology system (SNOMED CT). The method has been applied to the extraction of different types of sentinel events, including simple facts, temporal conditions, quantities, and degrees. A random selection of 215 anonymized consult letters was used for the study. The results of the NLP extraction were evaluated by comparison with coded sentinel event data captured independently by clinicians. The average accuracy of the automated extraction was 73.6%.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".