Patient information extraction in noisy tele-health texts
Bibliographic record
Abstract
We explore methods for effectively extracting information from clinical narratives, which are captured in a public health consulting phone service called HealthLink. The currently available data consists of dialogues constructed by nurses while consulting patients on the phone. Since the data are interviews transcribed by nurses during phone conversations, they include a significant volume and variety of noise: First is explicit noise, which includes spelling errors, unfinished sentences, omission of sentence delimiters, variants of terms, etc. Second is implicit noise, which includes non-patient's information and negation of patient's information. To filter explicit noise, we propose our biomedical term detection/normalization method: it resolves misspelling, term variations, and arbitrary abbreviation of terms by nurses. In detecting temporal terms and other types of named entities (which show patients' personal information such as age, and sex), we propose a bootstrapping-based pattern learning to detect all kinds of arbitrary variations of the named entities. To address implicit noise, we propose a dependency path-based filtering method. The result of our denoising is the extraction of normalized patient information. The experimental results show that we achieve reasonable performance with our noise reduction methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".