Recognition of Patient-Related Named Entities in Noisy Tele-Health Texts
Bibliographic record
Abstract
We explore methods for effectively extracting information from clinical narratives that are captured in a public health consulting phone service called HealthLink. Our research investigates the application of state-of-the-art natural language processing and machine learning to clinical narratives to extract information of interest. The currently available data consist of dialogues constructed by nurses while consulting patients by phone. Since the data are interviews transcribed by nurses during phone conversations, they include a significant volume and variety of noise. When we extract the patient-related information from the noisy data, we have to remove or correct at least two kinds of noise: explicit noise , which includes spelling errors, unfinished sentences, omission of sentence delimiters, and variants of terms, and implicit noise , which includes non-patient information and patient's untrustworthy information. To filter explicit noise, we propose our own biomedical term detection/normalization method: it resolves misspelling, term variations, and arbitrary abbreviation of terms by nurses. In detecting temporal terms, temperature, and other types of named entities (which show patients’ personal information such as age and sex), we propose a bootstrapping-based pattern learning process to detect a variety of arbitrary variations of 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, and we visualize the named entities by constructing a graph that shows the relations between named entities. The objective of this knowledge discovery task is to identify associations between biomedical terms and to clearly expose the trends of patients’ symptoms and concern; 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 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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".