Automated SNOMED CT concept and attribute relationship detection through a web-based implementation of cTAKES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Background Information in Electronic Health Records is largely stored as unstructured free text. Natural language processing (NLP), or Medical Language Processing (MLP) in medicine, aims at extracting structured information from free text, and is less expensive and time-consuming than manual extraction. However, most algorithms in MLP are institution-specific or address only one clinical need, and thus cannot be broadly applied. In addition, most MLP systems do not detect concepts in misspelled text and cannot detect attribute relationships between concepts. The objective of this study was to develop and evaluate an MLP application that includes generic algorithms for the detection of (misspelled) concepts and of attribute relationships between them. Methods An implementation of the MLP system cTAKES, called DIRECT, was developed with generic SNOMED CT concept filter, concept relationship detection, and attribute relationship detection algorithms and a custom dictionary. Four implementations of cTAKES were evaluated by comparing 98 manually annotated oncology charts with the output of DIRECT. The F 1 -score was determined for named-entity recognition and attribute relationship detection for the concepts ‘lung cancer’, ‘non-small cell lung cancer’, and ‘recurrence’. The performance of the four implementations was compared with a two-tailed permutation test. Results DIRECT detected lung cancer and non-small cell lung cancer concepts with F 1 -scores between 0.828 and 0.947 and between 0.862 and 0.933, respectively. The concept recurrence was detected with a significantly higher F 1 -score of 0.921, compared to the other implementations, and the relationship between recurrence and lung cancer with an F 1 -score of 0.857. The precision of the detection of lung cancer, non-small cell lung cancer, and recurrence concepts were 1.000, 0.966, and 0.879, compared to precisions of 0.943, 0.967, and 0.000 in the original implementation, respectively. Conclusion DIRECT can detect oncology concepts and attribute relationships with high precision and can detect recurrence with significant increase in F 1 -score, compared to the original implementation of cTAKES, due to the usage of a custom dictionary and a generic concept relationship detection algorithm. These concepts and relationships can be used to encode clinical narratives, and can thus substantially reduce manual chart abstraction efforts, saving time for clinicians and researchers.
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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 it