A Medical Decision Support Tool Using Text-mining Techniques with Electronic Medical Records
Bibliographic record
Abstract
Free-text clinical notes represent a vast amount of information which in the past has been un-analyzed data. In this paper we apply text-mining methods on the free-text in electronic medical records (EMRs) to define treatment options for patients with lower back pain. The goal of the project is to develop a generalized text-mining framework that can be used not only in the treatment of lower back pain, but any medical condition. The framework takes advantage of open-source algorithms for anonymization and the clinical NLP tool Apache Clinical Text Analysis and Knowledge Extraction System (cTAKES) to form structured data from clinical notes. The machine learning algorithm uses seven years of extracted clinical notes from the primary care physician to classify 20 patients’ pattern of back pain. With the small dataset provided, the algorithm managed to achieve diagnosis accuracy of up to 100%. The twenty-patient dataset was simply too homogenous and small to make statistical claims for sensitivity and specificity. However, the system shows indicators of satisfactory performance, and we are trying to extract more data of patients who do not have back pain to be able to validate our system better.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".