Medical search and classification tools for recommendation
Bibliographic record
Abstract
their patients' records from paper to computer, enormous amounts of electronic medical records (EMR) have become available for medical research. Some of the EMR data are well-structured, for which traditional database management systems can provide effective retrieval and management functions. However, most of the EMR data (such as progress notes and consultation letters) are in free text formats. How to effectively and efficiently retrieve and discover useful information from the vast amount of such semi-structured data is a challenge faced by medical professionals. Without proper tools, the rich information and knowledge buried in the medical health records are unavailable for clinical research and decision-making. The objective of our research is to develop text analytics tools that are capable of parsing clinical medical data so that predefined search subjects that correspond to a list of medical diagnoses can be extracted. In addition to this particular core functionality, it is also desired that several important assets should be present within the text-analytics tools in order to improve its overall ability to be used as recommendation tools. In this research, we work with research scientists at the Institute for Clinical Evaluative Sciences (ICES) in Toronto and examine a number of techniques for structuring and processing free text documents in order to effectively and efficiently search and analyze vast amount of medical records. We implement several powerful medical text analytics tools for clinical data searching and classification. For data classification, our tools sort through a great amount of patientrecords to identify the likelihood of a patient having myocardial infarction (MI) or hypertension (HTN), and classify the patients accordingly. Our tools can also identify the likelihood of a patient being a smoker, previous smoker or non-smoker based on the text data of medical records.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.031 |
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".