Extending the Power of Problem Oriented Medical Record with Disease Association Discovery: The Case Study of Empowering QL4POMR with OpenTargets
Bibliographic record
Abstract
Medical informatics was profoundly influenced by clinical case presentations for providing evidence-based medicine.However, concerns about weak inferences and the high likelihood of bias associated with such reports have resulted in minimal attention being devoted to developing frameworks for approaching, appraising, synthesizing, and applying evidence derived from case reports/series.Nevertheless, the nature of being in a connected world through the emerging information technologies presented a wind of change to link these clinical cases to create together with other knowledge sources from basic science to enhance human health and well-being as well as to build stronger evidence-based medicine.This article is an attempt to describe how to link clinical cases described through the SOAP (Subjective, Objective, Assessment, and Plan) note to the electronic healthcare record and the other associated clinical information from diverse courses.It is part of our efforts to extend our developed QL4POMR problem-oriented medical record to provide more associated clinical information on related drugs and evidence that strengthen the clinician's decision for diagnosis and prognosis.Providing such clinical association was made through the incorporation of a data layer using the Gatsby API and the external biomedical associations through the OpenTargets API.
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 | 0.036 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".