Pictorial Visualization of EMR Summary Interface and Medical Information Extraction of Clinical Notes
Bibliographic record
Abstract
Current Electronic Medical Records (EMR) systems contain large amounts of texts and various tables, to show numerous health data. This type of presentation limits people from promptly determining medical conditions or quickly finding desired information given the large volume of texts that needs to be read. We aim to tackle this as information visualization and extraction problems by creation of easy and intuitive user interfaces for visualizing medical information. We present both a novel graphical interface for visualizing a summary of medical information and an information extraction system that is able to extract and visualize the patient's medical information from structured clinical notes. The graphical interface allows spatial-position based representations of medical information on human body images (front and back views) and temporal-time based representation of it through interconnected time axes. Medical histories are classified into several event categories and 6 physiological systems to enable efficient browsing of selected information. To extract visual tags from a given clinical note, we use natural language processing. We employ Metamap of 2014AA knowledge source for medical information extraction. We trained 1294 English clinical notes with a Time-Entity Detection model by Apache Open NLP to abstract the time expressions. Extracted location of illness is assigned into one of 6 physiological systems is displayed in spatial interface while the related data are denoted on a horizontal timeline of temporal interface.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.036 | 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".