Developing a Pressure Injury Predictive Indicator System for Data Mining in Health Care Information Systems: A Sequential Mixed-Methods Study
Bibliographic record
Abstract
OBJECTIVE: Prevalence of hospital-acquired pressure injury (PI), as a critical measurement of medical care quality, has shown an upward trend. The aim of this study was to determine the predictive indicators of potential PIs and ensure that the predictive indicators can automatically be mined from electronic medical record systems. METHODS: The methods include 2 parts. One is the modified Delphi for indicator development, including clinical health care provider interviews, literature review, research group meetings, and Delphi survey. The other is feature selection, including extracting indicators from the health care information system (HIS) by structured query language and selecting indicators using the Random Forest technique. RESULTS: A predictive indicator system (with feature extraction rules for each indicator) consisting of 3 categories and 14 indicators was constructed. The experts' consensus was reached on all indicators (mean=4.28±0.65 to 4.94±0.23; coefficient of variation=4.63% to 17.20%; agreement rate=83.30% to 100.00%). The agreement between manual extraction and the computer's automatic extraction was good, with a Cohen κ score of 0.64 to 1.00. The accuracy of the good parsimonious prediction model was 95.26%. CONCLUSIONS: This predictive indicator system is prepared for automatic PI prediction in the HIS. Many revisions should be conducted in further studies and practices in a real-life medical environment.
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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 | 0.095 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".