SNOMED CT-Based Standardized e-Clinical Pathways for Enabling Big Data Analytics in Healthcare
Bibliographic record
Abstract
Automation of healthcare facilities represents a challenging task of streamlining a highly information-intensive sector. Modern healthcare processes produce large amounts of data that have great potential for health policymakers and data science researchers. However, a considerable portion of such data is not captured in electronic format and hidden inside the paperwork. A major source of missing data in healthcare is paper-based clinical pathways (CPs). CPs are healthcare plans that detail the interventions for the treatment of patients, and thus are the primary source for healthcare data. However, most CPs are used as paper-based documents and not fully automated. A key contribution towards the full automation of CPs is their proper computer modeling and encoding their data with international clinical terminologies. We present in this research an ontology-based CP automation model in which CP data are standardized with SNOMED CT, thus enabling machine learning algorithms to be applied to CP-based datasets. CPs automated under this model contribute significantly to reducing data missingness problems, enabling detailed statistical analyses on CP data, and improving the results of data analytics algorithms. Our experimental results on predicting the Length of Stay (LOS) of stroke patients using a dataset resulting from an e-clinical pathway demonstrate improved prediction results compared with LOS prediction using traditional EHR-based datasets. Fully automated CPs enrich medical datasets with more CP data and open new opportunities for machine learning algorithms to show their full potential in improving healthcare, reducing costs, and increasing patient satisfaction.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".