Large-Scale Evaluation and Liver Disease Risk Prediction in Finland’s National Electronic Health Record System: Feasibility Study Using Real-World Data
Bibliographic record
Abstract
BACKGROUND: Globally, the incidence and mortality of chronic liver disease are escalating. Early detection of liver disease remains a challenge, often occurring at symptomatic stages when preventative measures are less effective. The Chronic Liver Disease score (CLivD) is a predictive risk model developed using Finnish health care data, aiming to forecast an individual's risk of developing chronic liver disease in subsequent years. The Kanta Service is a national electronic health record system in Finland that stores comprehensive health care data including patient medical histories, prescriptions, and laboratory results, to facilitate health care delivery and research. OBJECTIVE: This study aimed to evaluate the feasibility of implementing an automatic CLivD score with the current Kanta platform and identify and suggest improvements for Kanta that would enable accurate automatic risk detection. METHODS: In this study, a real-world data repository (Kanta) was used as a data source for "The ClivD score" risk calculation model. Our dataset consisted of 96,200 individuals' whole medical history from Kanta. For real-world data use, we designed processes to handle missing input in the calculation process. RESULTS: We found that Kanta currently lacks many CLivD risk model input parameters in the structured format required to calculate precise risk scores. However, the risk scores can be improved by using the unstructured text in patient reports and by approximating variables by using other health data-like diagnosis information. Using structured data, we were able to identify only 33 out of 51,275 individuals in the "low risk" category and 308 out of 51,275 individuals (<1%) in the "moderate risk" category. By adding diagnosis information approximation and free text use, we were able to identify 18,895 out of 51,275 (37%) individuals in the "low risk" category and 2125 out of 51,275 (4%) individuals in the "moderate risk" category. In both cases, we were not able to identify any individuals in the "high-risk" category because of the missing waist-hip ratio measurement. We evaluated 3 scenarios to improve the coverage of waist-hip ratio data in Kanta and these yielded the most substantial improvement in prediction accuracy. CONCLUSIONS: We conclude that the current structured Kanta data is not enough for precise risk calculation for CLivD or other diseases where obesity, smoking, and alcohol use are important risk factors. Our simulations show up to 14% improvement in risk detection when adding support for missing input variables. Kanta shows the potential for implementing nationwide automated risk detection models that could result in improved disease prevention and public health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".