International journal of medical informatics · 2026
Auditing diagnosis-conditioned electronic health record exports for secondary use: A selection-aware study in child and adolescent psychiatry.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Toksöz Seda Bayat, Işık Gültekin, Gülbetekin Eda, Taşpolat Esra Rabia
Аннотация
BACKGROUND: Diagnosis-conditioned electronic health record (EHR) exports can embed selection effects in observed associations and machine-learning results.
OBJECTIVE: To evaluate Selection-Aware Mapping and Predictive Adequacy for Electronic Health Records (SAMPA-EHR) as a rejection-capable audit for secondary use.
METHODS: Four outpatient exports for autism, attention-deficit/hyperactivity disorder (ADHD), specific learning disorder, and anxiety (1 October 2025-21 July 2026) were merged at encounter level. The audit separated export-family selection diagnostics from positive co-coding with false-discovery-rate control, evaluated non-negative matrix factorization (NMF) ranks 2-6 across 50 initializations against stability and component-size gates, and benchmarked administrative features using repeated nested cross-validation, training-fold calibration, prevalence references, and a calendar-period stress test.
RESULTS: The 1,853 source rows yielded 1,631 encounters and 30 clinical labels; 421 encounters (25.8 %) had at least two labels. All six export-family associations were negative, including ADHD-anxiety despite 120 joint encounters (odds ratio 0.25; phi - 0.292), a pattern consistent with ascertainment effects. Seven positive label pairs met the descriptive retention criteria. No NMF rank passed both eligibility gates (highest mean adjusted Rand index: 0.742 at rank 2). Logistic regression showed no improvement over the prevalence reference (AUROC 0.505, 95 % CI 0.475-0.535; AUPRC 0.258 versus 0.258); calendar-period AUROC was 0.500.
CONCLUSIONS: SAMPA-EHR distinguished reportable local co-coding from latent and predictive outputs that did not meet the audit criteria. The findings support encounter-level data-governance decisions within the evaluated extraction context; they do not establish patient-level phenotypes or clinical prediction.
OBJECTIVE: To evaluate Selection-Aware Mapping and Predictive Adequacy for Electronic Health Records (SAMPA-EHR) as a rejection-capable audit for secondary use.
METHODS: Four outpatient exports for autism, attention-deficit/hyperactivity disorder (ADHD), specific learning disorder, and anxiety (1 October 2025-21 July 2026) were merged at encounter level. The audit separated export-family selection diagnostics from positive co-coding with false-discovery-rate control, evaluated non-negative matrix factorization (NMF) ranks 2-6 across 50 initializations against stability and component-size gates, and benchmarked administrative features using repeated nested cross-validation, training-fold calibration, prevalence references, and a calendar-period stress test.
RESULTS: The 1,853 source rows yielded 1,631 encounters and 30 clinical labels; 421 encounters (25.8 %) had at least two labels. All six export-family associations were negative, including ADHD-anxiety despite 120 joint encounters (odds ratio 0.25; phi - 0.292), a pattern consistent with ascertainment effects. Seven positive label pairs met the descriptive retention criteria. No NMF rank passed both eligibility gates (highest mean adjusted Rand index: 0.742 at rank 2). Logistic regression showed no improvement over the prevalence reference (AUROC 0.505, 95 % CI 0.475-0.535; AUPRC 0.258 versus 0.258); calendar-period AUROC was 0.500.
CONCLUSIONS: SAMPA-EHR distinguished reportable local co-coding from latent and predictive outputs that did not meet the audit criteria. The findings support encounter-level data-governance decisions within the evaluated extraction context; they do not establish patient-level phenotypes or clinical prediction.