Uncertainty aware prediction from incomplete clinical records: a conformal prediction study of Parkinsonian classification using C-OPN data

Abstract

Background: Incomplete clinical records and imbalanced diagnostic groups complicate Parkinsonian classification. We examined whether incomplete records could support useful lists of diagnostic possibilities through conformal prediction, assessing how often the lists retained the recorded diagnosis, their length and the uncertainty remaining. Methods: We analyzed records from 3,434 Canadian Open Parkinson Network participants: 2,853 with Parkinson's disease, 171 in the atypical Parkinsonian group (AP) and 410 healthy controls. In three repeats of five-fold cross-validation, we compared single-category predictions, conformal prediction sets and lists combined across 21 analyses using alternative models and assumptions about missing entries. An information deficit score summarized unavailable information. Results: The median proportion of eligible predictor entries unrecorded per participant was 75.6%. Single-category prediction correctly classified 89.29% of test records (balanced accuracy 55.40%), but only 10.33% of AP records. Conformal prediction sets included the recorded category in 93.35% of evaluation records and averaged 1.846 categories; combined lists increased inclusion to approximately 99% and averaged 2.289 categories. AP inclusion increased from 98.05% to 100%. Combined lists averaged 1.010 more categories in the group with the greatest information deficit than in the group with the least (95% interval 0.996-1.023). Conclusions: Even with extensive missing information and imbalanced diagnostic groups, clinical records can support informative prediction when uncertainty is quantified and expressed through sets of possible diagnoses rather than a single label. Joint assessment of list length and information deficit showed both the narrowing achieved and the records for which all three categories remained open.