Internship report: Using AI to personalise medical questionnaires
September 1st, 2021Personalising cancer patient questionnaires with AI. PRO-CTCAE is a patient-reported outcome system for evaluating symptomatic toxicity in cancer clinical trials. The library includes 124 items representing 78 symptomatic toxicities. Patients fill these in weekly during key treatment periods. The research objective: instead of a one-size-fits-all selection of questions, use AI to recommend the 10 most relevant questions per patient — those that matter most and are least known.
Approach: clustering and recommendation
Working with a dataset of 35 lung cancer patients (544 rows × 55 columns), intern Usman Dankoly applied K-means clustering on tumor classification and WHO scores, identifying two distinct patient clusters. The importance of each question was calculated by frequency of non-zero composite scores, while uncertainty was measured via standard deviation within clusters and across time.
Personalised results
Individual recommendation scores combined importance and uncertainty as tunable hyperparameters. In early treatment phases, more weight goes to uncertainty (what we know least). In later phases, more weight goes to importance (what matters most for this specific patient). This reduces questionnaire burden while maintaining clinical relevance. — Usman Dankoly, EHB Brussel
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