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Adverse events (AEs) and concomitant medications (CMs) underreporting remains a recurrent issue in clinical trials. This study aimed to build a mapping relationship between the AE and CM using a random forest (RF) model that can be embedded in the current Electronic Data Capture (EDC) system, to enable a reliable detection of underreporting AE or CM. Current data including 524 AEs and 684 CMs for 13,905 individuals, were taken from three cancer clinical trials and six other studies that are non-public. An additional validation dataset was consisted of 448 independent patients with 269 unique AEs and 407 CMs from a cancer clinical trial on Paclitaxel. We found that the machine learning method exhibited in a learning task between AE and CM well for a common AE like hypertension that was caused by a single cause, but it was ineffective when the AE, such as the increased blood alkaline phosphatase, was caused by complex reasons or just an associated symptom of some diseases. This study suggests the potential of automatically detecting the underreported AE and CM in detail, and it will improve further safety and validity inspections from clinical trials. ABBREVIATIONS:  AE, adverse event; ATC, anatomical therapeutic chemical; CM, concomitant medication; EDC, electronic data capture; FDA, Food and Drug Administration; GCP, good clinical practice; MedDRA, medical dictionary for regulatory activities; NMPA, National Medical Products Administration; PT, preferred term; RCT, randomized controlled trial; RF, random forests; SDV, source data verification; VIM, variable importance measure

Identifying the association rules between adverse events and concomitant medicines in clinical trial data management using random forest