Anomaly detection as a tool for identifying cases of interest in postmortem toxicology

Intoxication-induced death, both intentional and unintentional, is a global public health concern, contributing substantially to mortality in many regions. Postmortem toxicological interpretation typically relies on reference concentrations of individual substances, which fails to account for combinatorial effects when multiple drugs are present. More comprehensive approaches that analyse multiple substances simultaneously could improve diagnostic accuracy. This study applied computational anomaly detection methods to identify toxicological profiles deviating from known non-intoxication patterns and assessed whether such deviations correspond to fatal intoxications or could serve as a screening tool for cases requiring further review. Postmortem femoral blood drug concentrations covering 191 substances from 16,710 forensic autopsies were analyzed. Cases were divided into a training set of non-intoxications (n = 8320) and separate validation (n = 2674) and test sets (n = 3342) containing both intoxications and non-intoxications. A machine learning-based anomaly detection model was trained and optimized before being applied to the test set. The model distinguished intoxications from normal patterns with 83.9% accuracy. False negatives often involved opioids and ethanol, while false positives were associated with rare substances, numerous detections, or high concentrations. Overall, this approach demonstrates strong potential as a screening tool for identifying toxicological patterns of forensic interest.