Using Temporal Patterns in Medical Records to Discern Adverse Drug Events from Indications

Researchers estimate that electronic health record systems record roughly 2-million ambulatory adverse drug events and that patients suffer from adverse drug events in roughly 30% of hospital stays. Some have used structured databases of patient medical records and health insurance claims recently—going beyond the current paradigm of using spontaneous reporting systems like AERS—to detect drug-safety signals. However, most efforts do not use the free-text from clinical notes in monitoring for drug-safety signals. We hypothesize that drug–disease co-occurrences, extracted from ontology-based annotations of the clinical notes, can be examined for statistical enrichment and used for drug safety surveillance. When analyzing such co-occurrences of drugs and diseases, one major challenge is to differentiate whether the disease in a drug–disease pair represents an indication or an adverse event. We demonstrate that it is possible to make this distinction by combining the frequency distribution of the drug, the disease, and the drug-disease pair as well as the temporal ordering of the drugs and diseases in each pair across more than one million patients.