title: Automated Detection of Systematic Off-label Drug Use in Free Text of Electronic Medical Records.
abstract: Off-label use of a drug occurs when it is used in a manner that deviates from its FDA label. Studies estimate that 21% of prescriptions are off-label, with only 27% of those uses supported by evidence of safety and efficacy. We have developed methods to detect population level off-label usage using computationally efficient annotation of free text from clinical notes to generate features encoding empirical information about drug-disease mentions. By including additional features encoding prior knowledge about drugs, diseases, and known usage, we trained a highly accurate predictive model that was used to detect novel candidate off-label usages in a very large clinical corpus. We show that the candidate uses are plausible and can be prioritized for further analysis in terms of safety and efficacy.
authors.affiliation: Stanford University, Stanford, CA.
authors.forename: Kenneth
authors.initials: K
authors.lastname: Jung
authors.forename: Paea
authors.initials: P
authors.lastname: Lependu
authors.forename: Nigam
authors.initials: N
authors.lastname: Shah