LabeledIn: Cataloging labeled indications for human drugs

Abstract

Drug-disease treatment relationships, i.e., which drug(s) are indicated to treat which disease(s), are among the most frequently sought information in PubMed®. Such information is useful for feeding the Google Knowledge Graph, designing computational methods to predict novel drug indications, and validating clinical information in EMRs. Given the importance and utility of this information, there have been several efforts to create repositories of drugs and their indications. However, existing resources are incomplete. Furthermore, they neither label indications in a structured way nor differentiate them by drug-specific properties such as dosage form, and thus do not support computer processing or semantic interoperability. More recently, several studies have proposed automatic methods to extract structured indications from drug descriptions; however, their performance is limited by natural language challenges in disease named entity recognition and indication selection. In response, we report LabeledIn: a human-reviewed, machine-readable and source-linked catalog of labeled indications for human drugs. More specifically, we describe our semi-automatic approach to derive LabeledIn from drug descriptions through human annotations with aids from automatic methods. As the data source, we use the drug labels (or package inserts) submitted to the FDA by drug manufacturers and made available in DailyMed. Our machine-assisted human annotation workflow comprises: (i) a grouping method to remove redundancy and identify representative drug labels to be used for human annotation, (ii) an automatic method to recognize and normalize mentions of diseases in drug labels as candidate indications, and (iii) a two-round annotation workflow for human experts to judge the pre-computed candidates and deliver the final gold standard. In this study, we focused on 250 highly accessed drugs in PubMed Health, a newly developed public web resource for consumers and clinicians on prevention and treatment of diseases. These 250 drugs corresponded to more than 8,000 drug labels (500 unique) in DailyMed in which 2,950 candidate indications were pre-tagged by an automatic tool. After being reviewed independently by two experts, 1,618 indications were selected, and additional 97 (missed by computer) were manually added, with an inter-annotator agreement of 88.35% as measured by the Kappa coefficient. Our final annotation results in LabeledIn consist of 7,805 drug-disease treatment relationships where drugs are represented as a triplet of ingredient, dose form, and strength. A systematic comparison of LabeledIn with an existing computer-derived resource revealed significant discrepancies, confirming the need to involve humans in the creation of such a resource. In addition, LabeledIn is unique in that it contains detailed textual context of the selected indications in drug labels, making it suitable for the development of advanced computational methods for the automatic extraction of indications from free text. Finally, motivated by the studies ...

Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals

“…28,29 Hypertension medications were determined using medication strings with indications determined as part of MedicationIndication resource-High Performance Subset (MEDI-HPS), which lists on-and off-label indications of medications (Supplemental Table 3). [30][31][32][33] We used the hypertensive blood pressure guideline thresholds of 140 mmHg systolic and 90 mmHg diastolic. We separated vital readings into outpatient and inpatient only and collapsed multiple daily readings to their median values.…”

Scaling drug indication curation through crowdsourcing

“…Towards such a goal, we recently created LabeledIn ( 27 ) based on manual curation of drug labels. To accelerate the manual curation process, we adopted a semiautomated pipeline where all disease occurrences are first tagged by a text-mining tool.…”

Extraction of Lactation Frames from Drug Labels and LactMed

“…On the other hand, Duke et al (2013) demonstrated the dangers of using drug labels as an ADR KB by identifying numerous inconsistencies between the labels for bioequivalent drugs. Meanwhile, drug indications (i.e., the medical condition the drug is intended to treat) have also been well-studied (Névéol and Lu, 2010;Fung et al, 2013;Khare et al, 2014), as have drug interactions (Demner-Fushman et al, 2018a). All of these focus on general aspects of a drug, while hardly any work has focused on the information in drug labels related to specific populations, though both the TAC task as well as Culbertson et al (2014) identified ADRpopulation relations.…”