Development and evaluation of an ensemble resource linking medications to their indications

Wei, Wei Qi; Cronin, Robert M.; Xu, Hua; Lasko, Thomas A.; Bastarache, Lisa; Denny, Joshua C.

doi:10.1136/amiajnl-2012-001431

Cited by 90 publications

(89 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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.…”

Section: Input Feature Developmentmentioning

confidence: 99%

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

Teixeira

Wei

Cronin

et al. 2016

Journal of the American Medical Informatics Association

Self Cite

View full text Add to dashboard Cite

Objective: Phenotyping algorithms applied to electronic health record (EHR) data enable investigators to identify large cohorts for clinical and genomic research. Algorithm development is often iterative, depends on fallible investigator intuition, and is time-and labor-intensive. We developed and evaluated 4 types of phenotyping algorithms and categories of EHR information to identify hypertensive individuals and controls and provide a portable module for implementation at other sites. Materials and Methods: We reviewed the EHRs of 631 individuals followed at Vanderbilt for hypertension status. We developed features and phenotyping algorithms of increasing complexity. Input categories included International Classification of Diseases, Ninth Revision (ICD9) codes, medications, vital signs, narrative-text search results, and Unified Medical Language System (UMLS) concepts extracted using natural language processing (NLP). We developed a module and tested portability by replicating 10 of the best-performing algorithms at the Marshfield Clinic. Results: Random forests using billing codes, medications, vitals, and concepts had the best performance with a median area under the receiver operator characteristic curve (AUC) of 0.976. Normalized sums of all 4 categories also performed well (0.959 AUC). The best non-NLP algorithm combined normalized ICD9 codes, medications, and blood pressure readings with a median AUC of 0.948. Blood pressure cutoffs or ICD9 code counts alone had AUCs of 0.854 and 0.908, respectively. Marshfield Clinic results were similar. Conclusion: This work shows that billing codes or blood pressure readings alone yield good hypertension classification performance. However, even simple combinations of input categories improve performance. The most complex algorithms classified hypertension with excellent recall and precision.

show abstract

Section: Input Feature Developmentmentioning

confidence: 99%

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

Teixeira

Wei

Cronin

et al. 2016

Journal of the American Medical Informatics Association

Self Cite

View full text Add to dashboard Cite

show abstract

“…Second, LabeledIn only contains labeled/marketed indications. On the other hand, an existing resource MEDI 26 provides computable information regarding off-label indications from Wikipedia and MedlinePlus, in addition to labeled indications from SIDER 2 and NDF-RT. Hence, in the future we plan to investigate ways to integrate our results with existing resources such as MEDI.…”

Section: Discussionmentioning

confidence: 99%

“…Neveol and Lu 24 used text mining techniques to extract indications from FDA drug labels, and automatically extract 2,200 relationships between 1,263 ingredients and 581 diseases using SemRep 25 with precision of 73%. Wei and colleagues 26 created an ensemble indication resource called MEDI by integrating information from four resources: SIDER 2, NDF-RT, MedlinePlus, and Wikipedia. A subset of MEDI was sampled and reviewed by two physicians in two rounds to further determine the automatic inclusion strategy for a high precision dataset.…”

Section: Introductionmentioning

confidence: 99%

“…As found in the abovementioned studies 21, 24, 26 , automatic methods alone are not yet sufficient to deliver a gold standard due to the challenges in natural language processing (NLP), including: (a) the difficulties with automatic disease recognition and normalization 30–32 , and (b) the presence of disease mentions other than indications in drug labels. To illustrate these, Table 1 contains the indication fields of two sample drug labels (dl 1 and dl 2 : same ingredient but different dose forms) in DailyMed 22 , which houses the most up-to-date drug labels submitted to the FDA by drug manufacturers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

LabeledIn: Cataloging labeled indications for human drugs

Khare

2014

Journal of Biomedical Informatics

View full text Add to dashboard Cite

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 ...

show abstract

“…We collect known uses of drugs from MEDI database [32], which is an ensemble medication indication resource based on multiple commonly used medication resources (e.g., RxNorm, MedlinePlus, and Wikipedia). Indications in MEDI are coded as International Classification of Diseases, 9th edition (ICD9) codes.…”

Section: Experiments 41 Data Descriptionmentioning

confidence: 99%

Computational Drug Discovery with Dyadic Positive-Unlabeled Learning

Liu¹,

Qiu²,

Zhang³

et al. 2017

Proceedings of the 2017 SIAM International Conference on Data Mining

View full text Add to dashboard Cite

Computational Drug Discovery, which uses computational techniques to facilitate and improve the drug discovery process, has aroused considerable interests in recent years. Drug Repositioning (DR) and DrugDrug Interaction (DDI) prediction are two key problems in drug discovery and many computational techniques have been proposed for them in the last decade. Although these two problems have mostly been researched separately in the past, both DR and DDI can be formulated as the problem of detecting positive interactions between data entities (DR is between drug and disease, and DDI is between pairwise drugs). The challenge in both problems is that we can only observe a very small portion of positive interactions. In this paper, we propose a novel framework called Dyadic PositiveUnlabeled learning (DyPU) to solve the problem of detecting positive interactions. DyPU forces positive data pairs to rank higher than the average score of unlabeled data pairs. Moreover, we also derive the dual formulation of the proposed method with the rectifier scoring function and we show that the associated non-trivial proximal operator admits a closed form solution. Extensive experiments are conducted on real drug data sets and the results show that our method achieves superior performance comparing with the state-of-the-art.

show abstract

Development and evaluation of an ensemble resource linking medications to their indications

Cited by 90 publications

References 40 publications

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

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

LabeledIn: Cataloging labeled indications for human drugs

Computational Drug Discovery with Dyadic Positive-Unlabeled Learning

Contact Info

Product

Resources

About