Detection of new drug indications from electronic medical records

Dang, Tran-Thai; Ouankhamchan, Phetnidda; Ho, Tu Bao

doi:10.1109/rivf.2016.7800298

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…(5) lastly, the system presented in our paper was developed and is in use by medical researchers. EMR data can also be used to discover drug repurposing (Dang, Ouankhamchan, and Ho 2016;Xu et al 2015). This approach utilizes the large amounts of data collected on patients to discover correlations and connections between drugs and the medical parameters of the patients.…”

Section: Related Workmentioning

confidence: 99%

Separating Wheat from Chaff: Joining Biomedical Knowledge and Patient Data for Repurposing Medications

Nordon

Koren

Shalev

et al. 2019

AAAI

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We present a system that jointly harnesses large-scale electronic health records data and a concept graph mined from the medical literature to guide drug repurposing—the process of applying known drugs in new ways to treat diseases. Our study is unique in methods and scope, per the scale of the concept graph and the quantity of data. We harness 10 years of nation-wide medical records of more than 1.5 million people and extract medical knowledge from all of PubMed, the world’s largest corpus of online biomedical literature. We employ links on the concept graph to provide causal signals to prioritize candidate influences between medications and target diseases. We show results of the system on studies of drug repurposing for hypertension and diabetes. In both cases, we present drug families identified by the algorithm which were previously unknown. We verify the results via clinical expert opinion and by prospective clinical trials on hypertension.

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Section: Related Workmentioning

confidence: 99%

Separating Wheat from Chaff: Joining Biomedical Knowledge and Patient Data for Repurposing Medications

Nordon

Koren

Shalev

et al. 2019

AAAI

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“…For example, clusters of comorbidities have been used to identify population subtypes of autism, schizophrenia, and inflammatory bowel disease using EMRS data [33,34]. Unsupervised methods have been developed to identify new indications for existing medications -so called drug repurposing [35] -as well as to detect new adverse drug interactions that may put patients at risk [36]. In the area of health services utilization, unsupervised approaches can help detect patterns in the delivery of care that can inform future machine learning algorithm development [37].…”

Section: Hypothesis Generationmentioning

confidence: 99%

Grappling with the Future Use of Big Data for Translational Medicine and Clinical Care

Murphy¹,

Castro²,

Mandl³

2017

Yearb Med Inform

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SummaryObjectives: Although patients may have a wealth of imaging, genomic, monitoring, and personal device data, it has yet to be fully integrated into clinical care. Methods: We identify three reasons for the lack of integration. The first is that "Big Data" is poorly managed by most Electronic Medical Record Systems (EMRS). The data is mostly available on "cloud-native" platforms that are outside the scope of most EMRs, and even checking if such data is available on a patient often must be done outside the EMRS. The second reason is that extracting features from the Big Data that are relevant to healthcare often requires complex machine learning algorithms, such as determining if a genomic variant is protein-altering. The third reason is that applications that present Big Data need to be modified constantly to reflect the current state of knowledge, such as instructing when to order a new set of genomic tests. In some cases, applications need to be updated nightly. Results: A new architecture for EMRS is evolving which could unite Big Data, machine learning, and clinical care through a microservice-based architecture which can host applications focused on quite specific aspects of clinical care, such as managing cancer immunotherapy. Conclusion: Informatics innovation, medical research, and clinical care go hand in hand as we look to infuse science-based practice into healthcare. Innovative methods will lead to a new ecosystem of applications (Apps) interacting with healthcare providers to fulfill a promise that is still to be determined.

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“…Nearly 20% of drugs and 30% of chemotherapeutics are prescribed for non-FDA-approved indications 1 , 2 . This off-label prescribing is both common and costly—at $5 billion annually for just 10 drugs 2 —yet very poorly characterized 3 , 4 . Therefore, it is necessary to develop innovative ways to detect and aggregate information about off-label uses from existing data sources such as electronic health records 3 and patient data in social media 5 …”

Section: Introductionmentioning

confidence: 99%

Profiling off-label prescriptions in cancer treatment using social health networks

et al. 2019

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ObjectivesTo investigate using patient posts in social media as a resource to profile off-label prescriptions of cancer drugs.MethodsWe analyzed patient posts from the Inspire health forums (www.inspire.com) and extracted mentions of cancer drugs from the 14 most active cancer-type specific support groups. To quantify drug-disease associations, we calculated information component scores from the frequency of posts in each cancer-specific group with mentions of a given drug. We evaluated the results against three sources: manual review, Wolters-Kluwer Medi-span, and Truven MarketScan insurance claims.ResultsWe identified 279 frequently discussed and therefore highly associated drug-disease pairs from Inspire posts. Of these, 96 are FDA approved, 9 are known off-label uses, and 174 do not have records of known usage (potentially novel off-label uses). We achieved a mean average precision of 74.9% in identifying drug-disease pairs with a true indication association from patient posts and found consistent evidence in medical claims records. We achieved a recall of 69.2% in identifying known off-label drug uses (based on Wolters-Kluwer Medi-span) from patient posts.

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Detection of new drug indications from electronic medical records

Cited by 4 publications

References 21 publications

Separating Wheat from Chaff: Joining Biomedical Knowledge and Patient Data for Repurposing Medications

Separating Wheat from Chaff: Joining Biomedical Knowledge and Patient Data for Repurposing Medications

Grappling with the Future Use of Big Data for Translational Medicine and Clinical Care

Profiling off-label prescriptions in cancer treatment using social health networks

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