Identification of Adverse Drug Events from Free Text Electronic Patient Records and Information in a Large Mental Health Case Register

Iqbal, Ehtesham; Mallah, Robbie; Jackson, Roy; Ball, Michael; Ibrahim, Zina; Broadbent, Matthew; Dzahini, Olubanke; Stewart, Robert; Johnston, Caroline; Dobson, Richard

doi:10.1371/journal.pone.0134208

Cited by 66 publications

(52 citation statements)

References 37 publications

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“…We and others have used the EHR in pharmacoepidemiology studies to identify novel adverse effects of psychotropic medications. [Blumenthal and others 2014] [Castro and others 2013; Castro and others 2012; Castro and others 2016a; Castro and others 2016b; Clements and others 2014; Gallagher and others 2012; Iqbal and others 2015; Tatonetti and others 2011]. To date, however, few studies have capitalized on the availability of psychotropic drug response data in the EHR for genetic research.…”

Section: Future Directions and Opportunitiesmentioning

confidence: 99%

The use of electronic health records for psychiatric phenotyping and genomics

Smoller

2017

American J of Med Genetics Pt B

102

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The widespread adoption of electronic health record (EHRs) in healthcare systems has created a vast and continuously growing resource of clinical data and provides new opportunities for population-based research. In particular, the linking of EHRs to biospecimens and genomic data in biobanks may help address what has become a rate-limiting study for genetic research: the need for large sample sizes. The principal roadblock to capitalizing on these resources is the need to establish the validity of phenotypes extracted from the EHR. For psychiatric genetic research, this represents a particular challenge given that diagnosis is based on patient reports and clinician observations that may not be well-captured in billing codes or narrative records. This review addresses the opportunities and pitfalls in EHR-based phenotyping with a focus on their application to psychiatric genetic research. A growing number of studies have demonstrated that diagnostic algorithms with high positive predictive value can be derived from EHRs, especially when structured data are supplemented by text mining approaches. Such algorithms enable semi-automated phenotyping for large-scale case-control studies. In addition, the scale and scope of EHR databases have been used successfully to identify phenotypic subgroups and derive algorithms for longitudinal risk prediction. EHR-based genomics are particularly well-suited to rapid look-up replication of putative risk genes, studies of pleiotropy (phenomewide association studies or PheWAS), investigations of genetic networks and overlap across the phenome, and pharmacogenomic research. EHR phenotyping has been relatively under-utilized in psychiatric genomic research but may become a key component of efforts to advance precision psychiatry.

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Section: Future Directions and Opportunitiesmentioning

confidence: 99%

The use of electronic health records for psychiatric phenotyping and genomics

Smoller

2017

American J of Med Genetics Pt B

102

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“…The ADR spans we output can serve as an input for ADR normalization, giving medical experts a consolidated summary of the reported adverse events. Iqbal et al (2015) share our motivation to identify ADR mentions in the context of electronic health records (medical correspondence, discharge letters, etc. ), which are more formal, as opposed to our focus on social media domain.…”

Section: Related Workmentioning

confidence: 99%

Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models

Stanovsky¹,

Gruhl²,

Mendes³

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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Recognizing mentions of Adverse Drug Reactions (ADR) in social media is challenging: ADR mentions are contextdependent and include long, varied and unconventional descriptions as compared to more formal medical symptom terminology. We use the CADEC corpus to train a recurrent neural network (RNN) transducer, integrated with knowledge graph embeddings of DBpedia, and show the resulting model to be highly accurate (93.4 F1). Furthermore, even when lacking high quality expert annotations, we show that by employing an active learning technique and using purpose built annotation tools, we can train the RNN to perform well (83.9 F1).

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“…SLaM serves over 1.2 million patients and stores much of its clinical records and prescribing information in unstructured free text format. The ADR and drug episode information were extracted using an in-house developed natural language processing tools in conjunction with manual annotation [2] In this preliminary study, we focus on three types of adverse events identified in the CRIS registry: dry mouth (#event 58,347), constipation (#event 86,602) and enuresis (#93,366), which involves 20,795 distinct patients with mental health disorders. For each identified AE, we pick up the patient's past 30 days medication episodes.…”

Section: Data Source and Preparationmentioning

confidence: 99%

Encoding Medication Episodes for Adverse Drug Event Prediction

Ibrahim

Iqbal

et al. 2016

Research and Development in Intelligent Systems XXXIII

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Understanding the interplay among the multiple factors leading to Adverse Drug Reactions (ADRs) is crucial to increasing drug effectiveness, individualising drug therapy and reducing incurred cost. In this paper, we propose a flexible encoding mechanism that can effectively capture the dynamics of multiple medication episodes of a patient at any given time. We enrich the encoding with a drug ontology and patient demographics data and use it as a base for an ADR prediction model. We evaluate the resulting predictive approach under different settings using real anonymised patient data obtained from the EHR of the South London and Maudsley (SLaM), the largest mental health provider in Europe. Using the profiles of 38,000 mental health patients, we identified 240,000 affirmative mentions of dry mouth, constipation and enuresis and 44,000 negative ones. Our approach achieved 93% prediction accuracy and 93% F-Measure.

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Identification of Adverse Drug Events from Free Text Electronic Patient Records and Information in a Large Mental Health Case Register

Cited by 66 publications

References 37 publications

The use of electronic health records for psychiatric phenotyping and genomics

The use of electronic health records for psychiatric phenotyping and genomics

Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models

Encoding Medication Episodes for Adverse Drug Event Prediction

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