Mining Adverse Drug Reactions from Electronic Health Records

Lo, Henry Z.; Ding, Wei; Nazeri, Zohreh

doi:10.1109/icdmw.2013.43

Cited by 4 publications

(6 citation statements)

References 15 publications

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“…, free to access) knowledge bases; access to proprietary databases may be cost-prohibitive and difficult to maintain long-term. Our study seeks to build on the multiple in silico studies [12–25,28–30,65–72] that have evaluated the CTE relationship by integrating heterogeneous data resources bridging molecular pharmacology through clinical surveillance in scope, and ranging from traditional biochemical studies through text mining ( e.g. , ChemoText [42]) in origin of assertions.…”

Section: Discussionmentioning

confidence: 99%

“…Case reports may or may not be able to discern a mechanistic explanation for observed drug/ADE associations. Mining of electronic health record (EHR) data has proven fruitful for establishing ADEs [28], clinical trajectories [29], and other endpoints [30], though underlying mechanisms for these outcomes can be challenging to elucidate through this approach. Thus, additional approaches for detecting, validating, or identifying mechanisms behind ADEs should be sought.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions

Sedykh

Fourches

et al. 2018

Drug Saf

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The presented workflow, based on free-access databases and an association-based inference scheme, provided novel C-E relationships that have been validated post hoc in case reports. With refinement of prioritization schemes for the generated C-E inferences, this workflow may provide an effective computational method for the early detection of potential drug candidate ADEs that can be followed by targeted experimental investigations.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions

Sedykh

Fourches

et al. 2018

Drug Saf

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“…Lo et al [50] proposed an algorithm to facilitate detecting adverse drug reaction (ADR) using EHRs. This detection was done by using detection methods.…”

Section: Diseases Treatmentsmentioning

confidence: 99%

“…The future directions are that proposed method needs more extension for detecting required causes and effects associations in other longitudinal data types and deal with multiple causes and effects. Finally, it is required to select observation methods automatically [50].…”

mentioning

confidence: 99%

Electronic Health Records: Applications, Techniques and Challenges

Zaied¹,

Elmogy²,

Elkader³

2015

IJCA

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With the fast evolution of information technology, traditional healthcare is moving towards a more electronic stage. As a result, the e-Health term appears, and Electronic Health Records (EHRs) become a critical application of e-Health management systems. These computerized records have been widely used by clinicians, healthcare providers, patients, and health insurance companies for the purpose of creating, managing, and accessing the health information of patients everywhere. Moreover, these information resources can be shared by different healthcare parties for monitoring the patients' health, delivering effective treatments, and decreasing costs. In this paper, we present a survey of various EHR applications in e-Health systems. These applications include using EHRs for diagnosing, monitoring diseases, and selecting the most efficient paths of treatments. In addition, we discuss the usage of EHRs as a source for building a knowledge base for Clinical Decision Support Systems (CDSS). Finally, the challenges of EHR implementations in the healthcare environment and current research topics will be highlighted.

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“…estimated to send 701,547 patients to emergency rooms, hospitalize 117,318 patients each year [2], account for 2-6% of all hospital admissions, and increase the duration of hospital stays and costs [3]. Moreover, over 2 million serious side effects, including 100,000 deaths, occur due to ADRs [4]. In other words, ADRs strain healthcare resources and thus have strong implications for public health [5].Pharmacovigilance involves the detection, assessment, understanding, and prevention of ADRs [1] that were previously either unknown or poorly understood.…”

mentioning

confidence: 99%

Mining Sequential Patterns of Diseases Contracted and Medications Prescribed before the Development of Stevens-Johnson Syndrome in Taiwan

et al. 2019

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Medication is designed to cure diseases, but serious risks can arise from severe adverse drug reactions (ADRs). ADRs can lead to emergency room visits and hospitalization, straining healthcare resources and, thus, they have strong implications for public health. Stevens–Johnson Syndrome (SJS) is one ADR and comprises the highest proportion of all drug relief cases in Taiwan. Pharmacovigilance involves the collection, detection, assessment, monitoring, and prevention of ADRs, including SJS. Most medical specialists are not fully aware of the risk of drug-induced SJS. Consequently, various drugs may be prescribed to susceptible patients for a great variety of diseases and, in turn, cause SJS. In this research, medical records of SJS patients were retrieved from the Taiwan National Health Insurance Research Database, and the Generalized Sequential Patterns (GSP) algorithm was used to find the sequential patterns of diseases before SJS onset. Then we mined the sequential patterns of medications prescribed in each disease pattern. Afterwards, we detected significant associations of each pattern of diseases and medications prescribed among age groups with statistical analysis. We found that, first, most patients developed SJS after being prescribed the causative medications fewer than four times. Second, Respiratory System Diseases (RSDs) appeared in disease sequential patterns of all lengths. Patterns involving RSDs were more frequent than others. Third, NSAIDs, H2-antagonists for peptic ulcer, penicillin antibiotics, theophylline bronchodilators, and cephalosporin antibiotics were the most frequent medications prescribed. Fourth, we found that patients in certain age groups had higher risks of developing SJS. This study aimed to mine the sequential patterns of diseases contracted and medications prescribed before patients developed SJS in Taiwan. This useful information can be provided to physicians so that they can stop the administration of suspected drugs to avoid evolution towards more severe cases.

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Mining Adverse Drug Reactions from Electronic Health Records

Cited by 4 publications

References 15 publications

Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions

Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions

Electronic Health Records: Applications, Techniques and Challenges

Mining Sequential Patterns of Diseases Contracted and Medications Prescribed before the Development of Stevens-Johnson Syndrome in Taiwan

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