MedEx: a medication information extraction system for clinical narratives

Xu, Hua; Stenner, Shane P.; Doan, Son; Johnson, Kevin B.; Waitman, Lemuel R.; Denny, Joshua C.

doi:10.1197/jamia.m3378

Cited by 425 publications

(321 citation statements)

References 18 publications

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“…Medications were available from structured electronic prescribing records and also extracted from narrative documents using MedEx. 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.…”

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

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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.

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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

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“…At the level of patient care, integration of genotyping order template and/or genotype result into a robust system of electronic medical record (EMR) with popup action alert and order templates for actionable pharmacogenomic tests to be used by physicians will be necessary [113,114]. At the level of research, the health information technology would enable organizational management of all research data and accessibility by the EMR [115][116][117][118]. Both the patient care-and research-level informatics should incorporate updated information when available and be linked to other health informatics such as billing, clinical laboratory, and clinical trials within the healthcare facility.…”

Section: The Multifacet Process Of Clinicalmentioning

confidence: 99%

Scientific Challenges and Implementation Barriers to Translation of Pharmacogenomics in Clinical Practice

Lam¹

2014

Omics in Clinical Practice

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The mapping of the human genome and subsequent advancements in genetic technology had provided clinicians and scientists an understanding of the genetic basis of altered drug pharmacokinetics and pharmacodynamics, as well as some examples of applying genomic data in clinical practice. This has raised the public expectation that predicting patients' responses to drug therapy is now possible in every therapeutic area, and personalized drug therapy would come sooner than later. However, debate continues among most stakeholders involved in drug development and clinical decision-making on whether pharmacogenomic biomarkers should be used in patient assessment, as well as when and in whom to use the biomarker-based diagnostic tests. Currently, most would agree that achieving the goal of personalized therapy remains years, if not decades, away. Realistic application of genomic findings and technologies in clinical practice and drug development require addressing multiple logistics and challenges that go beyond discovery of gene variants and/or completion of prospective controlled clinical trials. The goal of personalized medicine can only be achieved when all stakeholders in the field work together, with willingness to accept occasional paradigm change in their current approach.

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“…Xu, et al [10] developed a system called MedEx. The system extracts medication names and signatures (e.g., dose, route, and frequency) from clinical narratives.…”

Section: Sentence Selection Summarizationmentioning

confidence: 99%

Template based Medical Reports Summarization

El-Qumsan¹,

El-Halees²

2018

IJCA

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The torrential information in the medical records is considered a great problem because it difficult to distinguish the needed and necessary information from the huge quantity of data. As a result, the importance of summarize medical reports is growing day after day. Medical information extraction is one of the important topics that aim to identify medical information and detect hidden relations. This topic is considered one of the most important topics in the field of text mining where is used to process unstructured texts and extract meaningful information which is hidden in the unstructured texts.The information extracted from medical reports is very useful to medical staff to detect hidden relations between medical information, and making decisions that will improve the medical service for patients, in addition to saving time and effort.In our paper, an approach that use template based medical reports summarization has been developed to transfer medical reports from semi structured and unstructured form to structured form. It classifies the identified entities then extracts important information such as diseases, medical procedures, and drugs. After that, it can discovery hidden relationship between medical information by using association rules. The dataset used in this paper was collected from the Palestinian Ministry of Health.To evaluate the performance and effectiveness of our model, human expert has been used as a reference to measure the degree of acceptance of the extracted association rules which have been extracted from the dataset. So, Likert's scale has been used for evaluation. After the data analysis obtained from the questionnaire. It shows us that the proportion of accuracy association rules, which have been extracted is about 80%.

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MedEx: a medication information extraction system for clinical narratives

Cited by 425 publications

References 18 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

Scientific Challenges and Implementation Barriers to Translation of Pharmacogenomics in Clinical Practice

Template based Medical Reports Summarization

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