MedXN: an open source medication extraction and normalization tool for clinical text

Sohn, Sunghwan; Clark, Cheryl; Halgrim, Scott; Murphy, Seán; Chute, Christopher G.; Liu, Hongfang

doi:10.1136/amiajnl-2013-002190

Cited by 76 publications

(61 citation statements)

References 35 publications

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“…The following five systems were eventually included in the evaluation. All of them are either open-source software or are freely available under academic licenses: BioMEDICUS (The BioMedical Information Collection and Understanding System) 19 CliNER (The Clinical Named Entity Recognition System) 20 MedEx (Medication Information Extraction System) 21 MedXN (Medication Extraction and Normalization) 22 MIST (The MITRE Identification Scrubber Toolkit) 23 …”

Section: Resultsmentioning

confidence: 99%

Ease of adoption of clinical natural language processing software: An evaluation of five systems

Zheng

Vydiswaran

Liu

et al. 2015

Journal of Biomedical Informatics

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Objective In recognition of potential barriers that may inhibit the widespread adoption of biomedical software, the 2014 i2b2 Challenge introduced a special track, Track 3—Software Usability Assessment, in order to develop a better understanding of the adoption issues that might be associated with the state-of-the-art clinical NLP systems. This paper reports the ease of adoption assessment methods we developed for this track, and the results of evaluating five clinical NLP system submissions. Materials and Methods A team of human evaluators performed a series of scripted adoptability test tasks with each of the participating systems. The evaluation team consisted of four “expert evaluators” with training in computer science, and eight “end user evaluators” with mixed backgrounds in medicine, nursing, pharmacy, and health informatics. We assessed how easy it is to adopt the submitted systems along the following three dimensions: communication effectiveness (i.e., how effective a system is in communicating its designed objectives to intended audience), effort required to install, and effort required to use. We used a formal software usability testing tool, TURF, to record the evaluators’ interactions with the systems and ‘think-aloud’ data revealing their thought processes when installing and using the systems and when resolving unexpected issues. Results Overall, the ease of adoption ratings that the five systems received are unsatisfactory. Installation of some of the systems proved to be rather difficult, and some systems failed to adequately communicate their designed objectives to intended adopters. Further, the average ratings provided by the end user evaluators on ease of use and ease of interpreting output are −0.35 and −0.53, respectively, indicating that this group of users generally deemed the systems extremely difficult to work with. While the ratings provided by the expert evaluators are higher, 0.6 and 0.45, respectively, these ratings are still low indicating that they also had considerable struggles. Discussion The results of the Track 3 evaluation show that the adoptability of the five participating clinical NLP systems has a great margin for improvement. Remedy strategies suggested by the evaluators included (1) more detailed and operation system specific use instructions; (2) provision of more pertinent onscreen feedback for easier diagnosis of problems; (3) including screen walk-throughs in use instructions so users know what to expect and what might have gone wrong; (4) avoiding jargon and acronyms in materials intended for end users; and (5) packaging prerequisites required within software distributions so that prospective adopters of the software do not have to obtain each of the third-party components on their own.

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Section: Resultsmentioning

confidence: 99%

Ease of adoption of clinical natural language processing software: An evaluation of five systems

Zheng

Vydiswaran

Liu

et al. 2015

Journal of Biomedical Informatics

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“…Also, there has been a study to automatically identify keywords related to post-surgical complications through sublanguage analysis instead of manual process by subject matter experts (12). In other studies, recent advances in NLP have produced promising results in text analytics from clinical text (16) and have been successfully applied in various clinical applications including medication information extraction (17), patient medical status identification (18–20), sentiment analysis (21), decision support (22, 23), genomewide association studies (24, 25), and diagnosis code assignment (26, 27). …”

Section: Discussionmentioning

confidence: 99%

Detection of clinically important colorectal surgical site infection using Bayesian network

Sohn

Larson

Habermann

et al. 2017

Journal of Surgical Research

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Structured Abstract Background Despite extensive efforts to monitor and prevent surgical site infections (SSIs), real-time surveillance of clinical practice has been sparse and expensive or non-existent. However, natural language processing (NLP) and machine learning (i.e., Bayesian network analysis) may provide the methodology necessary to approach this issue in a new way. We investigated the ability to identify SSIs following colorectal surgery (CRS) through an automated detection system using a Bayesian network. Materials and Methods Patients who underwent CRS from 2010 to 2012 and were captured in our institutional American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) comprised our cohort. A Bayesian network was applied to detect SSIs using risk factors from ACS-NSQIP data and keywords extracted from clinical notes by NLP. Two surgeons provided expertise informing the Bayesian network to identify clinically meaningful SSIs (CM-SSIs) occurring within 30 days after surgery. Results We utilized data from 751 CRS cases experiencing 67 (8.9%) SSIs and 78 (10.4%) CM-SSIs. Our Bayesian network detected ACS-NSQIP-captured SSIs with an ROC area under the curve of 0.827, but this value increased to 0.892 when using surgeon-identified CM-SSIs. Conclusions A Bayesian network coupled with NLP has the potential to be used in real-time SSI surveillance. Moreover, surgeons identified CM-SSI not captured under current NSQIP definitions. Future efforts to expand CM-SSI identification may lead to improved and potentially automated approaches to survey for post-operative SSI in clinical practice.

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“…The Medication Extraction and Normalization (MedXN) system was designed to extract medication information from clinical notes and convert it into an RxNorm concept unique identifier (RxCUI). [7] This system identifies medication names and attributes, such as dosage, strength, and frequency, in clinical notes using the RxNorm dictionary and regular expressions. The attributes associated with a medication name are combined together in the RxNorm standard order and normalized to a specific RxCUI.…”

Section: Medxnmentioning

confidence: 99%

“…[4] Their system extracted medication dosage information with about 80% accuracy. More recently, MedEx [5], CLAMP [6], MedXN [7], and medExtractR [8], have been developed to extract medication information from EHRs more accurately, which is useful for drugbased studies.…”

Section: Introductionmentioning

confidence: 99%

A post-processing algorithm for building longitudinal medication dose data from extracted medication information using natural language processing from electronic health records

McNeer

Beck

Weeks

et al. 2019

Preprint

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ObjectiveWe developed a post-processing algorithm to convert raw natural language processing output from electronic health records into a usable format for analysis. This algorithm was specifically developed for creating datasets that can be used for medication-based studies. Materials and MethodsThe algorithm was developed using output from two natural language processing systems, MedXN and medExtractR. We extracted medication information from deidentified clinical notes from Vanderbilt's electronic health record system for two medications, tacrolimus and lamotrigine, which have widely different prescribing patterns. The algorithm consists of two parts. Part I parses the raw output and connects entities together and Part II removes redundancies and calculates dose intake and daily dose. We evaluated both parts of the algorithm by comparing to gold standards that were generated using approximately 300 records from 10 subjects for both medications and both NLP systems. ResultsBoth parts of the algorithm performed well. For MedXN, the F-measures for Part I were at or above 0.94 and for Part II they were at or above 0.98. For medExtractR the Fmeasures for Part I were at or above 0.98 and for Part II they were at or above 0.91. DiscussionOur post-processing algorithm is useful for drug-based studies because it converts NLP output to analyzable data. It performed well, although it cannot handle highly complicated cases, which usually occurred when a NLP incorrectly extracted dose information. Future work will focus on identifying the most likely correct dose when conflicting doses are extracted on the same day. Part I Output:ID6_2015-12-28_note1.txt Lamictal 80 mcg 1 ID6_2015-12-28_note1.txt Lamictal 4.5 mcg

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MedXN: an open source medication extraction and normalization tool for clinical text

Cited by 76 publications

References 35 publications

Ease of adoption of clinical natural language processing software: An evaluation of five systems

Ease of adoption of clinical natural language processing software: An evaluation of five systems

Detection of clinically important colorectal surgical site infection using Bayesian network

A post-processing algorithm for building longitudinal medication dose data from extracted medication information using natural language processing from electronic health records

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