Integrating existing natural language processing tools for medication extraction from discharge summaries

Doan, Son; Bastarache, Lisa; Klimkowski, Sergio; Denny, Joshua C.; Xu, Hua

doi:10.1136/jamia.2010.003855

Cited by 61 publications

(36 citation statements)

References 5 publications

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“…More recently, i2b2 (the Center of Informatics for Integrating Biology and the Bedside) at Partners Health Care System has organized a few clinical NLP challenges that aimed to recognize clinical entities from text, including the 2009 challenge on medication recognition [9] and the 2010 i2b2 challenge on recognizing medical problems, treatments, and tests entities [10]. In the 2009 challenge, both rule-based [11,12] and machine learning based methods [13,14], as well as hybrid methods [15] have been developed to extract medication entities. In the 2010 i2b2 NLP challenge, organizers provided more annotated data.…”

Section: Introductionmentioning

confidence: 99%

Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features

Tang

Jiang

et al. 2013

BMC Med Inform Decis Mak

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BackgroundNamed entity recognition (NER) is an important task in clinical natural language processing (NLP) research. Machine learning (ML) based NER methods have shown good performance in recognizing entities in clinical text. Algorithms and features are two important factors that largely affect the performance of ML-based NER systems. Conditional Random Fields (CRFs), a sequential labelling algorithm, and Support Vector Machines (SVMs), which is based on large margin theory, are two typical machine learning algorithms that have been widely applied to clinical NER tasks. For features, syntactic and semantic information of context words has often been used in clinical NER systems. However, Structural Support Vector Machines (SSVMs), an algorithm that combines the advantages of both CRFs and SVMs, and word representation features, which contain word-level back-off information over large unlabelled corpus by unsupervised algorithms, have not been extensively investigated for clinical text processing. Therefore, the primary goal of this study is to evaluate the use of SSVMs and word representation features in clinical NER tasks.MethodsIn this study, we developed SSVMs-based NER systems to recognize clinical entities in hospital discharge summaries, using the data set from the concept extration task in the 2010 i2b2 NLP challenge. We compared the performance of CRFs and SSVMs-based NER classifiers with the same feature sets. Furthermore, we extracted two different types of word representation features (clustering-based representation features and distributional representation features) and integrated them with the SSVMs-based clinical NER system. We then reported the performance of SSVM-based NER systems with different types of word representation features.Results and discussionUsing the same training (N = 27,837) and test (N = 45,009) sets in the challenge, our evaluation showed that the SSVMs-based NER systems achieved better performance than the CRFs-based systems for clinical entity recognition, when same features were used. Both types of word representation features (clustering-based and distributional representations) improved the performance of ML-based NER systems. By combining two different types of word representation features together with SSVMs, our system achieved a highest F-measure of 85.82%, which outperformed the best system reported in the challenge by 0.6%. Our results show that SSVMs is a great potential algorithm for clinical NLP research, and both types of unsupervised word representation features are beneficial to clinical NER tasks.

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Section: Introductionmentioning

confidence: 99%

Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features

Tang

Jiang

et al. 2013

BMC Med Inform Decis Mak

Self Cite

View full text Add to dashboard Cite

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“…Several NLP tools were developed to extract main medication information (e.g., strength and frequency) . The detailed discussion of their techniques is beyond the scope of this paper, but the referenced studies simply focused on extraction of medication content and did not standardize the extracted information to support calculations of AWD.…”

Section: Discussionmentioning

confidence: 99%

“…A built‐in rule‐based regular expression dictionary is the other feature of the SIG extractor. The SIG extractor was evaluated by AWD, unlike previous studies that evaluated their extraction work by medication components . The decision was made because multiple pathways could be used to calculate AWD.…”

Section: Discussionmentioning

confidence: 99%

The use of natural language processing on narrative medication schedules to compute average weekly dose

Lü

Leng

Cannon

et al. 2016

Pharmacoepidemiology and Drug

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“…Because important relevant clinical data is included in narrative clinical notes rather than structured data elements or standardized coding systems, natural language processing methods can be used to extract phenotypes from clinical notes [39,40] and to process data for more advanced machine learning techniques. Phenotype definitions including general purpose natural language processing (NLP) tools [41–43] have accelerated the widespread use of NLP, which is an important component of some complex phenotypes [44].…”

Section: Evolution Of Phenotyping Methodsmentioning

confidence: 99%

Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods

Richesson

Sun

Pathak

et al. 2016

Artificial Intelligence in Medicine

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Objective The combination of phenomic data from electronic health records (EHR) and clinical data repositories with dense biological data has enabled genomic and pharmacogenomic discovery, a first step toward precision medicine. Computational methods for the identification of clinical phenotypes from EHR data will advance our understanding of disease risk and drug response, and support the practice of precision medicine on a national scale. Methods Based on our experience within three national research networks, we summarize the broad approaches to clinical phenotyping and highlight the important role of these networks in the progression of high-throughput phenotyping and precision medicine. We provide supporting literature in the form of a non-systematic review. Results The practice of clinical phenotyping is evolving to meet the growing demand for scalable, portable, and data driven methods and tools. The resources required for traditional phenotyping algorithms from expert defined rules are significant. In contrast, machine learning approaches that rely on data patterns will require fewer clinical domain experts and resources. Conclusions Machine learning approaches that generate phenotype definitions from patient features and clinical profiles will result in truly computational phenotypes, derived from data rather than experts. Research networks and phenotype developers should cooperate to develop methods, collaboration platforms, and data standards that will enable computational phenotyping and truly modernize biomedical research and precision medicine.

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Integrating existing natural language processing tools for medication extraction from discharge summaries

Abstract: The results show that the existing MedEx system, together with other NLP components, can extract medication information in clinical text from institutions other than the site of algorithm development with reasonable performance.

Cited by 61 publications

References 5 publications

Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features

Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features

The use of natural language processing on narrative medication schedules to compute average weekly dose

Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods

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