Lancet: a high precision medication event extraction system for clinical text

Li, Zuofeng; Liu, Feifan; Antieau, Lamont D.; Cao, Yanan; Yu, Hong

doi:10.1136/jamia.2010.004077

Cited by 49 publications

(35 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The combination module improved system's performance as well, but not as much as that reported in previous studies on gene names. 18 We plan to explore further more advanced combination methods such as building ensembles of classifiers in the next steps.…”

Section: Ml-based Ner For Clinical Textmentioning

confidence: 99%

A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries

Jiang

Chen

Liu

et al. 2011

J Am Med Inform Assoc

229

101

View full text Add to dashboard Cite

Systematic evaluation on the training set showed that Conditional Random Fields outperformed Support Vector Machines, and semantic information from existing natural-language-processing systems largely improved performance, although contributions from different types of features varied. The authors' hybrid entity extraction system achieved a maximum overall F-score of 0.8391 for concept extraction (ranked second) and 0.9313 for assertion classification (ranked fourth, but not statistically different than the first three systems) on the test data set in the challenge.

show abstract

Section: Ml-based Ner For Clinical Textmentioning

confidence: 99%

A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries

Jiang

Chen

Liu

et al. 2011

J Am Med Inform Assoc

229

101

View full text Add to dashboard Cite

show abstract

“…Lancet [ 87 ] is a supervised machine-learning system that automatically extracts medication events consisting of medication names and information pertaining to their prescribed use (dosage, mode, frequency, duration, and reason) from clinical discharge summaries. Lancet employs the CRFs model [ 64 ] for tagging individual medication names and associated fi elds, and the AdaBoost model with decision stump algorithm [ 88 ] for determining which medication names and fi elds belong to a single medication event.…”

Section: Learning-based Approachmentioning

confidence: 99%

Natural Language Processing, Electronic Health Records, and Clinical Research

Liu

Weng

2012

Health Informatics

Self Cite

View full text Add to dashboard Cite

Electronic health records (EHR) capture "real-world" disease and care processes and hence offer richer and more generalizable data for comparative effectiveness research than traditional randomized clinical trial studies. With the increasingly broadening adoption of EHR worldwide, there is a growing need to widen the use of EHR data to support clinical research. A big barrier to this goal is that much of the information in EHR is still narrative. This chapter describes the foundation of biomedical natural language processing and its common uses for extracting and transforming narrative information in EHR to support clinical research. Keywords Electronic health records • Biomedical natural language processing • Sublanguage approach • Machine-learning approach • Decision tree • Rule-based approachElectronic health records (EHR) capture "real-world" disease and care processes and hence offer richer and more generalizable data for comparative effectiveness research [ 1 ] than traditional randomized clinical trial studies. With the increasingly broadening adoption of EHR worldwide, there is a growing need to widen the use of EHR data to support clinical research [ 2 ] . A big barrier to this goal is that much of the information in EHR is still narrative. This chapter describes the foundation of biomedical language processing and its common uses for extracting and transforming narrative information in EHR to support clinical research.

show abstract

“…A system by Li et al that participated in the challenge workshop employed a CRF tagger for name recognition and AdaBoost with decision stumps for field identification. 33 They randomly selected 147 summaries among the provided data, and annotated them to train the machine learning models. After the workshop, Doan et al 35 used 268 annotated discharge summaries (17 summaries initially provided as examples and 251 summaries provided as the test set) and evaluated SVM taggers 12 (table 1).…”

Section: Clinical Concept Extraction Using Machine Learningmentioning

confidence: 99%

Using machine learning for concept extraction on clinical documents from multiple data sources

Torii¹,

Wagholikar²,

Liu³

2011

J Am Med Inform Assoc

106

View full text Add to dashboard Cite

Objective Concept extraction is a process to identify phrases referring to concepts of interests in unstructured text. It is a critical component in automated text processing. We investigate the performance of machine learning taggers for clinical concept extraction, particularly the portability of taggers across documents from multiple data sources. Methods We used BioTagger-GM to train machine learning taggers, which we originally developed for the detection of gene/protein names in the biology domain. Trained taggers were evaluated using the annotated clinical documents made available in the 2010 i2b2/VA Challenge workshop, consisting of documents from four data sources. Results As expected, performance of a tagger trained on one data source degraded when evaluated on another source, but the degradation of the performance varied depending on data sources. A tagger trained on multiple data sources was robust, and it achieved an F score as high as 0.890 on one data source. The results also suggest that performance of machine learning taggers is likely to improve if more annotated documents are available for training. Conclusion Our study shows how the performance of machine learning taggers is degraded when they are ported across clinical documents from different sources. The portability of taggers can be enhanced by training on datasets from multiple sources. The study also shows that BioTagger-GM can be easily extended to detect clinical concept mentions with good performance.

show abstract

Lancet: a high precision medication event extraction system for clinical text

Cited by 49 publications

References 17 publications

A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries

A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries

Natural Language Processing, Electronic Health Records, and Clinical Research

Using machine learning for concept extraction on clinical documents from multiple data sources

Contact Info

Product

Resources

About