A sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction

OBJECTIVES/SPECIFIC AIMS: This poster presents a detailed characterization of the distribution of semantic concepts used in the text describing eligibility criteria of clinical trials reported to ClincalTrials.gov and patient notes from MIMIC-III. The final goal of this study is to find a minimal set of semantic concepts that can describe clinical trials and patients for efficient computational matching of clinical trial descriptions to potential participants at large scale. METHODS/STUDY POPULATION: We downloaded the free text describing the eligibility criteria of all clinical trials reported to ClinicalTrials.gov as of July 28, 2015, ~195,000 trials and ~2,000,000 clinical notes from MIMIC-III. Using MetaMap 2014 we extracted UMLS concepts (CUIs) from the collected text. We calculated the frequency of presence of the semantic concepts in the texts describing the clinical trials eligibility criteria and patient notes. RESULTS/ANTICIPATED RESULTS: The results show a classical power distribution, Y=210X(−2.043), R2=0.9599, for clinical trial eligibility criteria and Y=513X(−2.684), R2=0.9477 for MIMIC patient notes, where Y represents the number of documents in which a concept appears and X is the cardinal order the concept ordered from more to less frequent. From this distribution, it is possible to realize that from the over, 100,000 concepts in UMLS, there are only ~60,000 and 50,000 concepts that appear in less than 10 clinical trial eligibility descriptions and MIMIC-III patient clinical notes, respectively. This indicates that it would be possible to describe clinical trials and patient notes with a relatively small number of concepts, making the search space for matching patients to clinical trials a relatively small sub-space of the overall UMLS search space. DISCUSSION/SIGNIFICANCE OF IMPACT: Our results showing that the concepts used to describe clinical trial eligibility criteria and patient clinical notes follow a power distribution can lead to tractable computational approaches to automatically match patients to clinical trials at large scale by considerably reducing the search space. While automatic patient matching is not the panacea for improving clinical trial recruitment, better low cost computational preselection processes can allow the limited human resources assigned to patient recruitment to be redirected to the most promising targets for recruitment.

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Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-019-0937-2 ◽

2019 ◽

Vol 19 (S5) ◽

Cited By ~ 4

Author(s):

Jun Xu ◽

Zhiheng Li ◽

Qiang Wei ◽

Yonghui Wu ◽

Yang Xiang ◽

...

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Entity Recognition ◽

Clinical Text ◽

Sequence Labeling ◽

Medical Concept ◽

Labeling Approach ◽

Relation Classification ◽

Medical Concepts ◽

Concept Attribute

Abstract Background To detect attributes of medical concepts in clinical text, a traditional method often consists of two steps: named entity recognition of attributes and then relation classification between medical concepts and attributes. Here we present a novel solution, in which attribute detection of given concepts is converted into a sequence labeling problem, thus attribute entity recognition and relation classification are done simultaneously within one step. Methods A neural architecture combining bidirectional Long Short-Term Memory networks and Conditional Random fields (Bi-LSTMs-CRF) was adopted to detect various medical concept-attribute pairs in an efficient way. We then compared our deep learning-based sequence labeling approach with traditional two-step systems for three different attribute detection tasks: disease-modifier, medication-signature, and lab test-value. Results Our results show that the proposed method achieved higher accuracy than the traditional methods for all three medical concept-attribute detection tasks. Conclusions This study demonstrates the efficacy of our sequence labeling approach using Bi-LSTM-CRFs on the attribute detection task, indicating its potential to speed up practical clinical NLP applications.

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Using word embeddings to expand terminology of dietary supplements on clinical notes

JAMIA Open ◽

10.1093/jamiaopen/ooz007 ◽

2019 ◽

Vol 2 (2) ◽

pp. 246-253 ◽

Cited By ~ 3

Author(s):

Yadan Fan ◽

Serguei Pakhomov ◽

Reed McEwan ◽

Wendi Zhao ◽

Elizabeth Lindemann ◽

...

Keyword(s):

Information Extraction ◽

Dietary Supplements ◽

Window Size ◽

Word Embedding ◽

Brand Names ◽

Word Embeddings ◽

Clinical Notes ◽

Corpus Size ◽

Top 40

Abstract Objective The objective of this study is to demonstrate the feasibility of applying word embeddings to expand the terminology of dietary supplements (DS) using over 26 million clinical notes. Methods Word embedding models (ie, word2vec and GloVe) trained on clinical notes were used to predefine a list of top 40 semantically related terms for each of 14 commonly used DS. Each list was further evaluated by experts to generate semantically similar terms. We investigated the effect of corpus size and other settings (ie, vector size and window size) as well as the 2 word embedding models on performance for DS term expansion. We compared the number of clinical notes (and patients they represent) that were retrieved using the word embedding expanded terms to both the baseline terms and external DS sources expanded terms. Results Using the word embedding models trained on clinical notes, we could identify 1–12 semantically similar terms for each DS. Using the word embedding expanded terms, we were able to retrieve averagely 8.39% more clinical notes and 11.68% more patients for each DS compared with 2 sets of terms. The increasing corpus size results in more misspellings, but not more semantic variants and brand names. Word2vec model is also found more capable of detecting semantically similar terms than GloVe. Conclusion Our study demonstrates the utility of word embeddings on clinical notes for terminology expansion on 14 DS. We propose that this method can be potentially applied to create a DS vocabulary for downstream applications, such as information extraction.

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