Identifying Smokers with a Medical Extraction System

Clark, Cheryl; Good, Kathleen; Jezierny, Lesley; Macpherson, Melissa; Wilson, Brian; Chajewska, Urszula

doi:10.1197/jamia.m2442

Cited by 55 publications

(40 citation statements)

References 4 publications

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“…McGinnis et al (2011) used VA EHR health factors to reliably identify current smokers), and unstructured data (e.g. Clark et al (2008);Savova et al (2008); Da Silva et al (2011) applied natural language processing methods to EHR clinical notes to identify smoking status).…”

Section: Introductionmentioning

confidence: 99%

Investigating the Documentation of Electronic Cigarette Use in the Veteran Affairs Electronic Health Record: A Pilot Study

Mowery¹,

South²,

Patterson³

et al. 2017

BioNLP 2017

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In this paper, we present pilot work on characterising the documentation of electronic cigarettes (e-cigarettes) in the United States Veterans Administration Electronic Health Record. The Veterans Health Administration is the largest health care system in the United States with 1,233 health care facilities nationwide, serving 8.9 million veterans per year. We identified a random sample of 2000 Veterans Administration patients, coded as current tobacco users, from 2008 to 2014. Using simple keyword matching techniques combined with qualitative analysis, we investigated the prevalence and distribution of e-cigarette terms in these clinical notes, discovering that for current smokers, 11.9% of patient records contain an ecigarette related term.

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Section: Introductionmentioning

confidence: 99%

Investigating the Documentation of Electronic Cigarette Use in the Veteran Affairs Electronic Health Record: A Pilot Study

Mowery¹,

South²,

Patterson³

et al. 2017

BioNLP 2017

View full text Add to dashboard Cite

show abstract

“…It employs support vector machines to attribute semantic categories to each word in discharge summaries. In [9] they developed a medical information extraction system that combines a rule-based extraction engine with machine learning algorithms to identify and categorize references in clinical reports to patients who smoke.…”

Section: Related Workmentioning

confidence: 99%

Using a Cellular Automaton to Extract Medical Information from Clinical Reports

Barigou¹,

Atmani²,

Beldjilali³

2012

Journal of Information Processing Systems

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Abstract-An important amount of clinical data concerning the medical history of a patient is in the form of clinical reports that are written by doctors. They describe patients, their pathologies, their personal and medical histories, findings made during interviews or during procedures, and so forth. They represent a source of precious information that can be used in several applications such as research information to diagnose new patients, epidemiological studies, decision support, statistical analysis, and data mining. But this information is difficult to access, as it is often in unstructured text form. To make access to patient data easy, our research aims to develop a system for extracting information from unstructured text. In a previous work, a rule-based approach is applied to a clinical reports corpus of infectious diseases to extract structured data in the form of named entities and properties. In this paper, we propose the use of a Boolean inference engine, which is based on a cellular automaton, to do extraction. Our motivation to adopt this Boolean modeling approach is twofold: first optimize storage, and second reduce the response time of the entities extraction.

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“…The first experiment was performed to evaluate the performance of online forum discussion against the clinical reports (baseline method (Clark et al, 2008)) in the smoking status classification problem. The experiment used the baseline method, which achieved the best results in the smoking status challenge (Uzuner et al, 2008).…”

Section: Compare With Baseline Methodsmentioning

confidence: 99%

“…Clark et al (Clark et al, 2008) developed the best performing system in the i2b2 challenge. They used the binary presence of unigram and bigram word features of document with SVM classifier algorithm and 10 cross-validation techniques.…”

Section: Introductionmentioning

confidence: 99%

Text Analysis of User-Generated Contents for Health-care Applications - Case Study on Smoking Status Classification

Hafeth

Ahmed

Cobham

2014

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

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Abstract:Text mining techniques have demonstrated a potential to unlock significant patient health information from unstructured text. However, most of the published work has been done using clinical reports, which are difficult to access due to patient confidentiality. In this paper, we present an investigation of text analysis for smoking status classification from User-Generated Contents (UGC), such as online forum discussions. UGC are more widely available, compared to clinical reports. Based on analyzing the properties of UGC, we propose the use of Linguistic Inquiry Word Count (LIWC) an approach being used for the first time for such a health-related task. We also explore various factors that affect the classification performance. The experimental results and evaluation indicate that the forum classification performs well with the proposed features. It has achieved an accuracy of up to 75% for smoking status prediction. Furthermore, the utilized features set is compact (88 features only) and independent of the dataset size.

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Identifying Smokers with a Medical Extraction System

Cited by 55 publications

References 4 publications

Investigating the Documentation of Electronic Cigarette Use in the Veteran Affairs Electronic Health Record: A Pilot Study

Investigating the Documentation of Electronic Cigarette Use in the Veteran Affairs Electronic Health Record: A Pilot Study

Using a Cellular Automaton to Extract Medical Information from Clinical Reports

Text Analysis of User-Generated Contents for Health-care Applications - Case Study on Smoking Status Classification

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