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Featured researches published by Scott R. Halgrim.


PLOS ONE | 2014

Negation’s Not Solved: Generalizability Versus Optimizability in Clinical Natural Language Processing

Stephen T. Wu; Timothy A. Miller; James J. Masanz; Matt Coarr; Scott R. Halgrim; David Carrell; Cheryl Clark

A review of published work in clinical natural language processing (NLP) may suggest that the negation detection task has been “solved.” This work proposes that an optimizable solution does not equal a generalizable solution. We introduce a new machine learning-based Polarity Module for detecting negation in clinical text, and extensively compare its performance across domains. Using four manually annotated corpora of clinical text, we show that negation detection performance suffers when there is no in-domain development (for manual methods) or training data (for machine learning-based methods). Various factors (e.g., annotation guidelines, named entity characteristics, the amount of data, and lexical and syntactic context) play a role in making generalizability difficult, but none completely explains the phenomenon. Furthermore, generalizability remains challenging because it is unclear whether to use a single source for accurate data, combine all sources into a single model, or apply domain adaptation methods. The most reliable means to improve negation detection is to manually annotate in-domain training data (or, perhaps, manually modify rules); this is a strategy for optimizing performance, rather than generalizing it. These results suggest a direction for future work in domain-adaptive and task-adaptive methods for clinical NLP.


Journal of the American Medical Informatics Association | 2014

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

Sunghwan Sohn; Cheryl Clark; Scott R. Halgrim; Sean P. Murphy; Christopher G. Chute; Hongfang Liu

OBJECTIVE We developed the Medication Extraction and Normalization (MedXN) system to extract comprehensive medication information and normalize it to the most appropriate RxNorm concept unique identifier (RxCUI) as specifically as possible. METHODS Medication descriptions in clinical notes were decomposed into medication name and attributes, which were separately extracted using RxNorm dictionary lookup and regular expression. Then, each medication name and its attributes were combined together according to RxNorm convention to find the most appropriate RxNorm representation. To do this, we employed serialized hierarchical steps implemented in Apaches Unstructured Information Management Architecture. We also performed synonym expansion, removed false medications, and employed inference rules to improve the medication extraction and normalization performance. RESULTS An evaluation on test data of 397 medication mentions showed F-measures of 0.975 for medication name and over 0.90 for most attributes. The RxCUI assignment produced F-measures of 0.932 for medication name and 0.864 for full medication information. Most false negative RxCUI assignments in full medication information are due to human assumption of missing attributes and medication names in the gold standard. CONCLUSIONS The MedXN system (http://sourceforge.net/projects/ohnlp/files/MedXN/) was able to extract comprehensive medication information with high accuracy and demonstrated good normalization capability to RxCUI as long as explicit evidence existed. More sophisticated inference rules might result in further improvements to specific RxCUI assignments for incomplete medication descriptions.


Biomedical Informatics Insights | 2013

Analysis of Cross-Institutional Medication Description Patterns in Clinical Narratives

Sunghwan Sohn; Cheryl Clark; Scott R. Halgrim; Sean P. Murphy; Siddhartha Jonnalagadda; Kavishwar B. Wagholikar; Stephen T. Wu; Christopher G. Chute; Hongfang Liu

A large amount of medication information resides in the unstructured text found in electronic medical records, which requires advanced techniques to be properly mined. In clinical notes, medication information follows certain semantic patterns (eg, medication, dosage, frequency, and mode). Some medication descriptions contain additional word(s) between medication attributes. Therefore, it is essential to understand the semantic patterns as well as the patterns of the context interspersed among them (ie, context patterns) to effectively extract comprehensive medication information. In this paper we examined both semantic and context patterns, and compared those found in Mayo Clinic and i2b2 challenge data. We found that some variations exist between the institutions but the dominant patterns are common.


eGEMs (Generating Evidence & Methods to improve patient outcomes) | 2016

An Automated Method for Identifying Individuals with a Lung Nodule Can Be Feasibly Implemented Across Health Systems

Farhood Farjah; Scott R. Halgrim; Diana S. M. Buist; Michael K. Gould; Steven B. Zeliadt; Elizabeth Trice Loggers; David Carrell

Introduction: The incidence of incidentally detected lung nodules is rapidly rising, but little is known about their management or associated patient outcomes. One barrier to studying lung nodule care is the inability to efficiently and reliably identify the cohort of interest (i.e. cases). Investigators at Kaiser Permanente Southern California (KPSC) recently developed an automated method to identify individuals with an incidentally discovered lung nodule, but the feasibility of implementing this method across other health systems is unknown. Methods: A random sample of Group Health (GH) members who had a computed tomography in 2012 underwent chart review to determine if a lung nodule was documented in the radiology report. A previously developed natural language processing (NLP) algorithm was implemented at our site using only knowledge of the key words, qualifiers, excluding terms, and the logic linking these parameters. Results: Among 499 subjects, 156 (31%, 95% confidence interval [CI] 27–36%) had an incidentally detected lung nodule. NLP identified 189 (38%, 95% CI 33–42%) individuals with a nodule. The accuracy of NLP at GH was similar to its accuracy at KPSC: sensitivity 90% (95% CI 85–95%) and specificity 86% (95% CI 82–89%) versus sensitivity 96% (95% CI 88–100%) and specificity 86% (95% CI 75–94%). Conclusion: Automated methods designed to identify individuals with an incidentally detected lung nodule can feasibly and independently be implemented across health systems. Use of these methods will likely facilitate the efficient conduct of multi-site studies evaluating practice patterns and associated outcomes.


AMIA | 2013

Negation's Not Solved: Reconsidering Negation Annotation and Evaluation.

Stephen T. Wu; Timothy A. Miller; James J. Masanz; Matthew Coarr; David Carrell; Scott R. Halgrim; David J. Harris; Cheryl Clark


Journal of Patient-Centered Research and Reviews | 2015

Using Natural Language Processing to Identify Health Plan Beneficiaries With Pulmonary Nodules

Farhood Farjah; Scott R. Halgrim; Steven B. Zeliadt; Michael K. Gould; David Carrell; Elizabeth T. Loggers; David R. Flum; Diana S. M. Buist


Clinical Medicine & Research | 2014

C3-2: All-In-One: How Group Health Organizes Clinical Text from Clarity for Research

Scott R. Halgrim; David Carrell; Diem-Thy Tran


Clinical Medicine & Research | 2014

PS1-17: Organization-wide Collaboration to Convert Binary Clinical Notes to Research-ready Text

Scott R. Halgrim; Shane Reeh; Roger Kelem; Roger Wong; Aruna Kamineni; Virginia Immanuel; Christopher Pattillo; Roy Pardee; David Carrell; Carolyn M. Rutter


Clinical Medicine & Research | 2014

C3-4: An Algorithm to Combine Machine Learning and Structured Data to Automate De-identification of Clinical Text.

Diem-Thy Tran; Scott R. Halgrim; David Carrell


AMIA | 2014

Linking Adenomas Between Colonoscopy And Pathology Notes For PROSPR.

Scott R. Halgrim; Leslie Sizemore; Edward Pham; Gabrielle Gundersen; David Carrell; Karen J. Wernli; Jessica Chubak; Carolyn M. Rutter

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Diana S. M. Buist

Group Health Research Institute

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Diem-Thy Tran

Group Health Research Institute

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