1
|
Eguia H, Sánchez-Bocanegra CL, Vinciarelli F, Alvarez-Lopez F, Saigí-Rubió F. Clinical Decision Support and Natural Language Processing in Medicine: Systematic Literature Review. J Med Internet Res 2024; 26:e55315. [PMID: 39348889 PMCID: PMC11474138 DOI: 10.2196/55315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 04/20/2024] [Accepted: 07/24/2024] [Indexed: 10/02/2024] Open
Abstract
BACKGROUND Ensuring access to accurate and verified information is essential for effective patient treatment and diagnosis. Although health workers rely on the internet for clinical data, there is a need for a more streamlined approach. OBJECTIVE This systematic review aims to assess the current state of artificial intelligence (AI) and natural language processing (NLP) techniques in health care to identify their potential use in electronic health records and automated information searches. METHODS A search was conducted in the PubMed, Embase, ScienceDirect, Scopus, and Web of Science online databases for articles published between January 2000 and April 2023. The only inclusion criteria were (1) original research articles and studies on the application of AI-based medical clinical decision support using NLP techniques and (2) publications in English. A Critical Appraisal Skills Programme tool was used to assess the quality of the studies. RESULTS The search yielded 707 articles, from which 26 studies were included (24 original articles and 2 systematic reviews). Of the evaluated articles, 21 (81%) explained the use of NLP as a source of data collection, 18 (69%) used electronic health records as a data source, and a further 8 (31%) were based on clinical data. Only 5 (19%) of the articles showed the use of combined strategies for NLP to obtain clinical data. In total, 16 (62%) articles presented stand-alone data review algorithms. Other studies (n=9, 35%) showed that the clinical decision support system alternative was also a way of displaying the information obtained for immediate clinical use. CONCLUSIONS The use of NLP engines can effectively improve clinical decision systems' accuracy, while biphasic tools combining AI algorithms and human criteria may optimize clinical diagnosis and treatment flows. TRIAL REGISTRATION PROSPERO CRD42022373386; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=373386.
Collapse
Affiliation(s)
- Hans Eguia
- SEMERGEN New Technologies Working Group, Madrid, Spain
- Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Barcelona, Spain
| | | | - Franco Vinciarelli
- SEMERGEN New Technologies Working Group, Madrid, Spain
- Emergency Hospital Clemente Álvarez, Rosario (Santa Fe), Argentina
| | | | - Francesc Saigí-Rubió
- Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Barcelona, Spain
| |
Collapse
|
2
|
Fu S, Wang L, He H, Wen A, Zong N, Kumari A, Liu F, Zhou S, Zhang R, Li C, Wang Y, St Sauver J, Liu H, Sohn S. A taxonomy for advancing systematic error analysis in multi-site electronic health record-based clinical concept extraction. J Am Med Inform Assoc 2024; 31:1493-1502. [PMID: 38742455 PMCID: PMC11187420 DOI: 10.1093/jamia/ocae101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 03/26/2024] [Accepted: 04/19/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Error analysis plays a crucial role in clinical concept extraction, a fundamental subtask within clinical natural language processing (NLP). The process typically involves a manual review of error types, such as contextual and linguistic factors contributing to their occurrence, and the identification of underlying causes to refine the NLP model and improve its performance. Conducting error analysis can be complex, requiring a combination of NLP expertise and domain-specific knowledge. Due to the high heterogeneity of electronic health record (EHR) settings across different institutions, challenges may arise when attempting to standardize and reproduce the error analysis process. OBJECTIVES This study aims to facilitate a collaborative effort to establish common definitions and taxonomies for capturing diverse error types, fostering community consensus on error analysis for clinical concept extraction tasks. MATERIALS AND METHODS We iteratively developed and evaluated an error taxonomy based on existing literature, standards, real-world data, multisite case evaluations, and community feedback. The finalized taxonomy was released in both .dtd and .owl formats at the Open Health Natural Language Processing Consortium. The taxonomy is compatible with several different open-source annotation tools, including MAE, Brat, and MedTator. RESULTS The resulting error taxonomy comprises 43 distinct error classes, organized into 6 error dimensions and 4 properties, including model type (symbolic and statistical machine learning), evaluation subject (model and human), evaluation level (patient, document, sentence, and concept), and annotation examples. Internal and external evaluations revealed strong variations in error types across methodological approaches, tasks, and EHR settings. Key points emerged from community feedback, including the need to enhancing clarity, generalizability, and usability of the taxonomy, along with dissemination strategies. CONCLUSION The proposed taxonomy can facilitate the acceleration and standardization of the error analysis process in multi-site settings, thus improving the provenance, interpretability, and portability of NLP models. Future researchers could explore the potential direction of developing automated or semi-automated methods to assist in the classification and standardization of error analysis.
Collapse
Affiliation(s)
- Sunyang Fu
- Department of AI and Informatics, Mayo Clinic, Rochester, MN 55902, United States
- Center for Translational AI Excellence and Applications in Medicine, University of Texas Health Science Center at Houston, Houston, TX 77030, United States
| | - Liwei Wang
- Department of AI and Informatics, Mayo Clinic, Rochester, MN 55902, United States
- Center for Translational AI Excellence and Applications in Medicine, University of Texas Health Science Center at Houston, Houston, TX 77030, United States
| | - Huan He
- Department of Biomedical Informatics & Data Science, Yale University, New Haven, CT 06520, United States
| | - Andrew Wen
- Center for Translational AI Excellence and Applications in Medicine, University of Texas Health Science Center at Houston, Houston, TX 77030, United States
| | - Nansu Zong
- Department of AI and Informatics, Mayo Clinic, Rochester, MN 55902, United States
| | - Anamika Kumari
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Boston, MA 01655, United States
| | - Feifan Liu
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Boston, MA 01655, United States
| | - Sicheng Zhou
- Division of Computational Health Sciences, University of Minnesota, Minneapolis, MN 55455, United States
| | - Rui Zhang
- Division of Computational Health Sciences, University of Minnesota, Minneapolis, MN 55455, United States
| | - Chenyu Li
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA 15260, United States
| | - Yanshan Wang
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA 15260, United States
| | - Jennifer St Sauver
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55902, United States
| | - Hongfang Liu
- Department of AI and Informatics, Mayo Clinic, Rochester, MN 55902, United States
- Center for Translational AI Excellence and Applications in Medicine, University of Texas Health Science Center at Houston, Houston, TX 77030, United States
| | - Sunghwan Sohn
- Department of AI and Informatics, Mayo Clinic, Rochester, MN 55902, United States
| |
Collapse
|
3
|
Sivarajkumar S, Kelley M, Samolyk-Mazzanti A, Visweswaran S, Wang Y. An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation Study. JMIR Med Inform 2024; 12:e55318. [PMID: 38587879 PMCID: PMC11036183 DOI: 10.2196/55318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/20/2024] [Accepted: 02/24/2024] [Indexed: 04/09/2024] Open
Abstract
BACKGROUND Large language models (LLMs) have shown remarkable capabilities in natural language processing (NLP), especially in domains where labeled data are scarce or expensive, such as the clinical domain. However, to unlock the clinical knowledge hidden in these LLMs, we need to design effective prompts that can guide them to perform specific clinical NLP tasks without any task-specific training data. This is known as in-context learning, which is an art and science that requires understanding the strengths and weaknesses of different LLMs and prompt engineering approaches. OBJECTIVE The objective of this study is to assess the effectiveness of various prompt engineering techniques, including 2 newly introduced types-heuristic and ensemble prompts, for zero-shot and few-shot clinical information extraction using pretrained language models. METHODS This comprehensive experimental study evaluated different prompt types (simple prefix, simple cloze, chain of thought, anticipatory, heuristic, and ensemble) across 5 clinical NLP tasks: clinical sense disambiguation, biomedical evidence extraction, coreference resolution, medication status extraction, and medication attribute extraction. The performance of these prompts was assessed using 3 state-of-the-art language models: GPT-3.5 (OpenAI), Gemini (Google), and LLaMA-2 (Meta). The study contrasted zero-shot with few-shot prompting and explored the effectiveness of ensemble approaches. RESULTS The study revealed that task-specific prompt tailoring is vital for the high performance of LLMs for zero-shot clinical NLP. In clinical sense disambiguation, GPT-3.5 achieved an accuracy of 0.96 with heuristic prompts and 0.94 in biomedical evidence extraction. Heuristic prompts, alongside chain of thought prompts, were highly effective across tasks. Few-shot prompting improved performance in complex scenarios, and ensemble approaches capitalized on multiple prompt strengths. GPT-3.5 consistently outperformed Gemini and LLaMA-2 across tasks and prompt types. CONCLUSIONS This study provides a rigorous evaluation of prompt engineering methodologies and introduces innovative techniques for clinical information extraction, demonstrating the potential of in-context learning in the clinical domain. These findings offer clear guidelines for future prompt-based clinical NLP research, facilitating engagement by non-NLP experts in clinical NLP advancements. To the best of our knowledge, this is one of the first works on the empirical evaluation of different prompt engineering approaches for clinical NLP in this era of generative artificial intelligence, and we hope that it will inspire and inform future research in this area.
Collapse
Affiliation(s)
- Sonish Sivarajkumar
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States
| | - Mark Kelley
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, United States
| | - Alyssa Samolyk-Mazzanti
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, United States
| | - Shyam Visweswaran
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Yanshan Wang
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| |
Collapse
|
4
|
van de Burgt BWM, Wasylewicz ATM, Dullemond B, Grouls RJE, Egberts TCG, Bouwman A, Korsten EMM. Combining text mining with clinical decision support in clinical practice: a scoping review. J Am Med Inform Assoc 2022; 30:588-603. [PMID: 36512578 PMCID: PMC9933076 DOI: 10.1093/jamia/ocac240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 10/17/2022] [Accepted: 12/01/2022] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVE Combining text mining (TM) and clinical decision support (CDS) could improve diagnostic and therapeutic processes in clinical practice. This review summarizes current knowledge of the TM-CDS combination in clinical practice, including their intended purpose, implementation in clinical practice, and barriers to such implementation. MATERIALS AND METHODS A search was conducted in PubMed, EMBASE, and Cochrane Library databases to identify full-text English language studies published before January 2022 with TM-CDS combination in clinical practice. RESULTS Of 714 identified and screened unique publications, 39 were included. The majority of the included studies are related to diagnosis (n = 26) or prognosis (n = 11) and used a method that was developed for a specific clinical domain, document type, or application. Most of the studies selected text containing parts of the electronic health record (EHR), such as reports (41%, n = 16) and free-text narratives (36%, n = 14), and 23 studies utilized a tool that had software "developed for the study". In 15 studies, the software source was openly available. In 79% of studies, the tool was not implemented in clinical practice. Barriers to implement these tools included the complexity of natural language, EHR incompleteness, validation and performance of the tool, lack of input from an expert team, and the adoption rate among professionals. DISCUSSION/CONCLUSIONS The available evidence indicates that the TM-CDS combination may improve diagnostic and therapeutic processes, contributing to increased patient safety. However, further research is needed to identify barriers to implementation and the impact of such tools in clinical practice.
Collapse
Affiliation(s)
- Britt W M van de Burgt
- Corresponding Author: Britt W.M. van de Burgt, MSc, Department Healthcare Intelligence, Catharina Hospital Eindhoven, Michelangelolaan 2, 5623 EJ Eindhoven, The Netherlands;
| | - Arthur T M Wasylewicz
- Department Healthcare Intelligence, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Bjorn Dullemond
- Department of Mathematics and Computer Science, Technical University of Eindhoven, Eindhoven, The Netherlands
| | - Rene J E Grouls
- Department of Clinical Pharmacy, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Toine C G Egberts
- Department of Clinical Pharmacy, University Medical Centre Utrecht, Utrecht, the Netherlands,Department of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Arthur Bouwman
- Department of Electrical Engineering, Signal Processing Group, Technical University Eindhoven, Eindhoven, The Netherlands,Department of Anesthesiology, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Erik M M Korsten
- Department Healthcare Intelligence, Catharina Hospital Eindhoven, Eindhoven, The Netherlands,Department of Electrical Engineering, Signal Processing Group, Technical University Eindhoven, Eindhoven, The Netherlands
| |
Collapse
|
5
|
Wang L, Fu S, Wen A, Ruan X, He H, Liu S, Moon S, Mai M, Riaz IB, Wang N, Yang P, Xu H, Warner JL, Liu H. Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing. JCO Clin Cancer Inform 2022; 6:e2200006. [PMID: 35917480 PMCID: PMC9470142 DOI: 10.1200/cci.22.00006] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 03/18/2022] [Accepted: 06/15/2022] [Indexed: 11/20/2022] Open
Abstract
PURPOSE The advancement of natural language processing (NLP) has promoted the use of detailed textual data in electronic health records (EHRs) to support cancer research and to facilitate patient care. In this review, we aim to assess EHR for cancer research and patient care by using the Minimal Common Oncology Data Elements (mCODE), which is a community-driven effort to define a minimal set of data elements for cancer research and practice. Specifically, we aim to assess the alignment of NLP-extracted data elements with mCODE and review existing NLP methodologies for extracting said data elements. METHODS Published literature studies were searched to retrieve cancer-related NLP articles that were written in English and published between January 2010 and September 2020 from main literature databases. After the retrieval, articles with EHRs as the data source were manually identified. A charting form was developed for relevant study analysis and used to categorize data including four main topics: metadata, EHR data and targeted cancer types, NLP methodology, and oncology data elements and standards. RESULTS A total of 123 publications were selected finally and included in our analysis. We found that cancer research and patient care require some data elements beyond mCODE as expected. Transparency and reproductivity are not sufficient in NLP methods, and inconsistency in NLP evaluation exists. CONCLUSION We conducted a comprehensive review of cancer NLP for research and patient care using EHRs data. Issues and barriers for wide adoption of cancer NLP were identified and discussed.
Collapse
Affiliation(s)
- Liwei Wang
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN
| | - Sunyang Fu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN
| | - Andrew Wen
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN
| | - Xiaoyang Ruan
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN
| | - Huan He
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN
| | - Sijia Liu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN
| | - Sungrim Moon
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN
| | - Michelle Mai
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN
| | - Irbaz B. Riaz
- Department of Hematology/Oncology, Mayo Clinic, Scottsdale, AZ
| | - Nan Wang
- Department of Computer Science and Engineering, College of Science and Engineering, University of Minnesota, Minneapolis, MN
| | - Ping Yang
- Department of Quantitative Health Sciences, Mayo Clinic, Scottsdale, AZ
| | - Hua Xu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX
| | - Jeremy L. Warner
- Departments of Medicine (Hematology/Oncology), Vanderbilt University, Nashville, TN
- Department Biomedical Informatics, Vanderbilt University, Nashville, TN
| | - Hongfang Liu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN
| |
Collapse
|
6
|
Vadyala SR, Sherer EA. Natural Language Processing Accurately Categorizes Indications, Findings and Pathology Reports from Multicenter Colonoscopy: Qualitative focus study (Preprint). JMIR Cancer 2021. [DOI: 10.2196/32973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
|
7
|
Zirikly A, Desmet B, Newman-Griffis D, Marfeo EE, McDonough C, Goldman H, Chan L. Viewpoint: An Information Extraction Framework for Disability Determination Using a Mental Functioning Use-Case (Preprint). JMIR Med Inform 2021; 10:e32245. [PMID: 35302510 PMCID: PMC8976250 DOI: 10.2196/32245] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 10/08/2021] [Accepted: 01/16/2022] [Indexed: 01/08/2023] Open
Abstract
Natural language processing (NLP) in health care enables transformation of complex narrative information into high value products such as clinical decision support and adverse event monitoring in real time via the electronic health record (EHR). However, information technologies for mental health have consistently lagged because of the complexity of measuring and modeling mental health and illness. The use of NLP to support management of mental health conditions is a viable topic that has not been explored in depth. This paper provides a framework for the advanced application of NLP methods to identify, extract, and organize information on mental health and functioning to inform the decision-making process applied to assessing mental health. We present a use-case related to work disability, guided by the disability determination process of the US Social Security Administration (SSA). From this perspective, the following questions must be addressed about each problem that leads to a disability benefits claim: When did the problem occur and how long has it existed? How severe is it? Does it affect the person’s ability to work? and What is the source of the evidence about the problem? Our framework includes 4 dimensions of medical information that are central to assessing disability—temporal sequence and duration, severity, context, and information source. We describe key aspects of each dimension and promising approaches for application in mental functioning. For example, to address temporality, a complete functional timeline must be created with all relevant aspects of functioning such as intermittence, persistence, and recurrence. Severity of mental health symptoms can be successfully identified and extracted on a 4-level ordinal scale from absent to severe. Some NLP work has been reported on the extraction of context for specific cases of wheelchair use in clinical settings. We discuss the links between the task of information source assessment and work on source attribution, coreference resolution, event extraction, and rule-based methods. Gaps were identified in NLP applications that directly applied to the framework and in existing relevant annotated data sets. We highlighted NLP methods with the potential for advanced application in the field of mental functioning. Findings of this work will inform the development of instruments for supporting SSA adjudicators in their disability determination process. The 4 dimensions of medical information may have relevance for a broad array of individuals and organizations responsible for assessing mental health function and ability. Further, our framework with 4 specific dimensions presents significant opportunity for the application of NLP in the realm of mental health and functioning beyond the SSA setting, and it may support the development of robust tools and methods for decision-making related to clinical care, program implementation, and other outcomes.
Collapse
Affiliation(s)
- Ayah Zirikly
- Rehabilitation Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD, United States
- Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, United States
| | - Bart Desmet
- Rehabilitation Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD, United States
| | - Denis Newman-Griffis
- Rehabilitation Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD, United States
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Elizabeth E Marfeo
- Rehabilitation Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD, United States
- Department of Occupational Therapy, Tufts University, Medford, MA, United States
| | - Christine McDonough
- Rehabilitation Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD, United States
- School of Health and Rehabilitation Science, University of Pittsburgh, Pittsburgh, PA, United States
| | - Howard Goldman
- Rehabilitation Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD, United States
- Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Leighton Chan
- Rehabilitation Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD, United States
| |
Collapse
|
8
|
Zhao J, Grabowska ME, Kerchberger VE, Smith JC, Eken HN, Feng Q, Peterson JF, Trent Rosenbloom S, Johnson KB, Wei WQ. ConceptWAS: A high-throughput method for early identification of COVID-19 presenting symptoms and characteristics from clinical notes. J Biomed Inform 2021; 117:103748. [PMID: 33774203 PMCID: PMC7992296 DOI: 10.1016/j.jbi.2021.103748] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 01/28/2021] [Accepted: 03/07/2021] [Indexed: 01/08/2023]
Abstract
OBJECTIVE Identifying symptoms and characteristics highly specific to coronavirus disease 2019 (COVID-19) would improve the clinical and public health response to this pandemic challenge. Here, we describe a high-throughput approach - Concept-Wide Association Study (ConceptWAS) - that systematically scans a disease's clinical manifestations from clinical notes. We used this method to identify symptoms specific to COVID-19 early in the course of the pandemic. METHODS We created a natural language processing pipeline to extract concepts from clinical notes in a local ER corresponding to the PCR testing date for patients who had a COVID-19 test and evaluated these concepts as predictors for developing COVID-19. We identified predictors from Firth's logistic regression adjusted by age, gender, and race. We also performed ConceptWAS using cumulative data every two weeks to identify the timeline for recognition of early COVID-19-specific symptoms. RESULTS We processed 87,753 notes from 19,692 patients subjected to COVID-19 PCR testing between March 8, 2020, and May 27, 2020 (1,483 COVID-19-positive). We found 68 concepts significantly associated with a positive COVID-19 test. We identified symptoms associated with increasing risk of COVID-19, including "anosmia" (odds ratio [OR] = 4.97, 95% confidence interval [CI] = 3.21-7.50), "fever" (OR = 1.43, 95% CI = 1.28-1.59), "cough with fever" (OR = 2.29, 95% CI = 1.75-2.96), and "ageusia" (OR = 5.18, 95% CI = 3.02-8.58). Using ConceptWAS, we were able to detect loss of smell and loss of taste three weeks prior to their inclusion as symptoms of the disease by the Centers for Disease Control and Prevention (CDC). CONCLUSION ConceptWAS, a high-throughput approach for exploring specific symptoms and characteristics of a disease like COVID-19, offers a promise for enabling EHR-powered early disease manifestations identification.
Collapse
Affiliation(s)
- Juan Zhao
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Monika E Grabowska
- Medical Scientist Training Program, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Vern Eric Kerchberger
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Medicine, Division of Allergy, Pulmonary & Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Joshua C Smith
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - H Nur Eken
- Vanderbilt University School of Medicine, Nashville, TN, USA
| | - QiPing Feng
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Josh F Peterson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - S Trent Rosenbloom
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kevin B Johnson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
| |
Collapse
|
9
|
Zhao J, Grabowska ME, Kerchberger VE, Smith JC, Eken HN, Feng Q, Peterson JF, Rosenbloom ST, Johnson KB, Wei WQ. ConceptWAS: a high-throughput method for early identification of COVID-19 presenting symptoms. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.11.06.20227165. [PMID: 33200151 PMCID: PMC7668764 DOI: 10.1101/2020.11.06.20227165] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Objective Identifying symptoms highly specific to COVID-19 would improve the clinical and public health response to infectious outbreaks. Here, we describe a high-throughput approach - Concept-Wide Association Study (ConceptWAS) that systematically scans a disease's clinical manifestations from clinical notes. We used this method to identify symptoms specific to COVID-19 early in the course of the pandemic. Methods Using the Vanderbilt University Medical Center (VUMC) EHR, we parsed clinical notes through a natural language processing pipeline to extract clinical concepts. We examined the difference in concepts derived from the notes of COVID-19-positive and COVID-19-negative patients on the PCR testing date. We performed ConceptWAS using the cumulative data every two weeks for early identifying specific COVID-19 symptoms. Results We processed 87,753 notes 19,692 patients (1,483 COVID-19-positive) subjected to COVID-19 PCR testing between March 8, 2020, and May 27, 2020. We found 68 clinical concepts significantly associated with COVID-19. We identified symptoms associated with increasing risk of COVID-19, including "absent sense of smell" (odds ratio [OR] = 4.97, 95% confidence interval [CI] = 3.21-7.50), "fever" (OR = 1.43, 95% CI = 1.28-1.59), "with cough fever" (OR = 2.29, 95% CI = 1.75-2.96), and "ageusia" (OR = 5.18, 95% CI = 3.02-8.58). Using ConceptWAS, we were able to detect loss sense of smell or taste three weeks prior to their inclusion as symptoms of the disease by the Centers for Disease Control and Prevention (CDC). Conclusion ConceptWAS is a high-throughput approach for exploring specific symptoms of a disease like COVID-19, with a promise for enabling EHR-powered early disease manifestations identification.
Collapse
Affiliation(s)
- Juan Zhao
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Monika E Grabowska
- Medical Scientist Training Program, Vanderbilt University School of Medicine, Nashville, TN
| | - Vern Eric Kerchberger
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
- Department of Medicine, Division of Allergy, Pulmonary & Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Joshua C. Smith
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - H. Nur Eken
- Vanderbilt University School of Medicine, Nashville, TN
| | - QiPing Feng
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Josh F. Peterson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - S. Trent Rosenbloom
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Kevin B. Johnson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| |
Collapse
|
10
|
Fu S, Chen D, He H, Liu S, Moon S, Peterson KJ, Shen F, Wang L, Wang Y, Wen A, Zhao Y, Sohn S, Liu H. Clinical concept extraction: A methodology review. J Biomed Inform 2020; 109:103526. [PMID: 32768446 PMCID: PMC7746475 DOI: 10.1016/j.jbi.2020.103526] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 07/30/2020] [Accepted: 08/02/2020] [Indexed: 01/11/2023]
Abstract
BACKGROUND Concept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinical information from text for a wide range of applications ranging from clinical decision support to care quality improvement. OBJECTIVES In this literature review, we provide a methodology review of clinical concept extraction, aiming to catalog development processes, available methods and tools, and specific considerations when developing clinical concept extraction applications. METHODS Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a literature search was conducted for retrieving EHR-based information extraction articles written in English and published from January 2009 through June 2019 from Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science, and the ACM Digital Library. RESULTS A total of 6,686 publications were retrieved. After title and abstract screening, 228 publications were selected. The methods used for developing clinical concept extraction applications were discussed in this review.
Collapse
Affiliation(s)
- Sunyang Fu
- Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States; University of Minnesota - Twin Cities, Minneapolis, MN 55455, United States.
| | - David Chen
- Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States.
| | - Huan He
- Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States.
| | - Sijia Liu
- Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States.
| | - Sungrim Moon
- Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States.
| | - Kevin J Peterson
- Department of Information Technology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States; University of Minnesota - Twin Cities, Minneapolis, MN 55455, United States.
| | - Feichen Shen
- Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States.
| | - Liwei Wang
- Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States.
| | - Yanshan Wang
- Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States.
| | - Andrew Wen
- Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States.
| | - Yiqing Zhao
- Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States.
| | - Sunghwan Sohn
- Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States.
| | - Hongfang Liu
- Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States; University of Minnesota - Twin Cities, Minneapolis, MN 55455, United States.
| |
Collapse
|
11
|
Baxter SL, Klie AR, Radha Saseendrakumar B, Ye GY, Hogarth M. Text Processing for Detection of Fungal Ocular Involvement in Critical Care Patients: Cross-Sectional Study. J Med Internet Res 2020; 22:e18855. [PMID: 32795984 PMCID: PMC7455861 DOI: 10.2196/18855] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 04/21/2020] [Accepted: 06/13/2020] [Indexed: 11/13/2022] Open
Abstract
Background Fungal ocular involvement can develop in patients with fungal bloodstream infections and can be vision-threatening. Ocular involvement has become less common in the current era of improved antifungal therapies. Retrospectively determining the prevalence of fungal ocular involvement is important for informing clinical guidelines, such as the need for routine ophthalmologic consultations. However, manual retrospective record review to detect cases is time-consuming. Objective This study aimed to determine the prevalence of fungal ocular involvement in a critical care database using both structured and unstructured electronic health record (EHR) data. Methods We queried microbiology data from 46,467 critical care patients over 12 years (2000-2012) from the Medical Information Mart for Intensive Care III (MIMIC-III) to identify 265 patients with culture-proven fungemia. For each fungemic patient, demographic data, fungal species present in blood culture, and risk factors for fungemia (eg, presence of indwelling catheters, recent major surgery, diabetes, immunosuppressed status) were ascertained. All structured diagnosis codes and free-text narrative notes associated with each patient’s hospitalization were also extracted. Screening for fungal endophthalmitis was performed using two approaches: (1) by querying a wide array of eye- and vision-related diagnosis codes, and (2) by utilizing a custom regular expression pipeline to identify and collate relevant text matches pertaining to fungal ocular involvement. Both approaches were validated using manual record review. The main outcome measure was the documentation of any fungal ocular involvement. Results In total, 265 patients had culture-proven fungemia, with Candida albicans (n=114, 43%) and Candida glabrata (n=74, 28%) being the most common fungal species in blood culture. The in-hospital mortality rate was 121 (46%). In total, 7 patients were identified as having eye- or vision-related diagnosis codes, none of whom had fungal endophthalmitis based on record review. There were 26,830 free-text narrative notes associated with these 265 patients. A regular expression pipeline based on relevant terms yielded possible matches in 683 notes from 108 patients. Subsequent manual record review again demonstrated that no patients had fungal ocular involvement. Therefore, the prevalence of fungal ocular involvement in this cohort was 0%. Conclusions MIMIC-III contained no cases of ocular involvement among fungemic patients, consistent with prior studies reporting low rates of ocular involvement in fungemia. This study demonstrates an application of natural language processing to expedite the review of narrative notes. This approach is highly relevant for ophthalmology, where diagnoses are often based on physical examination findings that are documented within clinical notes.
Collapse
Affiliation(s)
- Sally L Baxter
- Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, United States.,Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA, United States
| | - Adam R Klie
- Bioinformatics and Systems Biology, University of California San Diego, La Jolla, CA, United States
| | | | - Gordon Y Ye
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA, United States
| | - Michael Hogarth
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA, United States
| |
Collapse
|
12
|
Liang H, Yang L, Tao L, Shi L, Yang W, Bai J, Zheng D, Wang N, Ji J. Data mining-based model and risk prediction of colorectal cancer by using secondary health data: A systematic review. Chin J Cancer Res 2020; 32:242-251. [PMID: 32410801 PMCID: PMC7219096 DOI: 10.21147/j.issn.1000-9604.2020.02.11] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Accepted: 04/01/2020] [Indexed: 01/08/2023] Open
Abstract
OBJECTIVE Prevention and early detection of colorectal cancer (CRC) can increase the chances of successful treatment and reduce burden. Various data mining technologies have been utilized to strengthen the early detection of CRC in primary care. Evidence synthesis on the model's effectiveness is scant. This systematic review synthesizes studies that examine the effect of data mining on improving risk prediction of CRC. METHODS The PRISMA framework guided the conduct of this study. We obtained papers via PubMed, Cochrane Library, EMBASE and Google Scholar. Quality appraisal was performed using Downs and Black's quality checklist. To evaluate the performance of included models, the values of specificity and sensitivity were comparted, the values of area under the curve (AUC) were plotted, and the median of overall AUC of included studies was computed. RESULTS A total of 316 studies were reviewed for full text. Seven articles were included. Included studies implement techniques including artificial neural networks, Bayesian networks and decision trees. Six articles reported the overall model accuracy. Overall, the median AUC is 0.8243 [interquartile range (IQR): 0.8050-0.8886]. In the two articles that reported comparison results with traditional models, the data mining method performed better than the traditional models, with the best AUC improvement of 10.7%. CONCLUSIONS The adoption of data mining technologies for CRC detection is at an early stage. Limited numbers of included articles and heterogeneity of those studies implied that more rigorous research is expected to further investigate the techniques' effects.
Collapse
Affiliation(s)
- Hailun Liang
- School of Public Administration and Policy, Renmin University of China, Beijing 100872, China
| | - Lei Yang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Beijing Office for Cancer Prevention and Control, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Lei Tao
- Department of Public Policy, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - Leiyu Shi
- Johns Hopkins Primary Care Policy Center, Baltimore, MD 21205, USA
| | - Wuyang Yang
- Department of Neurosurgery, Johns Hopkins Medicine, Baltimore, MD 21205, USA
| | - Jiawei Bai
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Da Zheng
- Department of Computer Science, Johns Hopkins Whiting School of Engineering, Baltimore, MD 21205, USA
| | - Ning Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Beijing Office for Cancer Prevention and Control, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Jiafu Ji
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Center of Gastrointestinal Surgery, Peking University Cancer Hospital & Institute, Beijing 100142, China
| |
Collapse
|
13
|
A frame semantic overview of NLP-based information extraction for cancer-related EHR notes. J Biomed Inform 2019; 100:103301. [PMID: 31589927 DOI: 10.1016/j.jbi.2019.103301] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 09/04/2019] [Accepted: 10/03/2019] [Indexed: 02/07/2023]
Abstract
OBJECTIVE There is a lot of information about cancer in Electronic Health Record (EHR) notes that can be useful for biomedical research provided natural language processing (NLP) methods are available to extract and structure this information. In this paper, we present a scoping review of existing clinical NLP literature for cancer. METHODS We identified studies describing an NLP method to extract specific cancer-related information from EHR sources from PubMed, Google Scholar, ACL Anthology, and existing reviews. Two exclusion criteria were used in this study. We excluded articles where the extraction techniques used were too broad to be represented as frames (e.g., document classification) and also where very low-level extraction methods were used (e.g. simply identifying clinical concepts). 78 articles were included in the final review. We organized this information according to frame semantic principles to help identify common areas of overlap and potential gaps. RESULTS Frames were created from the reviewed articles pertaining to cancer information such as cancer diagnosis, tumor description, cancer procedure, breast cancer diagnosis, prostate cancer diagnosis and pain in prostate cancer patients. These frames included both a definition as well as specific frame elements (i.e. extractable attributes). We found that cancer diagnosis was the most common frame among the reviewed papers (36 out of 78), with recent work focusing on extracting information related to treatment and breast cancer diagnosis. CONCLUSION The list of common frames described in this paper identifies important cancer-related information extracted by existing NLP techniques and serves as a useful resource for future researchers requiring cancer information extracted from EHR notes. We also argue, due to the heavy duplication of cancer NLP systems, that a general purpose resource of annotated cancer frames and corresponding NLP tools would be valuable.
Collapse
|
14
|
Cho M, Kim JH, Hong KS, Kim JS, Kong HJ, Kim S. Identification of cecum time-location in a colonoscopy video by deep learning analysis of colonoscope movement. PeerJ 2019; 7:e7256. [PMID: 31392088 PMCID: PMC6673422 DOI: 10.7717/peerj.7256] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Accepted: 06/05/2019] [Indexed: 12/11/2022] Open
Abstract
Background Cecal intubation time is an important component for quality colonoscopy. Cecum is the turning point that determines the insertion and withdrawal phase of the colonoscope. For this reason, obtaining information related with location of the cecum in the endoscopic procedure is very useful. Also, it is necessary to detect the direction of colonoscope's movement and time-location of the cecum. Methods In order to analysis the direction of scope's movement, the Horn-Schunck algorithm was used to compute the pixel's motion change between consecutive frames. Horn-Schunk-algorithm applied images were trained and tested through convolutional neural network deep learning methods, and classified to the insertion, withdrawal and stop movements. Based on the scope's movement, the graph was drawn with a value of +1 for insertion, -1 for withdrawal, and 0 for stop. We regarded the turning point as a cecum candidate point when the total graph area sum in a certain section recorded the lowest. Results A total of 328,927 frame images were obtained from 112 patients. The overall accuracy, drawn from 5-fold cross-validation, was 95.6%. When the value of "t" was 30 s, accuracy of cecum discovery was 96.7%. In order to increase visibility, the movement of the scope was added to summary report of colonoscopy video. Insertion, withdrawal, and stop movements were mapped to each color and expressed with various scale. As the scale increased, the distinction between the insertion phase and the withdrawal phase became clearer. Conclusion Information obtained in this study can be utilized as metadata for proficiency assessment. Since insertion and withdrawal are technically different movements, data of scope's movement and phase can be quantified and utilized to express pattern unique to the colonoscopist and to assess proficiency. Also, we hope that the findings of this study can contribute to the informatics field of medical records so that medical charts can be transmitted graphically and effectively in the field of colonoscopy.
Collapse
Affiliation(s)
- Minwoo Cho
- Interdisciplinary Program for Bioengineering, Graduate School, Seoul National University, Seoul, South Korea
| | - Jee Hyun Kim
- Department of Gastroenterology, Seoul National University Boramae Medical Center, Seoul, South Korea
| | - Kyoung Sup Hong
- Department of Gastroenterology, Mediplex Sejong Hospital, Incheon, South Korea
| | - Joo Sung Kim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Hyoun-Joong Kong
- Department of Biomedical Engineering, Chungnam National University College of Medicine, Daejeon, South Korea
| | - Sungwan Kim
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, South Korea
| |
Collapse
|
15
|
Sheikhalishahi S, Miotto R, Dudley JT, Lavelli A, Rinaldi F, Osmani V. Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review. JMIR Med Inform 2019; 7:e12239. [PMID: 31066697 PMCID: PMC6528438 DOI: 10.2196/12239] [Citation(s) in RCA: 226] [Impact Index Per Article: 45.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Revised: 03/04/2019] [Accepted: 03/24/2019] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset. OBJECTIVE The goal of the research was to provide a comprehensive overview of the development and uptake of NLP methods applied to free-text clinical notes related to chronic diseases, including the investigation of challenges faced by NLP methodologies in understanding clinical narratives. METHODS Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed and searches were conducted in 5 databases using "clinical notes," "natural language processing," and "chronic disease" and their variations as keywords to maximize coverage of the articles. RESULTS Of the 2652 articles considered, 106 met the inclusion criteria. Review of the included papers resulted in identification of 43 chronic diseases, which were then further classified into 10 disease categories using the International Classification of Diseases, 10th Revision. The majority of studies focused on diseases of the circulatory system (n=38) while endocrine and metabolic diseases were fewest (n=14). This was due to the structure of clinical records related to metabolic diseases, which typically contain much more structured data, compared with medical records for diseases of the circulatory system, which focus more on unstructured data and consequently have seen a stronger focus of NLP. The review has shown that there is a significant increase in the use of machine learning methods compared to rule-based approaches; however, deep learning methods remain emergent (n=3). Consequently, the majority of works focus on classification of disease phenotype with only a handful of papers addressing extraction of comorbidities from the free text or integration of clinical notes with structured data. There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretability of predictions, which still represents a significant issue for more complex methods. Finally, scarcity of publicly available data may also have contributed to insufficient development of more advanced methods, such as extraction of word embeddings from clinical notes. CONCLUSIONS Efforts are still required to improve (1) progression of clinical NLP methods from extraction toward understanding; (2) recognition of relations among entities rather than entities in isolation; (3) temporal extraction to understand past, current, and future clinical events; (4) exploitation of alternative sources of clinical knowledge; and (5) availability of large-scale, de-identified clinical corpora.
Collapse
Affiliation(s)
- Seyedmostafa Sheikhalishahi
- eHealth Research Group, Fondazione Bruno Kessler Research Institute, Trento, Italy
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Riccardo Miotto
- Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Joel T Dudley
- Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Alberto Lavelli
- NLP Research Group, Fondazione Bruno Kessler Research Institute, Trento, Italy
| | - Fabio Rinaldi
- Institute of Computational Linguistics, University of Zurich, Zurich, Switzerland
| | - Venet Osmani
- eHealth Research Group, Fondazione Bruno Kessler Research Institute, Trento, Italy
| |
Collapse
|
16
|
Carlson J, Laryea J. Electronic Health Record-Based Registries: Clinical Research Using Registries in Colon and Rectal Surgery. Clin Colon Rectal Surg 2019; 32:82-90. [PMID: 30647550 DOI: 10.1055/s-0038-1673358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Electronic health records (EHRs) or electronic medical records (EMRs) contain a vast amount of clinical data that can be useful for multiple purposes including research. Disease registries are collections of data in predefined formats for population management, research, and other purposes. There are differences between EHRs and registries in the data structure, data standards, and protocols. Proprietary EHR systems use different coding systems and data standards, which are usually kept secret. For EHR data to flow seamlessly into registries, there is the need for interoperability between EHR systems and between EHRs and registries. The levels of interoperability required include functional, structural, and semantic interoperability. EHR data can be manually mapped to registry data, but that is a tedious, resource-intensive endeavor. The development of data standards that can be used as building blocks for both EHRs and registries will help overcome the problem of interoperability.
Collapse
Affiliation(s)
- Jacob Carlson
- Department of Surgery, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Jonathan Laryea
- Department of Surgery, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| |
Collapse
|
17
|
Adekkanattu P, Sholle ET, DeFerio J, Pathak J, Johnson SB, Campion TR. Ascertaining Depression Severity by Extracting Patient Health Questionnaire-9 (PHQ-9) Scores from Clinical Notes. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2018; 2018:147-156. [PMID: 30815052 PMCID: PMC6371338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The Patient Health Questionnaire-9 (PHQ-9) is a validated instrument for assessing depression severity. While some electronic health record (EHR) systems capture PHQ-9 scores in a structured format, unstructured clinical notes remain the only source in many settings, which presents data retrieval challenges for research and clinical decision support. To address this gap, we extended the open-source Leo natural language processing (NLP) platform to extract PHQ-9 scores from clinical notes and evaluated performance using EHR data for n=123,703 patients who were prescribed antidepressants. Compared to a reference standard, the NLP method exhibited high accuracy (97%), sensitivity (98%), precision (97%), and F-score (97%). Furthermore, of patients with PHQ-9 scores identified by the NLP method, 31% (n=498) had at least one PHQ-9 score clinically indicative of major depressive disorder (MDD), but lacked a structured ICD-9/10 diagnosis code for MDD. This NLP technique may facilitate accurate identification and stratification of patients with depression.
Collapse
Affiliation(s)
- Prakash Adekkanattu
- Information Technologies and Services Department, Weill Cornell Medicine, New York, NY
| | - Evan T Sholle
- Information Technologies and Services Department, Weill Cornell Medicine, New York, NY
| | - Joseph DeFerio
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY
| | - Jyotishman Pathak
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY
- Clinical and Translational Science Center, Weill Cornell Medicine, New York, NY
| | - Stephen B Johnson
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY
- Clinical and Translational Science Center, Weill Cornell Medicine, New York, NY
| | - Thomas R Campion
- Information Technologies and Services Department, Weill Cornell Medicine, New York, NY
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY
- Clinical and Translational Science Center, Weill Cornell Medicine, New York, NY
- Department of Pediatrics, Weill Cornell Medicine, New York, NY
| |
Collapse
|
18
|
Smith JC, Chen Q, Denny JC, Roden DM, Johnson KB, Miller RA. Evaluation of a Novel System to Enhance Clinicians' Recognition of Preadmission Adverse Drug Reactions. Appl Clin Inform 2018; 9:313-325. [PMID: 29742757 DOI: 10.1055/s-0038-1646963] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Often unrecognized by providers, adverse drug reactions (ADRs) diminish patients' quality of life, cause preventable admissions and emergency department visits, and increase health care costs. OBJECTIVE This article evaluates whether an automated system, the Adverse Drug Effect Recognizer (ADER), could assist clinicians in detecting and addressing inpatients' ongoing preadmission ADRs. METHODS ADER uses natural language processing to extract patients' medications, findings, and past diagnoses from admission notes. It compares excerpted information to a database of known medication adverse effects and promptly warns clinicians about potential ongoing ADRs and potential confounders via alerts placed in patients' electronic health records (EHRs). A 3-month intervention trial evaluated ADER's impact on antihypertensive medication ordering behaviors. At the time of patient admission, ADER warned providers on the Internal Medicine wards of Vanderbilt University Hospital about potential ongoing preadmission antihypertensive medication ADRs. A retrospective control group, comprised similar physicians from a period prior to the intervention, received no alerts. The evaluation compared ordering behaviors for each group to determine if preadmission medications changed during hospitalization or at discharge. The study also analyzed intervention group participants' survey responses and user comments. RESULTS ADER identified potential preadmission ADRs for 30% of both groups. Compared with controls, intervention providers more often withheld or discontinued suspected ADR-causing medications during the inpatient stay (p < 0.001). Intervention providers who responded to alert-related surveys held or discontinued suspected ADR-causing medications more often at discharge (p < 0.001). CONCLUSION Results indicate that ADER helped physicians recognize ADRs and reduced ordering of suspected ADR-causing medications. In hospitals using EHRs, ADER-like systems could improve clinicians' recognition and elimination of ongoing ADRs.
Collapse
Affiliation(s)
- Joshua C Smith
- Department of Biomedical Informatics, Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - Qingxia Chen
- Department of Biomedical Informatics, Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee, United States.,Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee, United States.,Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - Dan M Roden
- Department of Biomedical Informatics, Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee, United States.,Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States.,Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - Kevin B Johnson
- Department of Biomedical Informatics, Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee, United States.,Department of Pediatrics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - Randolph A Miller
- Department of Biomedical Informatics, Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee, United States.,Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States.,School of Nursing, Vanderbilt University, Nashville, Tennessee, United States
| |
Collapse
|
19
|
Robinson JR, Wei WQ, Roden DM, Denny JC. Defining Phenotypes from Clinical Data to Drive Genomic Research. Annu Rev Biomed Data Sci 2018; 1:69-92. [PMID: 34109303 DOI: 10.1146/annurev-biodatasci-080917-013335] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The rise in available longitudinal patient information in electronic health records (EHRs) and their coupling to DNA biobanks has resulted in a dramatic increase in genomic research using EHR data for phenotypic information. EHRs have the benefit of providing a deep and broad data source of health-related phenotypes, including drug response traits, expanding the phenome available to researchers for discovery. The earliest efforts at repurposing EHR data for research involved manual chart review of limited numbers of patients but now typically involve applications of rule-based and machine learning algorithms operating on sometimes huge corpora for both genome-wide and phenome-wide approaches. We highlight here the current methods, impact, challenges, and opportunities for repurposing clinical data to define patient phenotypes for genomics discovery. Use of EHR data has proven a powerful method for elucidation of genomic influences on diseases, traits, and drug-response phenotypes and will continue to have increasing applications in large cohort studies.
Collapse
Affiliation(s)
- Jamie R Robinson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN.,Department of General Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Dan M Roden
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN.,Department of Medicine, Vanderbilt University Medical Center, Nashville, TN.,Department of Pharmacology, Vanderbilt University Medical Center
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN.,Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| |
Collapse
|
20
|
Geraci J, Wilansky P, de Luca V, Roy A, Kennedy JL, Strauss J. Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression. EVIDENCE-BASED MENTAL HEALTH 2017; 20:83-87. [PMID: 28739578 PMCID: PMC5566092 DOI: 10.1136/eb-2017-102688] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Revised: 06/12/2017] [Accepted: 06/21/2017] [Indexed: 01/11/2023]
Abstract
Background We report a study of machine learning applied to the phenotyping of psychiatric diagnosis for research recruitment in youth depression, conducted with 861 labelled electronic medical records (EMRs) documents. A model was built that could accurately identify individuals who were suitable candidates for a study on youth depression. Objective Our objective was a model to identify individuals who meet inclusion criteria as well as unsuitable patients who would require exclusion. Methods Our methods included applying a system that coded the EMR documents by removing personally identifying information, using two psychiatrists who labelled a set of EMR documents (from which the 861 came), using a brute force search and training a deep neural network for this task. Findings According to a cross-validation evaluation, we describe a model that had a specificity of 97% and a sensitivity of 45% and a second model with a specificity of 53% and a sensitivity of 89%. We combined these two models into a third one (sensitivity 93.5%; specificity 68%; positive predictive value (precision) 77%) to generate a list of most suitable candidates in support of research recruitment. Conclusion Our efforts are meant to demonstrate the potential for this type of approach for patient recruitment purposes but it should be noted that a larger sample size is required to build a truly reliable recommendation system. Clinical implications Future efforts will employ alternate neural network algorithms available and other machine learning methods.
Collapse
Affiliation(s)
- Joseph Geraci
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Department of Pathology and Molecular Medicine, Queen's University, Kingston, New York, Canada.,Shannon Centennial Informatics Lab, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Pamela Wilansky
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Vincenzo de Luca
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Anvesh Roy
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - James L Kennedy
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - John Strauss
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Shannon Centennial Informatics Lab, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Cundill Centre for Child and Youth Depression, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| |
Collapse
|
21
|
Seol JW, Yi W, Choi J, Lee KS. Causality patterns and machine learning for the extraction of problem-action relations in discharge summaries. Int J Med Inform 2016; 98:1-12. [PMID: 28034407 DOI: 10.1016/j.ijmedinf.2016.10.021] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Revised: 08/19/2016] [Accepted: 10/29/2016] [Indexed: 10/20/2022]
Abstract
Clinical narrative text includes information related to a patient's medical history such as chronological progression of medical problems and clinical treatments. A chronological view of a patient's history makes clinical audits easier and improves quality of care. In this paper, we propose a clinical Problem-Action relation extraction method, based on clinical semantic units and event causality patterns, to present a chronological view of a patient's problem and a doctor's action. Based on our observation that a clinical text describes a patient's medical problems and a doctor's treatments in chronological order, a clinical semantic unit is defined as a problem and/or an action relation. Since a clinical event is a basic unit of the problem and action relation, events are extracted from narrative texts, based on the external knowledge resources context features of the conditional random fields. A clinical semantic unit is extracted from each sentence based on time expressions and context structures of events. Then, a clinical semantic unit is classified into a problem and/or action relation based on the event causality patterns of the support vector machines. Experimental results on Korean discharge summaries show 78.8% performance in the F1-measure. This result shows that the proposed method is effectively classifies clinical Problem-Action relations.
Collapse
Affiliation(s)
- Jae-Wook Seol
- Department of Information Convergence Research, Korea Institute of Science and Technology Information 245, Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.
| | - Wangjin Yi
- Interdisciplinary Program of Bioengineering, College of Engineering, Seoul National University 103, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
| | - Jinwook Choi
- Dept. of Biomedical Engineering, College of Medicine, Seoul National University 103, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
| | - Kyung Soon Lee
- Department of Computer Engineering, CAIIT, Chonbuk National University 567, Baekjedae-ro, Deokjin-gu, Jeonju, Jeollabukdo, 54896, Republic of Korea.
| |
Collapse
|
22
|
Arsoniadis EG, Melton GB. Leveraging the electronic health record for research and quality improvement: Current strengths and future challenges. SEMINARS IN COLON AND RECTAL SURGERY 2016. [DOI: 10.1053/j.scrs.2016.01.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
23
|
Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 939:139-166. [PMID: 27807747 DOI: 10.1007/978-981-10-1503-8_7] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
The key question of precision medicine is whether it is possible to find clinically actionable granularity in diagnosing disease and classifying patient risk. The advent of next-generation sequencing and the widespread adoption of electronic health records (EHRs) have provided clinicians and researchers a wealth of data and made possible the precise characterization of individual patient genotypes and phenotypes. Unstructured text-found in biomedical publications and clinical notes-is an important component of genotype and phenotype knowledge. Publications in the biomedical literature provide essential information for interpreting genetic data. Likewise, clinical notes contain the richest source of phenotype information in EHRs. Text mining can render these texts computationally accessible and support information extraction and hypothesis generation. This chapter reviews the mechanics of text mining in precision medicine and discusses several specific use cases, including database curation for personalized cancer medicine, patient outcome prediction from EHR-derived cohorts, and pharmacogenomic research. Taken as a whole, these use cases demonstrate how text mining enables effective utilization of existing knowledge sources and thus promotes increased value for patients and healthcare systems. Text mining is an indispensable tool for translating genotype-phenotype data into effective clinical care that will undoubtedly play an important role in the eventual realization of precision medicine.
Collapse
|
24
|
Yang H, Garibaldi JM. A hybrid model for automatic identification of risk factors for heart disease. J Biomed Inform 2015; 58 Suppl:S171-S182. [PMID: 26375492 PMCID: PMC4989091 DOI: 10.1016/j.jbi.2015.09.006] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2015] [Revised: 09/03/2015] [Accepted: 09/04/2015] [Indexed: 11/23/2022]
Abstract
Coronary artery disease (CAD) is the leading cause of death in both the UK and worldwide. The detection of related risk factors and tracking their progress over time is of great importance for early prevention and treatment of CAD. This paper describes an information extraction system that was developed to automatically identify risk factors for heart disease in medical records while the authors participated in the 2014 i2b2/UTHealth NLP Challenge. Our approaches rely on several nature language processing (NLP) techniques such as machine learning, rule-based methods, and dictionary-based keyword spotting to cope with complicated clinical contexts inherent in a wide variety of risk factors. Our system achieved encouraging performance on the challenge test data with an overall micro-averaged F-measure of 0.915, which was competitive to the best system (F-measure of 0.927) of this challenge task.
Collapse
Affiliation(s)
- Hui Yang
- School of Computer Science, University of Nottingham, Nottingham, UK; Advanced Data Analysis Centre, University of Nottingham, Nottingham, UK.
| | - Jonathan M Garibaldi
- School of Computer Science, University of Nottingham, Nottingham, UK; Advanced Data Analysis Centre, University of Nottingham, Nottingham, UK
| |
Collapse
|
25
|
Mo H, Thompson WK, Rasmussen LV, Pacheco JA, Jiang G, Kiefer R, Zhu Q, Xu J, Montague E, Carrell DS, Lingren T, Mentch FD, Ni Y, Wehbe FH, Peissig PL, Tromp G, Larson EB, Chute CG, Pathak J, Denny JC, Speltz P, Kho AN, Jarvik GP, Bejan CA, Williams MS, Borthwick K, Kitchner TE, Roden DM, Harris PA. Desiderata for computable representations of electronic health records-driven phenotype algorithms. J Am Med Inform Assoc 2015; 22:1220-30. [PMID: 26342218 PMCID: PMC4639716 DOI: 10.1093/jamia/ocv112] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2015] [Accepted: 06/24/2015] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM). METHODS A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms. RESULTS We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility. CONCLUSION A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.
Collapse
Affiliation(s)
- Huan Mo
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
| | - William K Thompson
- Center for Biomedical Research Informatics, NorthShore University HealthSystem, Evanston, IL, USA
| | - Luke V Rasmussen
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Jennifer A Pacheco
- Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Guoqian Jiang
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Richard Kiefer
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Qian Zhu
- Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, USA
| | - Jie Xu
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Enid Montague
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Todd Lingren
- Division of Biomedical Informatics, Cincinnati Children's Hospital, Cincinnati, OH, USA
| | - Frank D Mentch
- Center for Applied Genomics, the Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Yizhao Ni
- Division of Biomedical Informatics, Cincinnati Children's Hospital, Cincinnati, OH, USA
| | - Firas H Wehbe
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Peggy L Peissig
- Marshfield Clinic Research Foundation, Marshfield Clinic, Marshfield, WI, USA
| | - Gerard Tromp
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, University of Stellenbosch, Cape Town, South Africa
| | | | - Christopher G Chute
- Division of General Internal Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Jyotishman Pathak
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA Department of Medicine, Vanderbilt University, Nashville, TN, USA
| | - Peter Speltz
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
| | - Abel N Kho
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Gail P Jarvik
- Department of Medicine (Medical Genetics), University of Washington, Seattle, WA, USA Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Cosmin A Bejan
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
| | - Marc S Williams
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Kenneth Borthwick
- The Sigfried and Janet Weis Center for Research, Geisinger Health System, Danville, PA, USA
| | - Terrie E Kitchner
- Marshfield Clinic Research Foundation, Marshfield Clinic, Marshfield, WI, USA
| | - Dan M Roden
- Department of Medicine, Vanderbilt University, Nashville, TN, USA Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
| | - Paul A Harris
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
| |
Collapse
|
26
|
Wasfy JH, Singal G, O'Brien C, Blumenthal DM, Kennedy KF, Strom JB, Spertus JA, Mauri L, Normand SLT, Yeh RW. Enhancing the Prediction of 30-Day Readmission After Percutaneous Coronary Intervention Using Data Extracted by Querying of the Electronic Health Record. Circ Cardiovasc Qual Outcomes 2015; 8:477-85. [PMID: 26286871 DOI: 10.1161/circoutcomes.115.001855] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2014] [Accepted: 06/22/2015] [Indexed: 01/24/2023]
Abstract
BACKGROUND Early readmission after percutaneous coronary intervention is an important quality metric, but prediction models from registry data have only moderate discrimination. We aimed to improve ability to predict 30-day readmission after percutaneous coronary intervention from a previously validated registry-based model. METHODS AND RESULTS We matched readmitted to non-readmitted patients in a 1:2 ratio by risk of readmission, and extracted unstructured and unconventional structured data from the electronic medical record, including need for medical interpretation, albumin level, medical nonadherence, previous number of emergency department visits, atrial fibrillation/flutter, syncope/presyncope, end-stage liver disease, malignancy, and anxiety. We assessed differences in rates of these conditions between cases/controls, and estimated their independent association with 30-day readmission using logistic regression conditional on matched groups. Among 9288 percutaneous coronary interventions, we matched 888 readmitted with 1776 non-readmitted patients. In univariate analysis, cases and controls were significantly different with respect to interpreter (7.9% for cases and 5.3% for controls; P=0.009), emergency department visits (1.12 for cases and 0.77 for controls; P<0.001), homelessness (3.2% for cases and 1.6% for controls; P=0.007), anticoagulation (33.9% for cases and 22.1% for controls; P<0.001), atrial fibrillation/flutter (32.7% for cases and 28.9% for controls; P=0.045), presyncope/syncope (27.8% for cases and 21.3% for controls; P<0.001), and anxiety (69.4% for cases and 62.4% for controls; P<0.001). Anticoagulation, emergency department visits, and anxiety were independently associated with readmission. CONCLUSIONS Patient characteristics derived from review of the electronic health record can be used to refine risk prediction for hospital readmission after percutaneous coronary intervention.
Collapse
Affiliation(s)
- Jason H Wasfy
- From the Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston (J.H.W., C.O'B., D.M.B., R.W.Y.), Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (L.M.), Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston (G.S.), Cardiovascular Division, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA (J.B.S.), Saint Luke's Mid America Heart Institute/UMKC, Kansas City, MO (K.F.K., J.A.S.); and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (S.-L.T.N.)
| | - Gaurav Singal
- From the Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston (J.H.W., C.O'B., D.M.B., R.W.Y.), Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (L.M.), Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston (G.S.), Cardiovascular Division, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA (J.B.S.), Saint Luke's Mid America Heart Institute/UMKC, Kansas City, MO (K.F.K., J.A.S.); and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (S.-L.T.N.)
| | - Cashel O'Brien
- From the Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston (J.H.W., C.O'B., D.M.B., R.W.Y.), Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (L.M.), Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston (G.S.), Cardiovascular Division, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA (J.B.S.), Saint Luke's Mid America Heart Institute/UMKC, Kansas City, MO (K.F.K., J.A.S.); and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (S.-L.T.N.)
| | - Daniel M Blumenthal
- From the Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston (J.H.W., C.O'B., D.M.B., R.W.Y.), Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (L.M.), Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston (G.S.), Cardiovascular Division, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA (J.B.S.), Saint Luke's Mid America Heart Institute/UMKC, Kansas City, MO (K.F.K., J.A.S.); and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (S.-L.T.N.)
| | - Kevin F Kennedy
- From the Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston (J.H.W., C.O'B., D.M.B., R.W.Y.), Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (L.M.), Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston (G.S.), Cardiovascular Division, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA (J.B.S.), Saint Luke's Mid America Heart Institute/UMKC, Kansas City, MO (K.F.K., J.A.S.); and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (S.-L.T.N.)
| | - Jordan B Strom
- From the Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston (J.H.W., C.O'B., D.M.B., R.W.Y.), Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (L.M.), Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston (G.S.), Cardiovascular Division, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA (J.B.S.), Saint Luke's Mid America Heart Institute/UMKC, Kansas City, MO (K.F.K., J.A.S.); and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (S.-L.T.N.)
| | - John A Spertus
- From the Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston (J.H.W., C.O'B., D.M.B., R.W.Y.), Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (L.M.), Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston (G.S.), Cardiovascular Division, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA (J.B.S.), Saint Luke's Mid America Heart Institute/UMKC, Kansas City, MO (K.F.K., J.A.S.); and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (S.-L.T.N.)
| | - Laura Mauri
- From the Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston (J.H.W., C.O'B., D.M.B., R.W.Y.), Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (L.M.), Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston (G.S.), Cardiovascular Division, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA (J.B.S.), Saint Luke's Mid America Heart Institute/UMKC, Kansas City, MO (K.F.K., J.A.S.); and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (S.-L.T.N.)
| | - Sharon-Lise T Normand
- From the Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston (J.H.W., C.O'B., D.M.B., R.W.Y.), Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (L.M.), Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston (G.S.), Cardiovascular Division, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA (J.B.S.), Saint Luke's Mid America Heart Institute/UMKC, Kansas City, MO (K.F.K., J.A.S.); and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (S.-L.T.N.)
| | - Robert W Yeh
- From the Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston (J.H.W., C.O'B., D.M.B., R.W.Y.), Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (L.M.), Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston (G.S.), Cardiovascular Division, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA (J.B.S.), Saint Luke's Mid America Heart Institute/UMKC, Kansas City, MO (K.F.K., J.A.S.); and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (S.-L.T.N.).
| |
Collapse
|
27
|
Kim MY, Xu Y, Zaiane OR, Goebel R. Recognition of Patient-Related Named Entities in Noisy Tele-Health Texts. ACM T INTEL SYST TEC 2015. [DOI: 10.1145/2651444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
We explore methods for effectively extracting information from clinical narratives that are captured in a public health consulting phone service called HealthLink. Our research investigates the application of state-of-the-art natural language processing and machine learning to clinical narratives to extract information of interest. The currently available data consist of dialogues constructed by nurses while consulting patients by phone. Since the data are interviews transcribed by nurses during phone conversations, they include a significant volume and variety of noise. When we extract the patient-related information from the noisy data, we have to remove or correct at least two kinds of noise:
explicit noise
, which includes spelling errors, unfinished sentences, omission of sentence delimiters, and variants of terms, and
implicit noise
, which includes non-patient information and patient's untrustworthy information. To filter explicit noise, we propose our own biomedical term detection/normalization method: it resolves misspelling, term variations, and arbitrary abbreviation of terms by nurses. In detecting temporal terms, temperature, and other types of named entities (which show patients’ personal information such as age and sex), we propose a bootstrapping-based pattern learning process to detect a variety of arbitrary variations of named entities. To address implicit noise, we propose a dependency path-based filtering method. The result of our denoising is the extraction of normalized patient information, and we visualize the named entities by constructing a graph that shows the relations between named entities. The objective of this knowledge discovery task is to identify associations between biomedical terms and to clearly expose the trends of patients’ symptoms and concern; the experimental results show that we achieve reasonable performance with our noise reduction methods.
Collapse
Affiliation(s)
| | - Ying Xu
- University of Alberta, Canada
| | | | | |
Collapse
|
28
|
Predicting Non-Adherence with Outpatient Colonoscopy Using a Novel Electronic Tool that Measures Prior Non-Adherence. J Gen Intern Med 2015; 30:724-31. [PMID: 25586869 PMCID: PMC4441666 DOI: 10.1007/s11606-014-3165-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2013] [Revised: 07/09/2014] [Accepted: 12/08/2014] [Indexed: 10/25/2022]
Abstract
BACKGROUND Accurately predicting the risk of no-show for a scheduled colonoscopy can help target interventions to improve compliance with colonoscopy, and thereby reduce the disease burden of colorectal cancer and enhance the utilization of resources within endoscopy units. OBJECTIVES We aimed to utilize information available in an electronic medical record (EMR) and endoscopy scheduling system to create a predictive model for no-show risk, and to simultaneously evaluate the role for natural language processing (NLP) in developing such a model. DESIGN This was a retrospective observational study using discovery and validation phases to design a colonoscopy non-adherence prediction model. An NLP-derived variable called the Non-Adherence Ratio ("NAR") was developed, validated, and included in the model. PARTICIPANTS Patients scheduled for outpatient colonoscopy at an Academic Medical Center (AMC) that is part of a multi-hospital health system, 2009 to 2011, were included in the study. MAIN MEASURES Odds ratios for non-adherence were calculated for all variables in the discovery cohort, and an Area Under the Receiver Operating Curve (AUC) was calculated for the final non-adherence prediction model. KEY RESULTS The non-adherence model included six variables: 1) gender; 2) history of psychiatric illness, 3) NAR; 4) wait time in months; 5) number of prior missed endoscopies; and 6) education level. The model achieved discrimination in the validation cohort (AUC= =70.2 %). At a threshold non-adherence score of 0.46, the model's sensitivity and specificity were 33 % and 92 %, respectively. Removing the NAR from the model significantly reduced its predictive power (AUC = 64.3 %, difference = 5.9 %, p < 0.001). CONCLUSIONS A six-variable model using readily available clinical and demographic information demonstrated accuracy for predicting colonoscopy non-adherence. The NAR, a novel variable developed using NLP technology, significantly strengthened this model's predictive power.
Collapse
|
29
|
Imler TD, Morea J, Kahi C, Sherer EA, Cardwell J, Johnson CS, Xu H, Ahnen D, Antaki F, Ashley C, Baffy G, Cho I, Dominitz J, Hou J, Korsten M, Nagar A, Promrat K, Robertson D, Saini S, Shergill A, Smalley W, Imperiale TF. Multi-center colonoscopy quality measurement utilizing natural language processing. Am J Gastroenterol 2015; 110:543-52. [PMID: 25756240 DOI: 10.1038/ajg.2015.51] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2014] [Accepted: 01/02/2015] [Indexed: 12/11/2022]
Abstract
BACKGROUND An accurate system for tracking of colonoscopy quality and surveillance intervals could improve the effectiveness and cost-effectiveness of colorectal cancer (CRC) screening and surveillance. The purpose of this study was to create and test such a system across multiple institutions utilizing natural language processing (NLP). METHODS From 42,569 colonoscopies with pathology records from 13 centers, we randomly sampled 750 paired reports. We trained (n=250) and tested (n=500) an NLP-based program with 19 measurements that encompass colonoscopy quality measures and surveillance interval determination, using blinded, paired, annotated expert manual review as the reference standard. The remaining 41,819 nonannotated documents were processed through the NLP system without manual review to assess performance consistency. The primary outcome was system accuracy across the 19 measures. RESULTS A total of 176 (23.5%) documents with 252 (1.8%) discrepant content points resulted from paired annotation. Error rate within the 500 test documents was 31.2% for NLP and 25.4% for the paired annotators (P=0.001). At the content point level within the test set, the error rate was 3.5% for NLP and 1.9% for the paired annotators (P=0.04). When eight vaguely worded documents were removed, 125 of 492 (25.4%) were incorrect by NLP and 104 of 492 (21.1%) by the initial annotator (P=0.07). Rates of pathologic findings calculated from NLP were similar to those calculated by annotation for the majority of measurements. Test set accuracy was 99.6% for CRC, 95% for advanced adenoma, 94.6% for nonadvanced adenoma, 99.8% for advanced sessile serrated polyps, 99.2% for nonadvanced sessile serrated polyps, 96.8% for large hyperplastic polyps, and 96.0% for small hyperplastic polyps. Lesion location showed high accuracy (87.0-99.8%). Accuracy for number of adenomas was 92%. CONCLUSIONS NLP can accurately report adenoma detection rate and the components for determining guideline-adherent colonoscopy surveillance intervals across multiple sites that utilize different methods for reporting colonoscopy findings.
Collapse
Affiliation(s)
- Timothy D Imler
- 1] Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, Indiana, USA [2] Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA [3] Department of Biomedical Informatics, Regenstrief Institute, LLC, Indianapolis, Indiana, USA
| | - Justin Morea
- 1] Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA [2] Department of Biomedical Informatics, Regenstrief Institute, LLC, Indianapolis, Indiana, USA
| | - Charles Kahi
- 1] Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, Indiana, USA [2] Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA [3] Center of Innovation, Health Services Research and Development, Richard L, Roudebush VA Medical Center, Indianapolis, Indiana, USA
| | | | - Jon Cardwell
- Center of Innovation, Health Services Research and Development, Richard L, Roudebush VA Medical Center, Indianapolis, Indiana, USA
| | - Cynthia S Johnson
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Huiping Xu
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Dennis Ahnen
- Division of Gastroenterology, University of Colorado, Denver, Colorado, USA
| | - Fadi Antaki
- Division of Gastroenterology, Wayne State University, Detroit, Michigan, USA
| | - Christopher Ashley
- Division of Gastroenterology, Albany Medical College, Albany, New York, USA
| | - Gyorgy Baffy
- Department of Medicine, VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Ilseung Cho
- Division of Gastroenterology, New York University School of Medicine, New York, New York, USA
| | - Jason Dominitz
- Division of Gastroenterology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Jason Hou
- Division of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, Texas, USA
| | - Mark Korsten
- Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, Bronx, New York, USA
| | - Anil Nagar
- Division of Digestive Diseases, Yale School of Medicine, New Haven, Connecticut, USA
| | - Kittichai Promrat
- Division of Gastroenterology, Brown Medical School, Providence, Rhode Island, USA
| | - Douglas Robertson
- Division of Gastroenterology, The Dartmouth Institute, Lebanon, New Hampshire, USA
| | - Sameer Saini
- Division of Gastroenterology, University of Michigan, Ann Arbor, Michigan, USA
| | - Amandeep Shergill
- Division of Gastroenterology, University of California at San Francisco, San Francisco, California, USA
| | - Walter Smalley
- Division of Gastroenterology, Vanderbilt University, Nashville, Tennessee, USA
| | - Thomas F Imperiale
- 1] Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, Indiana, USA [2] Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA [3] Center of Innovation, Health Services Research and Development, Richard L, Roudebush VA Medical Center, Indianapolis, Indiana, USA [4] Health Services Research, Regenstrief Institute, Indianapolis, Indiana, USA
| |
Collapse
|
30
|
Bowton E, Field JR, Wang S, Schildcrout JS, Van Driest SL, Delaney JT, Cowan J, Weeke P, Mosley JD, Wells QS, Karnes JH, Shaffer C, Peterson JF, Denny JC, Roden DM, Pulley JM. Biobanks and electronic medical records: enabling cost-effective research. Sci Transl Med 2014; 6:234cm3. [PMID: 24786321 DOI: 10.1126/scitranslmed.3008604] [Citation(s) in RCA: 106] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
The use of electronic medical record data linked to biological specimens in health care settings is expected to enable cost-effective and rapid genomic analyses. Here, we present a model that highlights potential advantages for genomic discovery and describe the operational infrastructure that facilitated multiple simultaneous discovery efforts.
Collapse
Affiliation(s)
- Erica Bowton
- Institute for Clinical and Translational Research, School of Medicine, Vanderbilt University, Nashville, TN 37232, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
31
|
Hou JK, Imler TD, Imperiale TF. Current and future applications of natural language processing in the field of digestive diseases. Clin Gastroenterol Hepatol 2014; 12:1257-61. [PMID: 24858706 DOI: 10.1016/j.cgh.2014.05.013] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2014] [Accepted: 05/15/2014] [Indexed: 02/07/2023]
Abstract
Natural language processing (NLP) is a technology that uses computer-based linguistics and artificial intelligence to identify and extract information from free-text data sources such as progress notes, procedure and pathology reports, and laboratory and radiologic test results. With the creation of large databases and the trajectory of health care reform, NLP holds the promise of enhancing the availability, quality, and utility of clinical information with the goal of improving documentation, quality, and efficiency of health care in the United States. To date, NLP has shown promise in automatically determining appropriate colonoscopy intervals and identifying cases of inflammatory bowel disease from electronic health records. The objectives of this review are to provide background on NLP and its associated terminology, to describe how NLP has been used thus far in the field of digestive diseases, and to identify its potential future uses.
Collapse
Affiliation(s)
- Jason K Hou
- Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas; Department of Medicine, Baylor College of Medicine, Houston, Texas.
| | - Timothy D Imler
- Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, Indiana; Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana; Department of Biomedical Informatics, Regenstrief Institute, LLC, Indianapolis, Indiana
| | - Thomas F Imperiale
- Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, Indiana; Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana; Center of Innovation, Health Services Research and Development, Richard L. Roudeboush Veterans Affairs Medical Center, Indianapolis, Indiana
| |
Collapse
|
32
|
Heintzman J, Bailey SR, Hoopes MJ, Le T, Gold R, O'Malley JP, Cowburn S, Marino M, Krist A, DeVoe JE. Agreement of Medicaid claims and electronic health records for assessing preventive care quality among adults. J Am Med Inform Assoc 2014; 21:720-4. [PMID: 24508767 PMCID: PMC4078280 DOI: 10.1136/amiajnl-2013-002333] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2013] [Revised: 12/23/2013] [Accepted: 01/20/2014] [Indexed: 11/03/2022] Open
Abstract
To compare the agreement of electronic health record (EHR) data versus Medicaid claims data in documenting adult preventive care. Insurance claims are commonly used to measure care quality. EHR data could serve this purpose, but little information exists about how this source compares in service documentation. For 13 101 Medicaid-insured adult patients attending 43 Oregon community health centers, we compared documentation of 11 preventive services, based on EHR versus Medicaid claims data. Documentation was comparable for most services. Agreement was highest for influenza vaccination (κ = 0.77; 95% CI 0.75 to 0.79), cholesterol screening (κ = 0.80; 95% CI 0.79 to 0.81), and cervical cancer screening (κ = 0.71; 95% CI 0.70 to 0.73), and lowest on services commonly referred out of primary care clinics and those that usually do not generate claims. EHRs show promise for use in quality reporting. Strategies to maximize data capture in EHRs are needed to optimize the use of EHR data for service documentation.
Collapse
Affiliation(s)
- John Heintzman
- Department of Family Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | - Steffani R Bailey
- Department of Family Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | | | - Thuy Le
- OCHIN, Inc, Portland, Oregon, USA
| | - Rachel Gold
- OCHIN, Inc, Portland, Oregon, USA
- Kaiser Permanente Northwest Center for Health Research, Portland, Oregon, USA
| | - Jean P O'Malley
- Department of Public Health and Preventive Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | | | - Miguel Marino
- Department of Family Medicine, Oregon Health & Science University, Portland, Oregon, USA
- Department of Public Health and Preventive Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | - Alex Krist
- Department of Family Medicine and Community Health, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Jennifer E DeVoe
- Department of Family Medicine, Oregon Health & Science University, Portland, Oregon, USA
- OCHIN, Inc, Portland, Oregon, USA
| |
Collapse
|
33
|
Abstract
Electronic medical records (EMRs) are being widely implemented today, either as stand-alone applications in smaller practices or as systems-based integrated network solutions in larger health care organizations. Advantages include rapid accessibility, worldwide availability, ease of storage, and secure transfer of protected health information (PHI). Computerized physician order entry (CPOE) and decision-support capabilities such as the triggering of an alarm when multiple medications with known interactions are ordered, as well as the seemingly endless possibilities for electronic integration and extraction of PHI for clinical and research purposes, have created opportunities and pitfalls alike. Risks include breaches of confidentiality with a need to implement tighter measures for electronic security. These measures contrast efforts required for the realization of common data formats that have national and even international compatibility. EMRs provide a common platform that could potentially allow for the integration and administration of clinical care, research, and quality metrics, thus promoting optimal outcomes for patients. Technical and medicolegal difficulties need to be overcome in the years to come so that the safe use of PHI can be ensured while still maintaining the benefits and convenience of modern EMR systems.
Collapse
Affiliation(s)
- Matthias Turina
- Department of Colorectal Surgery, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Ravi P Kiran
- Department of Colorectal Surgery, Cleveland Clinic Foundation, Cleveland, Ohio
| |
Collapse
|
34
|
Text mining of cancer-related information: review of current status and future directions. Int J Med Inform 2014; 83:605-23. [PMID: 25008281 DOI: 10.1016/j.ijmedinf.2014.06.009] [Citation(s) in RCA: 112] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2013] [Revised: 06/12/2014] [Accepted: 06/14/2014] [Indexed: 12/21/2022]
Abstract
PURPOSE This paper reviews the research literature on text mining (TM) with the aim to find out (1) which cancer domains have been the subject of TM efforts, (2) which knowledge resources can support TM of cancer-related information and (3) to what extent systems that rely on knowledge and computational methods can convert text data into useful clinical information. These questions were used to determine the current state of the art in this particular strand of TM and suggest future directions in TM development to support cancer research. METHODS A review of the research on TM of cancer-related information was carried out. A literature search was conducted on the Medline database as well as IEEE Xplore and ACM digital libraries to address the interdisciplinary nature of such research. The search results were supplemented with the literature identified through Google Scholar. RESULTS A range of studies have proven the feasibility of TM for extracting structured information from clinical narratives such as those found in pathology or radiology reports. In this article, we provide a critical overview of the current state of the art for TM related to cancer. The review highlighted a strong bias towards symbolic methods, e.g. named entity recognition (NER) based on dictionary lookup and information extraction (IE) relying on pattern matching. The F-measure of NER ranges between 80% and 90%, while that of IE for simple tasks is in the high 90s. To further improve the performance, TM approaches need to deal effectively with idiosyncrasies of the clinical sublanguage such as non-standard abbreviations as well as a high degree of spelling and grammatical errors. This requires a shift from rule-based methods to machine learning following the success of similar trends in biological applications of TM. Machine learning approaches require large training datasets, but clinical narratives are not readily available for TM research due to privacy and confidentiality concerns. This issue remains the main bottleneck for progress in this area. In addition, there is a need for a comprehensive cancer ontology that would enable semantic representation of textual information found in narrative reports.
Collapse
|
35
|
Rosenbloom ST, Harris P, Pulley J, Basford M, Grant J, DuBuisson A, Rothman RL. The Mid-South clinical Data Research Network. J Am Med Inform Assoc 2014; 21:627-32. [PMID: 24821742 PMCID: PMC4078290 DOI: 10.1136/amiajnl-2014-002745] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
The Mid-South Clinical Data Research Network (CDRN) encompasses three large health systems: (1) Vanderbilt Health System (VU) with electronic medical records for over 2 million patients, (2) the Vanderbilt Healthcare Affiliated Network (VHAN) which currently includes over 40 hospitals, hundreds of ambulatory practices, and over 3 million patients in the Mid-South, and (3) Greenway Medical Technologies, with access to 24 million patients nationally. Initial goals of the Mid-South CDRN include: (1) expansion of our VU data network to include the VHAN and Greenway systems, (2) developing data integration/interoperability across the three systems, (3) improving our current tools for extracting clinical data, (4) optimization of tools for collection of patient-reported data, and (5) expansion of clinical decision support. By 18 months, we anticipate our CDRN will robustly support projects in comparative effectiveness research, pragmatic clinical trials, and other key research areas and have the capacity to share data and health information technology tools nationally.
Collapse
Affiliation(s)
- S Trent Rosenbloom
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Paul Harris
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jill Pulley
- Office of Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA Office of Personalized Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Melissa Basford
- Office of Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jason Grant
- Vanderbilt Health Affiliated Network, Nashville, Tennessee, USA
| | | | - Russell L Rothman
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA Center for Health Services Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| |
Collapse
|
36
|
Wei WQ, Feng Q, Weeke P, Bush W, Waitara MS, Iwuchukwu OF, Roden DM, Wilke RA, Stein CM, Denny JC. Creation and Validation of an EMR-based Algorithm for Identifying Major Adverse Cardiac Events while on Statins. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2014; 2014:112-9. [PMID: 25717410 PMCID: PMC4333709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Statin medications are often prescribed to ameliorate a patient's risk of cardiovascular events due in part to cholesterol reduction. We developed and evaluated an algorithm that can accurately identify subjects with major adverse cardiac events (MACE) while on statins using electronic medical record (EMR) data. The algorithm also identifies subjects experiencing their first MACE while on statins for primary prevention. The algorithm achieved 90% to 97% PPVs in identification of MACE cases as compared against physician review. By applying the algorithm to EMR data in BioVU, cases and controls were identified and used subsequently to replicate known associations with eight genetic variants. We replicated 6/8 previously reported genetic associations with cardiovascular diseases or lipid metabolism disorders. Our results demonstrated that the algorithm can be used to accurately identify subjects with MACE and MACE while on statins. Consequently, future e studies can be conducted to investigate and validate the relationship between statins and MACE using real-world clinical data.
Collapse
Affiliation(s)
- Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN
| | - Qiping Feng
- Division of Clinical Pharmacology, Vanderbilt University School of Medicine, Nashville, TN
| | - Peter Weeke
- Division of Clinical Pharmacology, Vanderbilt University School of Medicine, Nashville, TN
| | - William Bush
- Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, TN
| | - Magarya S. Waitara
- Division of Clinical Pharmacology, Vanderbilt University School of Medicine, Nashville, TN
| | - Otito F. Iwuchukwu
- Division of Clinical Pharmacology, Vanderbilt University School of Medicine, Nashville, TN
| | - Dan M. Roden
- Division of Clinical Pharmacology, Vanderbilt University School of Medicine, Nashville, TN,Oates Institute for Experimental Therapeutics, Vanderbilt University, Nashville, TN,Office of Personalized Medicine, Vanderbilt University, Nashville, TN
| | | | - Charles M Stein
- Division of Clinical Pharmacology, Vanderbilt University School of Medicine, Nashville, TN
| | - Joshua C. Denny
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN
| |
Collapse
|
37
|
McPeek Hinz ER, Bastarache L, Denny JC. A natural language processing algorithm to define a venous thromboembolism phenotype. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2013; 2013:975-983. [PMID: 24551388 PMCID: PMC3900229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Deep venous thrombosis and pulmonary embolism are diseases associated with significant morbidity and mortality. Known risk factors are attributed for only slight majority of venous thromboembolic disease (VTE) with the remainder of risk presumably related to unidentified genetic factors. We designed a general purpose Natural Language (NLP) algorithm to retrospectively capture both acute and historical cases of thromboembolic disease in a de-identified electronic health record. Applying the NLP algorithm to a separate evaluation set found a positive predictive value of 84.7% and sensitivity of 95.3% for an F-measure of 0.897, which was similar to the training set of 0.925. Use of the same algorithm on problem lists only in patients without VTE ICD-9s was found to be the best means of capturing historical cases with a PPV of 83%. NLP of VTE ICD-9 positive cases and non-ICD-9 positive problem lists provides an effective means for capture of both acute and historical cases of venous thromboembolic disease.
Collapse
Affiliation(s)
| | | | - Joshua C Denny
- Departments of Biomedical Informatics, Nashville, TN ; Medicine Vanderbilt University School of Medicine, Nashville, TN
| |
Collapse
|
38
|
Nikfarjam A, Emadzadeh E, Gonzalez G. Towards generating a patient's timeline: extracting temporal relationships from clinical notes. J Biomed Inform 2013; 46 Suppl:S40-S47. [PMID: 24212118 DOI: 10.1016/j.jbi.2013.11.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2013] [Revised: 10/31/2013] [Accepted: 11/01/2013] [Indexed: 10/26/2022]
Abstract
Clinical records include both coded and free-text fields that interact to reflect complicated patient stories. The information often covers not only the present medical condition and events experienced by the patient, but also refers to relevant events in the past (such as signs, symptoms, tests or treatments). In order to automatically construct a timeline of these events, we first need to extract the temporal relations between pairs of events or time expressions presented in the clinical notes. We designed separate extraction components for different types of temporal relations, utilizing a novel hybrid system that combines machine learning with a graph-based inference mechanism to extract the temporal links. The temporal graph is a directed graph based on parse tree dependencies of the simplified sentences and frequent pattern clues. We generalized the sentences in order to discover patterns that, given the complexities of natural language, might not be directly discoverable in the original sentences. The proposed hybrid system performance reached an F-measure of 0.63, with precision at 0.76 and recall at 0.54 on the 2012 i2b2 Natural Language Processing corpus for the temporal relation (TLink) extraction task, achieving the highest precision and third highest f-measure among participating teams in the TLink track.
Collapse
Affiliation(s)
- Azadeh Nikfarjam
- Department of Biomedical Informatics, Arizona State University, Tempe, USA.
| | - Ehsan Emadzadeh
- Department of Biomedical Informatics, Arizona State University, Tempe, USA
| | - Graciela Gonzalez
- Department of Biomedical Informatics, Arizona State University, Tempe, USA
| |
Collapse
|
39
|
Sun W, Rumshisky A, Uzuner O. Temporal reasoning over clinical text: the state of the art. J Am Med Inform Assoc 2013; 20:814-9. [PMID: 23676245 PMCID: PMC3756277 DOI: 10.1136/amiajnl-2013-001760] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2013] [Revised: 04/17/2013] [Accepted: 04/20/2013] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVES To provide an overview of the problem of temporal reasoning over clinical text and to summarize the state of the art in clinical natural language processing for this task. TARGET AUDIENCE This overview targets medical informatics researchers who are unfamiliar with the problems and applications of temporal reasoning over clinical text. SCOPE We review the major applications of text-based temporal reasoning, describe the challenges for software systems handling temporal information in clinical text, and give an overview of the state of the art. Finally, we present some perspectives on future research directions that emerged during the recent community-wide challenge on text-based temporal reasoning in the clinical domain.
Collapse
Affiliation(s)
- Weiyi Sun
- Department of Informatics, University at Albany, SUNY, Albany, New York, USA
| | - Anna Rumshisky
- Department of Computer Science, University of Massachusetts, Lowell, Massachusetts, USA
| | - Ozlem Uzuner
- Department of Information Studies, University at Albany, SUNY, Albany, New York, USA
| |
Collapse
|
40
|
Wei WQ, Cronin RM, Xu H, Lasko TA, Bastarache L, Denny JC. Development and evaluation of an ensemble resource linking medications to their indications. J Am Med Inform Assoc 2013; 20:954-61. [PMID: 23576672 PMCID: PMC3756263 DOI: 10.1136/amiajnl-2012-001431] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2012] [Revised: 02/25/2013] [Accepted: 03/18/2013] [Indexed: 11/09/2022] Open
Abstract
OBJECTIVE To create a computable MEDication Indication resource (MEDI) to support primary and secondary use of electronic medical records (EMRs). MATERIALS AND METHODS We processed four public medication resources, RxNorm, Side Effect Resource (SIDER) 2, MedlinePlus, and Wikipedia, to create MEDI. We applied natural language processing and ontology relationships to extract indications for prescribable, single-ingredient medication concepts and all ingredient concepts as defined by RxNorm. Indications were coded as Unified Medical Language System (UMLS) concepts and International Classification of Diseases, 9th edition (ICD9) codes. A total of 689 extracted indications were randomly selected for manual review for accuracy using dual-physician review. We identified a subset of medication-indication pairs that optimizes recall while maintaining high precision. RESULTS MEDI contains 3112 medications and 63 343 medication-indication pairs. Wikipedia was the largest resource, with 2608 medications and 34 911 pairs. For each resource, estimated precision and recall, respectively, were 94% and 20% for RxNorm, 75% and 33% for MedlinePlus, 67% and 31% for SIDER 2, and 56% and 51% for Wikipedia. The MEDI high-precision subset (MEDI-HPS) includes indications found within either RxNorm or at least two of the three other resources. MEDI-HPS contains 13 304 unique indication pairs regarding 2136 medications. The mean±SD number of indications for each medication in MEDI-HPS is 6.22 ± 6.09. The estimated precision of MEDI-HPS is 92%. CONCLUSIONS MEDI is a publicly available, computable resource that links medications with their indications as represented by concepts and billing codes. MEDI may benefit clinical EMR applications and reuse of EMR data for research.
Collapse
Affiliation(s)
- Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA
| | | | | | | | | | | |
Collapse
|
41
|
Sun W, Rumshisky A, Uzuner O. Annotating temporal information in clinical narratives. J Biomed Inform 2013; 46 Suppl:S5-S12. [PMID: 23872518 DOI: 10.1016/j.jbi.2013.07.004] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2013] [Revised: 07/10/2013] [Accepted: 07/10/2013] [Indexed: 11/26/2022]
Abstract
Temporal information in clinical narratives plays an important role in patients' diagnosis, treatment and prognosis. In order to represent narrative information accurately, medical natural language processing (MLP) systems need to correctly identify and interpret temporal information. To promote research in this area, the Informatics for Integrating Biology and the Bedside (i2b2) project developed a temporally annotated corpus of clinical narratives. This corpus contains 310 de-identified discharge summaries, with annotations of clinical events, temporal expressions and temporal relations. This paper describes the process followed for the development of this corpus and discusses annotation guideline development, annotation methodology, and corpus quality.
Collapse
Affiliation(s)
- Weiyi Sun
- Department of Informatics, University at Albany, SUNY, 1400 Washington Ave., Draper 114B, Albany, NY 12222, United States.
| | - Anna Rumshisky
- Department of Computer Science, University of Massachusetts, 198 Riverside St., Olsen Hall, Lowell, MA 01854, United States
| | - Ozlem Uzuner
- Department of Information Studies, University at Albany, SUNY, 1400 Washington Ave., Draper 114A, Albany, NY 12222, United States
| |
Collapse
|
42
|
Natural language processing accurately categorizes findings from colonoscopy and pathology reports. Clin Gastroenterol Hepatol 2013; 11:689-94. [PMID: 23313839 PMCID: PMC4026927 DOI: 10.1016/j.cgh.2012.11.035] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2012] [Revised: 11/07/2012] [Accepted: 11/27/2012] [Indexed: 02/07/2023]
Abstract
BACKGROUND & AIMS Little is known about the ability of natural language processing (NLP) to extract meaningful information from free-text gastroenterology reports for secondary use. METHODS We randomly selected 500 linked colonoscopy and pathology reports from 10,798 nonsurveillance colonoscopies to train and test the NLP system. By using annotation by gastroenterologists as the reference standard, we assessed the accuracy of an open-source NLP engine that processed and extracted clinically relevant concepts. The primary outcome was the highest level of pathology. Secondary outcomes were location of the most advanced lesion, largest size of an adenoma removed, and number of adenomas removed. RESULTS The NLP system identified the highest level of pathology with 98% accuracy, compared with triplicate annotation by gastroenterologists (the standard). Accuracy values for location, size, and number were 97%, 96%, and 84%, respectively. CONCLUSIONS The NLP can extract specific meaningful concepts with 98% accuracy. It might be developed as a method to further quantify specific quality metrics.
Collapse
|
43
|
Tang B, Wu Y, Jiang M, Chen Y, Denny JC, Xu H. A hybrid system for temporal information extraction from clinical text. J Am Med Inform Assoc 2013; 20:828-35. [PMID: 23571849 DOI: 10.1136/amiajnl-2013-001635] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVE To develop a comprehensive temporal information extraction system that can identify events, temporal expressions, and their temporal relations in clinical text. This project was part of the 2012 i2b2 clinical natural language processing (NLP) challenge on temporal information extraction. MATERIALS AND METHODS The 2012 i2b2 NLP challenge organizers manually annotated 310 clinic notes according to a defined annotation guideline: a training set of 190 notes and a test set of 120 notes. All participating systems were developed on the training set and evaluated on the test set. Our system consists of three modules: event extraction, temporal expression extraction, and temporal relation (also called Temporal Link, or 'TLink') extraction. The TLink extraction module contains three individual classifiers for TLinks: (1) between events and section times, (2) within a sentence, and (3) across different sentences. The performance of our system was evaluated using scripts provided by the i2b2 organizers. Primary measures were micro-averaged Precision, Recall, and F-measure. RESULTS Our system was among the top ranked. It achieved F-measures of 0.8659 for temporal expression extraction (ranked fourth), 0.6278 for end-to-end TLink track (ranked first), and 0.6932 for TLink-only track (ranked first) in the challenge. We subsequently investigated different strategies for TLink extraction, and were able to marginally improve performance with an F-measure of 0.6943 for TLink-only track.
Collapse
Affiliation(s)
- Buzhou Tang
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | | | | | | | | | | |
Collapse
|
44
|
Xu Y, Wang Y, Liu T, Tsujii J, Chang EIC. An end-to-end system to identify temporal relation in discharge summaries: 2012 i2b2 challenge. J Am Med Inform Assoc 2013; 20:849-58. [PMID: 23467472 DOI: 10.1136/amiajnl-2012-001607] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVE To create an end-to-end system to identify temporal relation in discharge summaries for the 2012 i2b2 challenge. The challenge includes event extraction, timex extraction, and temporal relation identification. DESIGN An end-to-end temporal relation system was developed. It includes three subsystems: an event extraction system (conditional random fields (CRF) name entity extraction and their corresponding attribute classifiers), a temporal extraction system (CRF name entity extraction, their corresponding attribute classifiers, and context-free grammar based normalization system), and a temporal relation system (10 multi-support vector machine (SVM) classifiers and a Markov logic networks inference system) using labeled sequential pattern mining, syntactic structures based on parse trees, and results from a coordination classifier. Micro-averaged precision (P), recall (R), averaged P&R (P&R), and F measure (F) were used to evaluate results. RESULTS For event extraction, the system achieved 0.9415 (P), 0.8930 (R), 0.9166 (P&R), and 0.9166 (F). The accuracies of their type, polarity, and modality were 0.8574, 0.8585, and 0.8560, respectively. For timex extraction, the system achieved 0.8818, 0.9489, 0.9141, and 0.9141, respectively. The accuracies of their type, value, and modifier were 0.8929, 0.7170, and 0.8907, respectively. For temporal relation, the system achieved 0.6589, 0.7129, 0.6767, and 0.6849, respectively. For end-to-end temporal relation, it achieved 0.5904, 0.5944, 0.5921, and 0.5924, respectively. With the F measure used for evaluation, we were ranked first out of 14 competing teams (event extraction), first out of 14 teams (timex extraction), third out of 12 teams (temporal relation), and second out of seven teams (end-to-end temporal relation). CONCLUSIONS The system achieved encouraging results, demonstrating the feasibility of the tasks defined by the i2b2 organizers. The experiment result demonstrates that both global and local information is useful in the 2012 challenge.
Collapse
Affiliation(s)
- Yan Xu
- State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing, China
| | | | | | | | | |
Collapse
|
45
|
Abstract
Abstract: The combination of improved genomic analysis methods, decreasing genotyping costs, and increasing computing resources has led to an explosion of clinical genomic knowledge in the last decade. Similarly, healthcare systems are increasingly adopting robust electronic health record (EHR) systems that not only can improve health care, but also contain a vast repository of disease and treatment data that could be mined for genomic research. Indeed, institutions are creating EHR-linked DNA biobanks to enable genomic and pharmacogenomic research, using EHR data for phenotypic information. However, EHRs are designed primarily for clinical care, not research, so reuse of clinical EHR data for research purposes can be challenging. Difficulties in use of EHR data include: data availability, missing data, incorrect data, and vast quantities of unstructured narrative text data. Structured information includes billing codes, most laboratory reports, and other variables such as physiologic measurements and demographic information. Significant information, however, remains locked within EHR narrative text documents, including clinical notes and certain categories of test results, such as pathology and radiology reports. For relatively rare observations, combinations of simple free-text searches and billing codes may prove adequate when followed by manual chart review. However, to extract the large cohorts necessary for genome-wide association studies, natural language processing methods to process narrative text data may be needed. Combinations of structured and unstructured textual data can be mined to generate high-validity collections of cases and controls for a given condition. Once high-quality cases and controls are identified, EHR-derived cases can be used for genomic discovery and validation. Since EHR data includes a broad sampling of clinically-relevant phenotypic information, it may enable multiple genomic investigations upon a single set of genotyped individuals. This chapter reviews several examples of phenotype extraction and their application to genetic research, demonstrating a viable future for genomic discovery using EHR-linked data.
Collapse
Affiliation(s)
- Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America.
| |
Collapse
|
46
|
Xu Y, Tsujii J, Chang EIC. Named entity recognition of follow-up and time information in 20,000 radiology reports. J Am Med Inform Assoc 2012; 19:792-9. [PMID: 22771530 DOI: 10.1136/amiajnl-2012-000812] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVE To develop a system to extract follow-up information from radiology reports. The method may be used as a component in a system which automatically generates follow-up information in a timely fashion. METHODS A novel method of combining an LSP (labeled sequential pattern) classifier with a CRF (conditional random field) recognizer was devised. The LSP classifier filters out irrelevant sentences, while the CRF recognizer extracts follow-up and time phrases from candidate sentences presented by the LSP classifier. MEASUREMENTS The standard performance metrics of precision (P), recall (R), and F measure (F) in the exact and inexact matching settings were used for evaluation. RESULTS Four experiments conducted using 20,000 radiology reports showed that the CRF recognizer achieved high performance without time-consuming feature engineering and that the LSP classifier further improved the performance of the CRF recognizer. The performance of the current system is P=0.90, R=0.86, F=0.88 in the exact matching setting and P=0.98, R=0.93, F=0.95 in the inexact matching setting. CONCLUSION The experiments demonstrate that the system performs far better than a baseline rule-based system and is worth considering for deployment trials in an alert generation system. The LSP classifier successfully compensated for the inherent weakness of CRF, that is, its inability to use global information.
Collapse
Affiliation(s)
- Yan Xu
- State Key Laboratory of Software Development Environment, Beihang University, Beijing, China
| | | | | |
Collapse
|
47
|
Reeves RM, Ong FR, Matheny ME, Denny JC, Aronsky D, Gobbel GT, Montella D, Speroff T, Brown SH. Detecting temporal expressions in medical narratives. Int J Med Inform 2012; 82:118-27. [PMID: 22595284 DOI: 10.1016/j.ijmedinf.2012.04.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2012] [Revised: 03/30/2012] [Accepted: 04/12/2012] [Indexed: 12/27/2022]
Abstract
BACKGROUND Clinical practice and epidemiological information aggregation require knowing when, how long, and in what sequence medically relevant events occur. The Temporal Awareness and Reasoning Systems for Question Interpretation (TARSQI) Toolkit (TTK) is a complete, open source software package for the temporal ordering of events within narrative text documents. TTK was developed on newspaper articles. We extended TTK to support medical notes using veterans' affairs (VA) clinical notes and compared it to TTK. METHODS We used a development set consisting of 200 VA clinical notes to modify and append rules to TTK's time tagger, creating Med-TTK. We then evaluated the performances of TTK and Med-TTK on an independent random selection of 100 clinical notes. Evaluation tasks were to identify and classify time-referring expressions as one of four temporal classes (DATE, TIME, DURATION, and SET). The reference standard for this test set was generated by dual human manual review with disagreements resolved by a third reviewer. Outcome measures included recall and precision for each class, and inter-rater agreement scores. RESULTS There were 3146 temporal expressions in the reference standard. TTK identified 1595 temporal expressions. Recall was 0.15 (95% confidence interval [CI] 0.12-0.15) and precision was 0.27 (95% CI 0.25-0.29) for TTK. Med-TTK identified 3174 expressions. Recall was 0.86 (95% CI 0.84-0.87) and precision was 0.85 (95% CI 0.84-0.86) for Med-TTK. CONCLUSION The algorithms for identifying and classifying temporal expressions in medical narratives developed within Med-TTK significantly improved performance compared to TTK. Natural language processing applications such as Med-TTK provide a foundation for meaningful longitudinal mapping of patient history events among electronic health records. The tool can be accessed at the following site: http://code.google.com/p/med-ttk/.
Collapse
Affiliation(s)
- Ruth M Reeves
- Geriatric Research Education and Clinical Center, Tennessee Valley Healthcare System, Department of Veterans Affairs, Nashville, TN, USA.
| | | | | | | | | | | | | | | | | |
Collapse
|
48
|
Abstract
Clinical research informatics is the rapidly evolving sub-discipline within biomedical informatics that focuses on developing new informatics theories, tools, and solutions to accelerate the full translational continuum: basic research to clinical trials (T1), clinical trials to academic health center practice (T2), diffusion and implementation to community practice (T3), and ‘real world’ outcomes (T4). We present a conceptual model based on an informatics-enabled clinical research workflow, integration across heterogeneous data sources, and core informatics tools and platforms. We use this conceptual model to highlight 18 new articles in the JAMIA special issue on clinical research informatics.
Collapse
Affiliation(s)
- Michael G Kahn
- Department of Pediatrics, University of Colorado, Aurora, Colorado 80045, USA.
| | | |
Collapse
|
49
|
Carroll RJ, Thompson WK, Eyler AE, Mandelin AM, Cai T, Zink RM, Pacheco JA, Boomershine CS, Lasko TA, Xu H, Karlson EW, Perez RG, Gainer VS, Murphy SN, Ruderman EM, Pope RM, Plenge RM, Kho AN, Liao KP, Denny JC. Portability of an algorithm to identify rheumatoid arthritis in electronic health records. J Am Med Inform Assoc 2012; 19:e162-9. [PMID: 22374935 DOI: 10.1136/amiajnl-2011-000583] [Citation(s) in RCA: 164] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVES Electronic health records (EHR) can allow for the generation of large cohorts of individuals with given diseases for clinical and genomic research. A rate-limiting step is the development of electronic phenotype selection algorithms to find such cohorts. This study evaluated the portability of a published phenotype algorithm to identify rheumatoid arthritis (RA) patients from EHR records at three institutions with different EHR systems. MATERIALS AND METHODS Physicians reviewed charts from three institutions to identify patients with RA. Each institution compiled attributes from various sources in the EHR, including codified data and clinical narratives, which were searched using one of two natural language processing (NLP) systems. The performance of the published model was compared with locally retrained models. RESULTS Applying the previously published model from Partners Healthcare to datasets from Northwestern and Vanderbilt Universities, the area under the receiver operating characteristic curve was found to be 92% for Northwestern and 95% for Vanderbilt, compared with 97% at Partners. Retraining the model improved the average sensitivity at a specificity of 97% to 72% from the original 65%. Both the original logistic regression models and locally retrained models were superior to simple billing code count thresholds. DISCUSSION These results show that a previously published algorithm for RA is portable to two external hospitals using different EHR systems, different NLP systems, and different target NLP vocabularies. Retraining the algorithm primarily increased the sensitivity at each site. CONCLUSION Electronic phenotype algorithms allow rapid identification of case populations in multiple sites with little retraining.
Collapse
Affiliation(s)
- Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
50
|
Denny JC, Choma NN, Peterson JF, Miller RA, Bastarache L, Li M, Peterson NB. Natural language processing improves identification of colorectal cancer testing in the electronic medical record. Med Decis Making 2012; 32:188-197. [PMID: 21393557 PMCID: PMC9616628 DOI: 10.1177/0272989x11400418] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/16/2023]
Abstract
BACKGROUND Difficulty identifying patients in need of colorectal cancer (CRC) screening contributes to low screening rates. OBJECTIVE To use Electronic Health Record (EHR) data to identify patients with prior CRC testing. DESIGN A clinical natural language processing (NLP) system was modified to identify 4 CRC tests (colonoscopy, flexible sigmoidoscopy, fecal occult blood testing, and double contrast barium enema) within electronic clinical documentation. Text phrases in clinical notes referencing CRC tests were interpreted by the system to determine whether testing was planned or completed and to estimate the date of completed tests. SETTING Large academic medical center. PATIENTS 200 patients ≥ 50 years old who had completed ≥ 2 non-acute primary care visits within a 1-year period. MEASURES Recall and precision of the NLP system, billing records, and human chart review were compared to a reference standard of human review of all available information sources. RESULTS For identification of all CRC tests, recall and precision were as follows: NLP system (recall 93%, precision 94%), chart review (74%, 98%), and billing records review (44%, 83%). Recall and precision for identification of patients in need of screening were: NLP system (recall 95%, precision 88%), chart review (99%, 82%), and billing records (99%, 67%). LIMITATIONS Small sample size and requirement for a robust EHR. CONCLUSIONS Applying NLP to EHR records detected more CRC tests than either manual chart review or billing records review alone. NLP had better precision but marginally lower recall to identify patients who were due for CRC screening than billing record review.
Collapse
Affiliation(s)
- Joshua C. Denny
- Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Neesha N. Choma
- Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Veterans Administration, Tennessee Valley Healthcare System, Tennessee Valley Geriatric Research Education Clinical Center (GRECC), Nashville, Tennessee
| | - Josh F. Peterson
- Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
- Veterans Administration, Tennessee Valley Healthcare System, Tennessee Valley Geriatric Research Education Clinical Center (GRECC), Nashville, Tennessee
| | - Randolph A. Miller
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Ming Li
- Department of Biostatistics, Vanderbilt University, Nashville, Tennessee
| | - Neeraja B. Peterson
- Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| |
Collapse
|