1
|
Fu L, Weng Z, Zhang J, Xie H, Cao Y. MMBERT: a unified framework for biomedical named entity recognition. Med Biol Eng Comput 2024; 62:327-341. [PMID: 37833517 DOI: 10.1007/s11517-023-02934-8] [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: 02/09/2023] [Accepted: 09/07/2023] [Indexed: 10/15/2023]
Abstract
Named entity recognition (NER) is an important task in natural language processing (NLP). In recent years, NER has attracted much attention in the biomedical field. However, due to the lack of biomedical named entity identification datasets, the complexity and rarity of biomedical named entities and so on, biomedical NER is more difficult than general domain NER. So in this paper, we propose a framework (MMBERT) based on Transformer to solve the problems above. To address the scarcity of biomedical named entity recognition datasets, we introduce ERNIE-Health, a new Chinese language representation model pre-trained on large-scale biomedical text corpora. Because of the complexity and rarity of biomedical named entities, we use the Bert and CW-LSTM structures to get the joint feature vector of word pairs relations. In addition, we design multi-granularity 2D convolution to refine the relationship and representation between word pairs. Finally, we design a convolutional neural network (CNN) structure and a co-predictor to improve the model's generalization capability and prediction accuracy. We have conducted extensive experiments on three benchmark datasets, and the experimental results show that our model achieves the best results compared with several baseline models in the experiment.
Collapse
Affiliation(s)
- Lei Fu
- College of Electromechanical and Information Engineering, PuTian University, PuTian, 351100, Fujian Province, China
| | - Zuquan Weng
- College of Biological Science and Engineering, Fuzhou University, Fuzhou, 350000, Fujian Province, China.
- The Centre for Big Data Research in Burns and Trauma, College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350000, Fujian Province, China.
| | - Jiheng Zhang
- College of Biological Science and Engineering, Fuzhou University, Fuzhou, 350000, Fujian Province, China
- The Centre for Big Data Research in Burns and Trauma, College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350000, Fujian Province, China
| | - Haihe Xie
- College of Electromechanical and Information Engineering, PuTian University, PuTian, 351100, Fujian Province, China
| | - Yiqing Cao
- College of Electromechanical and Information Engineering, PuTian University, PuTian, 351100, Fujian Province, China
| |
Collapse
|
2
|
NEAR: Named Entity and Attribute Recognition of clinical concepts. J Biomed Inform 2022; 130:104092. [DOI: 10.1016/j.jbi.2022.104092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 02/21/2022] [Accepted: 05/01/2022] [Indexed: 11/23/2022]
|
3
|
Yang T, He Y, Yang N. Named Entity Recognition of Medical Text Based on the Deep Neural Network. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:3990563. [PMID: 35295179 PMCID: PMC8920682 DOI: 10.1155/2022/3990563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 01/26/2022] [Indexed: 11/17/2022]
Abstract
Medical text data records detailed clinical data; named entity recognition is the basis of text information processing and an important part of mining valuable information in medical texts. The named entity recognition technology can accurately identify the information needed in medical texts and help medical staff make clinical decision-making, evidence-based medicine, and epidemic disease monitoring. This paper proposes a hybrid neural network medical text named entity recognition model. First, a coding method based on a fully self-attentive mechanism is proposed. The vector representation of each word is related to the entire sentence through the attention mechanism. It determines the weight distribution by scoring the characters or words in all positions and obtains the position information in the sentence that needs the most attention. The encoding vector at each position is integrated with the context information of full sentence, which solves the ambiguity problem. Second, a multivariate convolutional decoding method is proposed. This method can effectively pay attention to the characteristics of medical text named entity recognition in the decoding process. It uses two-dimensional convolutional decoding to associate the current position word with surrounding words to improve decoding efficiency while extracting features from the logic of the preceding and following words. Using the same number of convolution kernels as the entity category, it can effectively extract effective features from the label dimension. Besides, according to the characteristics of the named entity recognition task, a special mixed loss is designed. The experimental results verify that the proposed method is effective, and it is improved compared with some existing medical text named entity recognition methods.
Collapse
Affiliation(s)
- Tianjiao Yang
- College of Electronic Information, Qingdao University, Qingdao, Shandong 266071, China
| | - Ying He
- College of Electronic Information, Qingdao University, Qingdao, Shandong 266071, China
| | - Ning Yang
- Qingdao Lanzhi Modern Service Industry Digital Engineering Technology Research Center, Qingdao, Shandong 266071, China
| |
Collapse
|
4
|
Cross Disciplinary Consultancy to Bridge Public Health Technical Needs and Analytic Developers: Negation Detection Use Case. Online J Public Health Inform 2018; 10:e209. [PMID: 30349627 PMCID: PMC6194092 DOI: 10.5210/ojphi.v10i2.8944] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
This paper describes a continuing initiative of the International Society for Disease Surveillance designed to bring together public health practitioners and analytics solution developers from both academia and industry. Funded by the Defense Threat Reduction Agency, a series of consultancies have been conducted on a range of topics of pressing concern to public health (e.g. developing methods to enhance prediction of asthma exacerbation, developing tools for asyndromic surveillance from chief complaints). The topic of this final consultancy, conducted at the University of Utah in January 2017, is focused on defining a roadmap for the development of algorithms, tools, and datasets for improving the capabilities of text processing algorithms to identify negated terms (i.e. negation detection) in free-text chief complaints
and triage reports.
Collapse
|
5
|
NegBio: a high-performance tool for negation and uncertainty detection in radiology reports. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2018; 2017:188-196. [PMID: 29888070 PMCID: PMC5961822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Negative and uncertain medical findings are frequent in radiology reports, but discriminating them from positive findings remains challenging for information extraction. Here, we propose a new algorithm, NegBio, to detect negative and uncertain findings in radiology reports. Unlike previous rule-based methods, NegBio utilizes patterns on universal dependencies to identify the scope of triggers that are indicative of negation or uncertainty. We evaluated NegBio on four datasets, including two public benchmarking corpora of radiology reports, a new radiology corpus that we annotated for this work, and a public corpus of general clinical texts. Evaluation on these datasets demonstrates that NegBio is highly accurate for detecting negative and uncertain findings and compares favorably to a widely-used state-of-the-art system NegEx (an average of 9.5% improvement in precision and 5.1% in F1-score). AVAILABILITY https://github.com/ncbi-nlp/NegBio.
Collapse
|
6
|
Manimaran J, Velmurugan T. Evaluation of lexicon- and syntax-based negation detection algorithms using clinical text data. BIO-ALGORITHMS AND MED-SYSTEMS 2017. [DOI: 10.1515/bams-2017-0016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
AbstractBackground:Clinical Text Analysis and Knowledge Extraction System (cTAKES) is an open-source natural language processing (NLP) system. In recent development modules of cTAKES, a negation detection (ND) algorithm is used to improve annotation capabilities and simplify automatic identification of negative context in large clinical documents. In this research, the two types of ND algorithms used are lexicon and syntax, which are analyzed using a database made openly available by the National Center for Biomedical Computing. The aim of this analysis is to find the pros and cons of these algorithms.Methods:Patient medical reports were collected from three institutions included the 2010 i2b2/VA Clinical NLP Challenge, which is the input data for this analysis. This database includes patient discharge summaries and progress notes. The patient data is fed into five ND algorithms: NegEx, ConText, pyConTextNLP, DEEPEN and Negation Resolution (NR). NegEx, ConText and pyConTextNLP are lexicon-based, whereas DEEPEN and NR are syntax-based. The results from these five ND algorithms are post-processed and compared with the annotated data. Finally, the performance of these ND algorithms is evaluated by computing standard measures including F-measure, kappa statistics and ROC, among others, as well as the execution time of each algorithm.Results:This research is tested through practical implementation based on the accuracy of each algorithm’s results and computational time to evaluate its performance in order to find a robust and reliable ND algorithm.Conclusions:The performance of the chosen ND algorithms is analyzed based on the results produced by this research approach. The time and accuracy of each algorithm are calculated and compared to suggest the best method.
Collapse
|
7
|
Cohen KB, Glass B, Greiner HM, Holland-Bouley K, Standridge S, Arya R, Faist R, Morita D, Mangano F, Connolly B, Glauser T, Pestian J. Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates Through Natural Language Processing and Machine Learning. BIOMEDICAL INFORMATICS INSIGHTS 2016; 8:11-8. [PMID: 27257386 PMCID: PMC4876984 DOI: 10.4137/bii.s38308] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2015] [Revised: 03/03/2016] [Accepted: 03/03/2016] [Indexed: 01/26/2023]
Abstract
OBJECTIVE We describe the development and evaluation of a system that uses machine learning and natural language processing techniques to identify potential candidates for surgical intervention for drug-resistant pediatric epilepsy. The data are comprised of free-text clinical notes extracted from the electronic health record (EHR). Both known clinical outcomes from the EHR and manual chart annotations provide gold standards for the patient's status. The following hypotheses are then tested: 1) machine learning methods can identify epilepsy surgery candidates as well as physicians do and 2) machine learning methods can identify candidates earlier than physicians do. These hypotheses are tested by systematically evaluating the effects of the data source, amount of training data, class balance, classification algorithm, and feature set on classifier performance. The results support both hypotheses, with F-measures ranging from 0.71 to 0.82. The feature set, classification algorithm, amount of training data, class balance, and gold standard all significantly affected classification performance. It was further observed that classification performance was better than the highest agreement between two annotators, even at one year before documented surgery referral. The results demonstrate that such machine learning methods can contribute to predicting pediatric epilepsy surgery candidates and reducing lag time to surgery referral.
Collapse
Affiliation(s)
- Kevin Bretonnel Cohen
- Computational Bioscience Program, University of Colorado School of Medicine, Denver, CO, USA
| | - Benjamin Glass
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Hansel M. Greiner
- Division of Neurology, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Katherine Holland-Bouley
- Division of Neurology, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Shannon Standridge
- Division of Neurology, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Ravindra Arya
- Division of Neurology, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Robert Faist
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Diego Morita
- Division of Neurology, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Francesco Mangano
- Division of Pediatric Neurosurgery, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Brian Connolly
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Tracy Glauser
- Division of Neurology, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - John Pestian
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| |
Collapse
|
8
|
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
|
9
|
Zheng C, Rashid N, Wu YL, Koblick R, Lin AT, Levy GD, Cheetham TC. Using natural language processing and machine learning to identify gout flares from electronic clinical notes. Arthritis Care Res (Hoboken) 2014; 66:1740-8. [PMID: 24664671 DOI: 10.1002/acr.22324] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2013] [Accepted: 03/18/2014] [Indexed: 12/19/2022]
Abstract
OBJECTIVE Gout flares are not well documented by diagnosis codes, making it difficult to conduct accurate database studies. We implemented a computer-based method to automatically identify gout flares using natural language processing (NLP) and machine learning (ML) from electronic clinical notes. METHODS Of 16,519 patients, 1,264 and 1,192 clinical notes from 2 separate sets of 100 patients were selected as the training and evaluation data sets, respectively, which were reviewed by rheumatologists. We created separate NLP searches to capture different aspects of gout flares. For each note, the NLP search outputs became the ML system inputs, which provided the final classification decisions. The note-level classifications were grouped into patient-level gout flares. Our NLP+ML results were validated using a gold standard data set and compared with the claims-based method used by prior literatures. RESULTS For 16,519 patients with a diagnosis of gout and a prescription for a urate-lowering therapy, we identified 18,869 clinical notes as gout flare positive (sensitivity 82.1%, specificity 91.5%): 1,402 patients with ≥3 flares (sensitivity 93.5%, specificity 84.6%), 5,954 with 1 or 2 flares, and 9,163 with no flare (sensitivity 98.5%, specificity 96.4%). Our method identified more flare cases (18,869 versus 7,861) and patients with ≥3 flares (1,402 versus 516) when compared to the claims-based method. CONCLUSION We developed a computer-based method (NLP and ML) to identify gout flares from the clinical notes. Our method was validated as an accurate tool for identifying gout flares with higher sensitivity and specificity compared to previous studies.
Collapse
|
10
|
Wu S, Miller T, Masanz J, Coarr M, Halgrim S, Carrell D, Clark C. Negation's not solved: generalizability versus optimizability in clinical natural language processing. PLoS One 2014; 9:e112774. [PMID: 25393544 PMCID: PMC4231086 DOI: 10.1371/journal.pone.0112774] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2014] [Accepted: 10/18/2014] [Indexed: 11/30/2022] Open
Abstract
A review of published work in clinical natural language processing (NLP) may suggest that the negation detection task has been “solved.” This work proposes that an optimizable solution does not equal a generalizable solution. We introduce a new machine learning-based Polarity Module for detecting negation in clinical text, and extensively compare its performance across domains. Using four manually annotated corpora of clinical text, we show that negation detection performance suffers when there is no in-domain development (for manual methods) or training data (for machine learning-based methods). Various factors (e.g., annotation guidelines, named entity characteristics, the amount of data, and lexical and syntactic context) play a role in making generalizability difficult, but none completely explains the phenomenon. Furthermore, generalizability remains challenging because it is unclear whether to use a single source for accurate data, combine all sources into a single model, or apply domain adaptation methods. The most reliable means to improve negation detection is to manually annotate in-domain training data (or, perhaps, manually modify rules); this is a strategy for optimizing performance, rather than generalizing it. These results suggest a direction for future work in domain-adaptive and task-adaptive methods for clinical NLP.
Collapse
Affiliation(s)
- Stephen Wu
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America; Oregon Health and Science University, Portland, Oregon, United States of America
| | - Timothy Miller
- Children's Hospital Boston Informatics Program, Harvard Medical School, Boston, Massachusetts, United States of America
| | - James Masanz
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Matt Coarr
- Human Language Technology Department, The MITRE Corporation, Bedford, Massachusetts, United States of America
| | - Scott Halgrim
- Group Health Research Institute, Seattle, Washington, United States of America
| | - David Carrell
- Group Health Research Institute, Seattle, Washington, United States of America
| | - Cheryl Clark
- Human Language Technology Department, The MITRE Corporation, Bedford, Massachusetts, United States of America
| |
Collapse
|
11
|
Abstract
OBJECTIVES Implementation of Electronic Health Record (EHR) systems continues to expand. The massive number of patient encounters results in high amounts of stored data. Transforming clinical data into knowledge to improve patient care has been the goal of biomedical informatics professionals for many decades, and this work is now increasingly recognized outside our field. In reviewing the literature for the past three years, we focus on "big data" in the context of EHR systems and we report on some examples of how secondary use of data has been put into practice. METHODS We searched PubMed database for articles from January 1, 2011 to November 1, 2013. We initiated the search with keywords related to "big data" and EHR. We identified relevant articles and additional keywords from the retrieved articles were added. Based on the new keywords, more articles were retrieved and we manually narrowed down the set utilizing predefined inclusion and exclusion criteria. RESULTS Our final review includes articles categorized into the themes of data mining (pharmacovigilance, phenotyping, natural language processing), data application and integration (clinical decision support, personal monitoring, social media), and privacy and security. CONCLUSION The increasing adoption of EHR systems worldwide makes it possible to capture large amounts of clinical data. There is an increasing number of articles addressing the theme of "big data", and the concepts associated with these articles vary. The next step is to transform healthcare big data into actionable knowledge.
Collapse
Affiliation(s)
- M K Ross
- Lucila Ohno-Machado, Division of Biomedical Informatics, 9500 Gilman Drive, MC 0505, La Jolla, California, 92037-0505, USA, Tel: +1 858 822 4931, E-mail:
| | | | | |
Collapse
|
12
|
Velupillai S, Skeppstedt M, Kvist M, Mowery D, Chapman BE, Dalianis H, Chapman WW. Cue-based assertion classification for Swedish clinical text--developing a lexicon for pyConTextSwe. Artif Intell Med 2014; 61:137-44. [PMID: 24556644 PMCID: PMC4104142 DOI: 10.1016/j.artmed.2014.01.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2013] [Revised: 12/19/2013] [Accepted: 01/10/2014] [Indexed: 11/17/2022]
Abstract
OBJECTIVE The ability of a cue-based system to accurately assert whether a disorder is affirmed, negated, or uncertain is dependent, in part, on its cue lexicon. In this paper, we continue our study of porting an assertion system (pyConTextNLP) from English to Swedish (pyConTextSwe) by creating an optimized assertion lexicon for clinical Swedish. METHODS AND MATERIAL We integrated cues from four external lexicons, along with generated inflections and combinations. We used subsets of a clinical corpus in Swedish. We applied four assertion classes (definite existence, probable existence, probable negated existence and definite negated existence) and two binary classes (existence yes/no and uncertainty yes/no) to pyConTextSwe. We compared pyConTextSwe's performance with and without the added cues on a development set, and improved the lexicon further after an error analysis. On a separate evaluation set, we calculated the system's final performance. RESULTS Following integration steps, we added 454 cues to pyConTextSwe. The optimized lexicon developed after an error analysis resulted in statistically significant improvements on the development set (83% F-score, overall). The system's final F-scores on an evaluation set were 81% (overall). For the individual assertion classes, F-score results were 88% (definite existence), 81% (probable existence), 55% (probable negated existence), and 63% (definite negated existence). For the binary classifications existence yes/no and uncertainty yes/no, final system performance was 97%/87% and 78%/86% F-score, respectively. CONCLUSIONS We have successfully ported pyConTextNLP to Swedish (pyConTextSwe). We have created an extensive and useful assertion lexicon for Swedish clinical text, which could form a valuable resource for similar studies, and which is publicly available.
Collapse
Affiliation(s)
- Sumithra Velupillai
- Department of Computer and Systems Sciences (DSV), Stockholm University, Forum 100, 164 40 Kista, Sweden.
| | - Maria Skeppstedt
- Department of Computer and Systems Sciences (DSV), Stockholm University, Forum 100, 164 40 Kista, Sweden.
| | - Maria Kvist
- Department of Computer and Systems Sciences (DSV), Stockholm University, Forum 100, 164 40 Kista, Sweden; Department of Learning, Informatics, Management and Ethics (LIME), Karolinska Institutet, Widerström Building, Tomtebodavägen 18A, Solna, Sweden.
| | - Danielle Mowery
- Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Boulevard, BAUM 423, Pittsburgh, PA 15206-3701, United States.
| | - Brian E Chapman
- Department of Radiology, University of Utah, 729 Arapeen Drive, Salt Lake City, UT 84108, United States.
| | - Hercules Dalianis
- Department of Computer and Systems Sciences (DSV), Stockholm University, Forum 100, 164 40 Kista, Sweden.
| | - Wendy W Chapman
- Department of Biomedical Informatics, University of Utah, 26 South 2000 East, Room 5775 HSEB, Salt Lake City, UT 84112-5775, United States.
| |
Collapse
|
13
|
Pathak J, Kho AN, Denny JC. Electronic health records-driven phenotyping: challenges, recent advances, and perspectives. J Am Med Inform Assoc 2014; 20:e206-11. [PMID: 24302669 DOI: 10.1136/amiajnl-2013-002428] [Citation(s) in RCA: 165] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Affiliation(s)
- Jyotishman Pathak
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | | | | |
Collapse
|
14
|
Pathak J, Bailey KR, Beebe CE, Bethard S, Carrell DS, Chen PJ, Dligach D, Endle CM, Hart LA, Haug PJ, Huff SM, Kaggal VC, Li D, Liu H, Marchant K, Masanz J, Miller T, Oniki TA, Palmer M, Peterson KJ, Rea S, Savova GK, Stancl CR, Sohn S, Solbrig HR, Suesse DB, Tao C, Taylor DP, Westberg L, Wu S, Zhuo N, Chute CG. Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium. J Am Med Inform Assoc 2013; 20:e341-8. [PMID: 24190931 PMCID: PMC3861933 DOI: 10.1136/amiajnl-2013-001939] [Citation(s) in RCA: 77] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2013] [Revised: 10/07/2013] [Accepted: 10/11/2013] [Indexed: 11/03/2022] Open
Abstract
RESEARCH OBJECTIVE To develop scalable informatics infrastructure for normalization of both structured and unstructured electronic health record (EHR) data into a unified, concept-based model for high-throughput phenotype extraction. MATERIALS AND METHODS Software tools and applications were developed to extract information from EHRs. Representative and convenience samples of both structured and unstructured data from two EHR systems-Mayo Clinic and Intermountain Healthcare-were used for development and validation. Extracted information was standardized and normalized to meaningful use (MU) conformant terminology and value set standards using Clinical Element Models (CEMs). These resources were used to demonstrate semi-automatic execution of MU clinical-quality measures modeled using the Quality Data Model (QDM) and an open-source rules engine. RESULTS Using CEMs and open-source natural language processing and terminology services engines-namely, Apache clinical Text Analysis and Knowledge Extraction System (cTAKES) and Common Terminology Services (CTS2)-we developed a data-normalization platform that ensures data security, end-to-end connectivity, and reliable data flow within and across institutions. We demonstrated the applicability of this platform by executing a QDM-based MU quality measure that determines the percentage of patients between 18 and 75 years with diabetes whose most recent low-density lipoprotein cholesterol test result during the measurement year was <100 mg/dL on a randomly selected cohort of 273 Mayo Clinic patients. The platform identified 21 and 18 patients for the denominator and numerator of the quality measure, respectively. Validation results indicate that all identified patients meet the QDM-based criteria. CONCLUSIONS End-to-end automated systems for extracting clinical information from diverse EHR systems require extensive use of standardized vocabularies and terminologies, as well as robust information models for storing, discovering, and processing that information. This study demonstrates the application of modular and open-source resources for enabling secondary use of EHR data through normalization into standards-based, comparable, and consistent format for high-throughput phenotyping to identify patient cohorts.
Collapse
Affiliation(s)
- Jyotishman Pathak
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Kent R Bailey
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Calvin E Beebe
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Steven Bethard
- Department of Linguistics, University of Colorado, Boulder, Colorado, USA
| | | | - Pei J Chen
- Boston Children's Hospital, Harvard University, Boston, Massachusetts, USA
| | - Dmitriy Dligach
- Boston Children's Hospital, Harvard University, Boston, Massachusetts, USA
| | - Cory M Endle
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Lacey A Hart
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Peter J Haug
- Homer Warner Center for Informatics Research, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Stanley M Huff
- Homer Warner Center for Informatics Research, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Vinod C Kaggal
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Dingcheng Li
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Hongfang Liu
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | | | - James Masanz
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Timothy Miller
- Boston Children's Hospital, Harvard University, Boston, Massachusetts, USA
| | - Thomas A Oniki
- Homer Warner Center for Informatics Research, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Martha Palmer
- Department of Linguistics, University of Colorado, Boulder, Colorado, USA
| | - Kevin J Peterson
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Susan Rea
- Homer Warner Center for Informatics Research, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Guergana K Savova
- Boston Children's Hospital, Harvard University, Boston, Massachusetts, USA
| | - Craig R Stancl
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Sunghwan Sohn
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Harold R Solbrig
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Dale B Suesse
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Cui Tao
- School of Biomedical Informatics, University of Texas Health Sciences Center, Houston, Texas, USA
| | - David P Taylor
- Homer Warner Center for Informatics Research, Intermountain Healthcare, Salt Lake City, Utah, USA
| | | | - Stephen Wu
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Ning Zhuo
- Homer Warner Center for Informatics Research, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Christopher G Chute
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| |
Collapse
|
15
|
Mowery DL, Jordan P, Wiebe J, Harkema H, Dowling J, Chapman WW. Semantic annotation of clinical events for generating a problem list. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2013; 2013:1032-41. [PMID: 24551392 PMCID: PMC3900128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
We present a pilot study of an annotation schema representing problems and their attributes, along with their relationship to temporal modifiers. We evaluated the ability for humans to annotate clinical reports using the schema and assessed the contribution of semantic annotations in determining the status of a problem mention as active, inactive, proposed, resolved, negated, or other. Our hypothesis is that the schema captures semantic information useful for generating an accurate problem list. Clinical named entities such as reference events, time points, time durations, aspectual phase, ordering words and their relationships including modifications and ordering relations can be annotated by humans with low to moderate recall. Once identified, most attributes can be annotated with low to moderate agreement. Some attributes - Experiencer, Existence, and Certainty - are more informative than other attributes - Intermittency and Generalized/Conditional - for predicting a problem mention's status. Support vector machine outperformed Naïve Bayes and Decision Tree for predicting a problem's status.
Collapse
|
16
|
Assertion modeling and its role in clinical phenotype identification. J Biomed Inform 2013; 46:68-74. [DOI: 10.1016/j.jbi.2012.09.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2012] [Revised: 09/01/2012] [Accepted: 09/05/2012] [Indexed: 11/20/2022]
|
17
|
Byrd RJ, Steinhubl SR, Sun J, Ebadollahi S, Stewart WF. Automatic identification of heart failure diagnostic criteria, using text analysis of clinical notes from electronic health records. Int J Med Inform 2013; 83:983-92. [PMID: 23317809 DOI: 10.1016/j.ijmedinf.2012.12.005] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2012] [Revised: 12/13/2012] [Accepted: 12/17/2012] [Indexed: 10/27/2022]
Abstract
OBJECTIVE Early detection of Heart Failure (HF) could mitigate the enormous individual and societal burden from this disease. Clinical detection is based, in part, on recognition of the multiple signs and symptoms comprising the Framingham HF diagnostic criteria that are typically documented, but not necessarily synthesized, by primary care physicians well before more specific diagnostic studies are done. We developed a natural language processing (NLP) procedure to identify Framingham HF signs and symptoms among primary care patients, using electronic health record (EHR) clinical notes, as a prelude to pattern analysis and clinical decision support for early detection of HF. DESIGN We developed a hybrid NLP pipeline that performs two levels of analysis: (1) At the criteria mention level, a rule-based NLP system is constructed to annotate all affirmative and negative mentions of Framingham criteria. (2) At the encounter level, we construct a system to label encounters according to whether any Framingham criterion is asserted, denied, or unknown. MEASUREMENTS Precision, recall, and F-score are used as performance metrics for criteria mention extraction and for encounter labeling. RESULTS Our criteria mention extractions achieve a precision of 0.925, a recall of 0.896, and an F-score of 0.910. Encounter labeling achieves an F-score of 0.932. CONCLUSION Our system accurately identifies and labels affirmations and denials of Framingham diagnostic criteria in primary care clinical notes and may help in the attempt to improve the early detection of HF. With adaptation and tooling, our development methodology can be repeated in new problem settings.
Collapse
Affiliation(s)
- Roy J Byrd
- IBM T. J. Watson Research Center, Yorktown Heights, NY, United States.
| | - Steven R Steinhubl
- Geisinger Medical Center, Center for Health Research, Danville, PA, United States
| | - Jimeng Sun
- IBM T. J. Watson Research Center, Yorktown Heights, NY, United States
| | | | - Walter F Stewart
- Sutter Health, Research, Development, & Dissemination, Concord, CA, United States
| |
Collapse
|
18
|
Li Q, Zhai H, Deleger L, Lingren T, Kaiser M, Stoutenborough L, Solti I. A sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction. J Am Med Inform Assoc 2012; 20:915-21. [PMID: 23268488 PMCID: PMC3756265 DOI: 10.1136/amiajnl-2012-001487] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Objective The goal of this work was to evaluate machine learning methods, binary classification and sequence labeling, for medication–attribute linkage detection in two clinical corpora. Data and methods We double annotated 3000 clinical trial announcements (CTA) and 1655 clinical notes (CN) for medication named entities and their attributes. A binary support vector machine (SVM) classification method with parsimonious feature sets, and a conditional random fields (CRF)-based multi-layered sequence labeling (MLSL) model were proposed to identify the linkages between the entities and their corresponding attributes. We evaluated the system's performance against the human-generated gold standard. Results The experiments showed that the two machine learning approaches performed statistically significantly better than the baseline rule-based approach. The binary SVM classification achieved 0.94 F-measure with individual tokens as features. The SVM model trained on a parsimonious feature set achieved 0.81 F-measure for CN and 0.87 for CTA. The CRF MLSL method achieved 0.80 F-measure on both corpora. Discussion and conclusions We compared the novel MLSL method with a binary classification and a rule-based method. The MLSL method performed statistically significantly better than the rule-based method. However, the SVM-based binary classification method was statistically significantly better than the MLSL method for both the CTA and CN corpora. Using parsimonious feature sets both the SVM-based binary classification and CRF-based MLSL methods achieved high performance in detecting medication name and attribute linkages in CTA and CN.
Collapse
Affiliation(s)
- Qi Li
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | | | | | | | | | | | | |
Collapse
|
19
|
Strauss JA, Chao CR, Kwan ML, Ahmed SA, Schottinger JE, Quinn VP. Identifying primary and recurrent cancers using a SAS-based natural language processing algorithm. J Am Med Inform Assoc 2012; 20:349-55. [PMID: 22822041 PMCID: PMC3638182 DOI: 10.1136/amiajnl-2012-000928] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Objective Significant limitations exist in the timely and complete identification of primary and recurrent cancers for clinical and epidemiologic research. A SAS-based coding, extraction, and nomenclature tool (SCENT) was developed to address this problem. Materials and methods SCENT employs hierarchical classification rules to identify and extract information from electronic pathology reports. Reports are analyzed and coded using a dictionary of clinical concepts and associated SNOMED codes. To assess the accuracy of SCENT, validation was conducted using manual review of pathology reports from a random sample of 400 breast and 400 prostate cancer patients diagnosed at Kaiser Permanente Southern California. Trained abstractors classified the malignancy status of each report. Results Classifications of SCENT were highly concordant with those of abstractors, achieving κ of 0.96 and 0.95 in the breast and prostate cancer groups, respectively. SCENT identified 51 of 54 new primary and 60 of 61 recurrent cancer cases across both groups, with only three false positives in 792 true benign cases. Measures of sensitivity, specificity, positive predictive value, and negative predictive value exceeded 94% in both cancer groups. Discussion Favorable validation results suggest that SCENT can be used to identify, extract, and code information from pathology report text. Consequently, SCENT has wide applicability in research and clinical care. Further assessment will be needed to validate performance with other clinical text sources, particularly those with greater linguistic variability. Conclusion SCENT is proof of concept for SAS-based natural language processing applications that can be easily shared between institutions and used to support clinical and epidemiologic research.
Collapse
Affiliation(s)
- Justin A Strauss
- Kaiser Permanente Southern California, Research and Evaluation, Pasadena, California, USA
| | | | | | | | | | | |
Collapse
|
20
|
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
|
21
|
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
|