HHS Public Access Author manuscript Author Manuscript
J Biomed Inform. Author manuscript; available in PMC 2017 October 01. Published in final edited form as: J Biomed Inform. 2016 October ; 63: 11–21. doi:10.1016/j.jbi.2016.07.016.
A Computational Framework for Converting Textual Clinical Diagnostic Criteria into the Quality Data Model Na Hong1,2,¥, Dingcheng Li1,¥, Yue Yu3,¥, Qiongying Xiu4, Hongfang Liu1, and Guoqian Jiang1,§ 1Department
Author Manuscript
2Institute
of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
of Medical Information, Chinese Academy of Medical Sciences, Beijing, China
3Department
of Medical Informatics, School of Public Health, Jilin University, Changchun, Jilin,
China 4Computer
Science, Winona State University, Rochester, MN, USA
Abstract Background—Constructing standard and computable clinical diagnostic criteria is an important but challenging research field in the clinical informatics community. The Quality Data Model (QDM) is emerging as a promising information model for standardizing clinical diagnostic criteria.
Author Manuscript
Objective—To develop and evaluate automated methods for converting textual clinical diagnostic criteria in a structured format using QDM. Methods—We used a clinical Natural Language Processing (NLP) tool known as cTAKES to detect sentences and annotate events in diagnostic criteria. We developed a rule-based approach for assigning the QDM datatype(s) to an individual criterion, whereas we invoked a machine learning algorithm based on the Conditional Random Fields (CRFs) for annotating attributes belonging to each particular QDM datatype. We manually developed an annotated corpus as the gold standard and used standard measures (precision, recall and f-measure) for the performance evaluation. Results—We harvested 267 individual criteria with the datatypes of Symptom and Laboratory Test from 63 textual diagnostic criteria. We manually annotated attributes and values in 142
Author Manuscript
§
Corresponding author: Correspondence to Guoqian Jiang, MD, PhD, Mayo Clinic, Department of Health Sciences Research, Mayo Clinic, 200 First Street, SW, Rochester, MN 55905, USA, Telephone: 507-266-1327, Fax: 507-284-1516, [email protected]. ¥Co-first author Competing interests The authors declare that they have no competing interests.
Contributors G.J. and N.H. conceived and designed the study; N.H., D.L., and G.J. drafted the manuscript; N.H., Y.Y., and D.L. led the data collection and individual criteria extraction and classification; D.L., Q.X., Y.Y., and N.H. led the implementation of CRFs-based algorithms and web-based interface. H.L. and G.J. provided leadership for the project; all authors contributed expertise and edits. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Hong et al.
Page 2
Author Manuscript
individual Laboratory Test criteria. The average performance of our rule-based approach was 0.84 of precision, 0.86 of recall, and 0.85 of f-measure; the performance of CRFs-based classification was 0.95 of precision, 0.88 of recall and 0.91 of f-measure. We also implemented a web-based tool that automatically translates textual Laboratory Test criteria into the QDM XML template format. The results indicated that our approaches leveraging cTAKES and CRFs are effective in facilitating diagnostic criteria annotation and classification. Conclusion—Our NLP-based computational framework is a feasible and useful solution in developing diagnostic criteria representation and computerization.
Graphical abstract
Author Manuscript
Keywords Diagnostic Criteria; Quality Data Model; Natural Language Processing; cTAKES; Conditional Random Fields
1 Introduction Author Manuscript
The term “diagnostic criteria” designates the specific combination of signs, symptoms, and test results that clinicians use to determine correct diagnosis [1]. It is one of the most valuable sources of knowledge that can be used for supporting clinical decision making and improving patient care [2]. However, existing diagnostic criteria are scattered over different media such as medical textbooks, literature, and clinical practice guidelines, and they are usually described in unstructured free text without uniform standard. This situation hinders the efficient use of diagnostic criteria for supporting contemporary clinical decision making, which needs an integrated system with interoperable and computable processes.
Author Manuscript
One solution to better support clinical decision making is to make these diagnostic criteria computerized; however, it is costly and time-consuming for experts and clinicians to complete all of the tasks manually. To this end, on the one hand, the Natural Language Processing (NLP) technology could be used to enable automatically, or semi-automatically transforming diagnostic criteria into a computable format. On the other hand, a data model to represent diagnostic criteria is equally essential for its computerized implementation. Such a data model would enable the representation of diagnostic criteria in a structured, standard, and encoded framework to support many of the clinical applications in a scalable fashion. In order to investigate the model adaptability to diagnostic criteria, we previously evaluated the application feasibility of the National Quality Forum (NQF) Quality Data Model (QDM) [3] through a data-driven approach, in which we manually analyzed the distribution and coverage of the data elements extracted from a collection of diagnostic
J Biomed Inform. Author manuscript; available in PMC 2017 October 01.
Hong et al.
Page 3
Author Manuscript
criteria in QDM. The results demonstrated that the use of QDM is feasible in building a standards-based information model for representing computable diagnostic criteria [4]. The objective of the present study is to develop and evaluate automated methods for converting textual clinical diagnostic criteria into a structured format using QDM. We leverage clinical NLP tools to facilitate the computerization and standardization of diagnostic criteria. Specifically, we use a combination of the Clinical Text Analysis and Knowledge Extraction System (cTAKES)-supported and rule-based methods for extracting individual diagnostic criterion from full-text clinical diagnostic criteria. We also develop a machine learning algorithm based on the Conditional Random Fields (CRFs) to automatically annotate and classify the attributes of diagnosis events. Finally, we develop an integrated web-based system that automatically transforms textual diagnostic criteria into a standard QDM template by implementing the algorithms.
Author Manuscript
2 Background 2.1 Clinical NLP Tools and Information Models
Author Manuscript
A number of tools and methods based on NLP technology have been reported and used in structuring free-text–based clinical text, such as clinical guidelines, clinical notes, and electronic health records (EHRs) [5, 6]. Typical clinical NLP tools that could support term recognition and text annotation from clinical text include Health Information Text Extraction tool (HITex) [7], MetaMap [8], OpenNLP [9], and cTAKES [10]. Some studies compared the performance of these frequently used NLP tools, and the cTAKES shows satisfactory performance and usability [11, 12]. cTAKES is an open-source Apache project and is an NLP system designed to extract information from EHR-based clinical free-text. cTAKES was built on the Unstructured Information Management Architecture (UIMA) framework, which is an open source framework designed by IBM and a series of comprehensive NLP methods [13]. Its modular architecture is composed of pipelined components combining rule-based and machine learning techniques [10]. These components exchange data using a standard data structure known as the Common Analysis System (CAS). CAS contains the original document with annotated results, and a powerful index system. The components of cTAKES are specifically trained for use in the clinical domain, and create rich linguistic and semantic annotations that can be utilized by clinical decision support systems, as well as in clinical and translational research [14].
Author Manuscript
Other than common tools in clinical NLP tasks, there are also many successful applications of machine learning algorithms [15–18] that are customized to support different scenarios and use cases. In recent years, the Conditional Random Fields (CRFs) algorithms demonstrated significant performance in the clinical NLP field in comparison with other machine learning algorithms [19–21]. In the CRFs applications, such as entity extraction and text classification, we usually wish to predict a vector Y = [y0; y1; : : : ; ym] of random variables given an observed feature vector X = [x0; x1; : : : ; xn], which requires us to label the words in a sentence with their corresponding features (i.e., contextual information) which subsequently used for training. The features can be part-of-speech (POS), neighboring words and word bigrams, prefixes and suffixes, capitalization, membership in domainspecific lexicons, semantic information of words, etc. Considering the advantage of CRFs in J Biomed Inform. Author manuscript; available in PMC 2017 October 01.
Hong et al.
Page 4
Author Manuscript
the contextual information understanding and decent NLP performance, we leveraged CRFs for the purpose of the attributes extraction and classification and the performance tuning in the present study.
Author Manuscript
Current efforts to develop international recommendation standard models in clinical domains have laid the foundation for modeling and representing computable diagnostic criteria. There are a number of clinical data models developed in related fields (eg, QDM, Clinical Element Models [CEMs] [22], and HL7 Fast Health Interoperable Resources [FHIR] [23]). QDM is designed to allow EHRs and other clinical electronic systems to share a common understanding and interpretation of the clinical data. It allows quality measure developers and many clinical researchers or performers to clearly and unambiguously describe the data required to calculate the quality measure. Different from CEM and FHIR, QDM contains both a data model module and a logic module. The latter handles logic expressions elegantly with a collection of functions, logic operators and temporal operators. Therefore, we chose QDM as the information model for standard representation of diagnostic criteria in the present study. 2.2 Clinical Text Computerization and Standardization The related studies on clinical text computerization mainly include the following three aspects.
Author Manuscript Author Manuscript
1.
Clinical guideline computerization and Computer Interpretable Guideline (CIG) Systems. Various computerized clinical guidelines and the decision support systems that incorporate the guidelines have been developed. Researchers have tried different approaches to computerizing clinical practice guidelines [24–27], but those guidelines cover many complex medical procedures; thus, the application of these studies in real-world clinical practice is still very limited. However, the methods used to computerize guidelines are valuable in addressing the issues in diagnostic criteria computerization.
2.
Clinical NLP technologies. Unstructured clinical text mainly exists in the form of clinical notes, eligibility criteria, and clinical guidelines. There is much precedent on the work of clinical NLP applications using machine learning, rule-based methods, and other novel methods [19, 28, 29]. These studies offer valuable contribution in exploring different methods to automatically process information in clinical text.
3.
Formalization method studies on clinical research data. Some previous studies investigated the eligibility criteria in clinical trial protocols, developed approaches for eligibility criteria extraction and semantic representation, and used hierarchical clustering for dynamic categorization of such criteria [28, 30]. For example, EliXR provided a corpus-based knowledge acquisition framework that uses the Unified Medical Language System (UMLS) to standardize eligibility-concept encoding and to enrich eligibility-concept relations for clinical research eligibility criteria from
J Biomed Inform. Author manuscript; available in PMC 2017 October 01.
Hong et al.
Page 5
Author Manuscript
text. QDM-based phenotyping methods used for identification of patient cohorts from EHR data also provide valuable reference on our work [31]. Although current studies on the computerization and standardization of diagnostic criteria are still immature, there are some studies that started working on the diagnostic criteria computerization and only focused on some particular diseases. Examples of such studies include the computerized diagnostic criterion of inclusion body myositis [32] or Brugadatype electrocardiograms [33]. However, few of the current studies are taken from the perspective of using a standard information model to build generalizable and scalable methods for the diagnostic criteria computerization.
3 Materials and Methods 3.1 Materials
Author Manuscript
3.1.1 Data Collection—In this study, our diagnostic criteria were collected from a number of sources, including medical textbooks, journal papers, documents issued by professional organizations (such as the World Health Organization [WHO] [34]), and the Internet. Table 1 shows an example of textual diagnostic criteria for diabetes mellitus [35].
Author Manuscript
In this study, we collected 237 textual diagnostic criteria using web crawler and manual retrieval. According to our previous study [36], most commonly used datatypes in diagnostic criteria include laboratory test, symptom, diagnostic study, diagnostic activity, physical exam, medication, recommended procedure and patient characteristic. In this study, for the experiments, we randomly selected 63 out of these 237 textual diagnostic criteria and focused on the computational methods for two typical datatypes: Symptom and Laboratory Test. Table 2 shows the number and source of the selected diagnostic criteria by 6 clinical topics. 3.1.2 The Multi-purpose Annotation Environment Annotator—We used the Multipurpose Annotation Environment (MAE) toolkit [37] for manually annotating attributes of individual diagnostic criteria [38]. The MAE tool allows defining flexible annotation objects in a document type definition (DTD) file in terms of the annotation task, and it also enables easy annotation by simply highlighting the word and selecting defined entity tags. The information a bout a tag is added to the appropriate tab at the bottom of the tool display window. The id, start, end, and text features are automatically generated by MAE, which makes the MAE fit well for our task. Figure 1 shows our manual annotation example for the attributes of a laboratory test in an individual criterion: Renal, Creatinine (mg/dL):>5.0 or
1), it will be classified into different categoties: Caterogy (Y1) to Caterogy (Yn). For example:
Author Manuscript
Sentence1 “Serum triglycerides 150 mg/dL (≥1.7 mmol/L) or HDL cholesterol
J Biomed Inform. Author manuscript; available in PMC 2017 October 01. Published in final edited form as: J Biomed Inform. 2016 October ; 63: 11–21. doi:10.1016/j.jbi.2016.07.016.
A Computational Framework for Converting Textual Clinical Diagnostic Criteria into the Quality Data Model Na Hong1,2,¥, Dingcheng Li1,¥, Yue Yu3,¥, Qiongying Xiu4, Hongfang Liu1, and Guoqian Jiang1,§ 1Department
Author Manuscript
2Institute
of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
of Medical Information, Chinese Academy of Medical Sciences, Beijing, China
3Department
of Medical Informatics, School of Public Health, Jilin University, Changchun, Jilin,
China 4Computer
Science, Winona State University, Rochester, MN, USA
Abstract Background—Constructing standard and computable clinical diagnostic criteria is an important but challenging research field in the clinical informatics community. The Quality Data Model (QDM) is emerging as a promising information model for standardizing clinical diagnostic criteria.
Author Manuscript
Objective—To develop and evaluate automated methods for converting textual clinical diagnostic criteria in a structured format using QDM. Methods—We used a clinical Natural Language Processing (NLP) tool known as cTAKES to detect sentences and annotate events in diagnostic criteria. We developed a rule-based approach for assigning the QDM datatype(s) to an individual criterion, whereas we invoked a machine learning algorithm based on the Conditional Random Fields (CRFs) for annotating attributes belonging to each particular QDM datatype. We manually developed an annotated corpus as the gold standard and used standard measures (precision, recall and f-measure) for the performance evaluation. Results—We harvested 267 individual criteria with the datatypes of Symptom and Laboratory Test from 63 textual diagnostic criteria. We manually annotated attributes and values in 142
Author Manuscript
§
Corresponding author: Correspondence to Guoqian Jiang, MD, PhD, Mayo Clinic, Department of Health Sciences Research, Mayo Clinic, 200 First Street, SW, Rochester, MN 55905, USA, Telephone: 507-266-1327, Fax: 507-284-1516, [email protected]. ¥Co-first author Competing interests The authors declare that they have no competing interests.
Contributors G.J. and N.H. conceived and designed the study; N.H., D.L., and G.J. drafted the manuscript; N.H., Y.Y., and D.L. led the data collection and individual criteria extraction and classification; D.L., Q.X., Y.Y., and N.H. led the implementation of CRFs-based algorithms and web-based interface. H.L. and G.J. provided leadership for the project; all authors contributed expertise and edits. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Hong et al.
Page 2
Author Manuscript
individual Laboratory Test criteria. The average performance of our rule-based approach was 0.84 of precision, 0.86 of recall, and 0.85 of f-measure; the performance of CRFs-based classification was 0.95 of precision, 0.88 of recall and 0.91 of f-measure. We also implemented a web-based tool that automatically translates textual Laboratory Test criteria into the QDM XML template format. The results indicated that our approaches leveraging cTAKES and CRFs are effective in facilitating diagnostic criteria annotation and classification. Conclusion—Our NLP-based computational framework is a feasible and useful solution in developing diagnostic criteria representation and computerization.
Graphical abstract
Author Manuscript
Keywords Diagnostic Criteria; Quality Data Model; Natural Language Processing; cTAKES; Conditional Random Fields
1 Introduction Author Manuscript
The term “diagnostic criteria” designates the specific combination of signs, symptoms, and test results that clinicians use to determine correct diagnosis [1]. It is one of the most valuable sources of knowledge that can be used for supporting clinical decision making and improving patient care [2]. However, existing diagnostic criteria are scattered over different media such as medical textbooks, literature, and clinical practice guidelines, and they are usually described in unstructured free text without uniform standard. This situation hinders the efficient use of diagnostic criteria for supporting contemporary clinical decision making, which needs an integrated system with interoperable and computable processes.
Author Manuscript
One solution to better support clinical decision making is to make these diagnostic criteria computerized; however, it is costly and time-consuming for experts and clinicians to complete all of the tasks manually. To this end, on the one hand, the Natural Language Processing (NLP) technology could be used to enable automatically, or semi-automatically transforming diagnostic criteria into a computable format. On the other hand, a data model to represent diagnostic criteria is equally essential for its computerized implementation. Such a data model would enable the representation of diagnostic criteria in a structured, standard, and encoded framework to support many of the clinical applications in a scalable fashion. In order to investigate the model adaptability to diagnostic criteria, we previously evaluated the application feasibility of the National Quality Forum (NQF) Quality Data Model (QDM) [3] through a data-driven approach, in which we manually analyzed the distribution and coverage of the data elements extracted from a collection of diagnostic
J Biomed Inform. Author manuscript; available in PMC 2017 October 01.
Hong et al.
Page 3
Author Manuscript
criteria in QDM. The results demonstrated that the use of QDM is feasible in building a standards-based information model for representing computable diagnostic criteria [4]. The objective of the present study is to develop and evaluate automated methods for converting textual clinical diagnostic criteria into a structured format using QDM. We leverage clinical NLP tools to facilitate the computerization and standardization of diagnostic criteria. Specifically, we use a combination of the Clinical Text Analysis and Knowledge Extraction System (cTAKES)-supported and rule-based methods for extracting individual diagnostic criterion from full-text clinical diagnostic criteria. We also develop a machine learning algorithm based on the Conditional Random Fields (CRFs) to automatically annotate and classify the attributes of diagnosis events. Finally, we develop an integrated web-based system that automatically transforms textual diagnostic criteria into a standard QDM template by implementing the algorithms.
Author Manuscript
2 Background 2.1 Clinical NLP Tools and Information Models
Author Manuscript
A number of tools and methods based on NLP technology have been reported and used in structuring free-text–based clinical text, such as clinical guidelines, clinical notes, and electronic health records (EHRs) [5, 6]. Typical clinical NLP tools that could support term recognition and text annotation from clinical text include Health Information Text Extraction tool (HITex) [7], MetaMap [8], OpenNLP [9], and cTAKES [10]. Some studies compared the performance of these frequently used NLP tools, and the cTAKES shows satisfactory performance and usability [11, 12]. cTAKES is an open-source Apache project and is an NLP system designed to extract information from EHR-based clinical free-text. cTAKES was built on the Unstructured Information Management Architecture (UIMA) framework, which is an open source framework designed by IBM and a series of comprehensive NLP methods [13]. Its modular architecture is composed of pipelined components combining rule-based and machine learning techniques [10]. These components exchange data using a standard data structure known as the Common Analysis System (CAS). CAS contains the original document with annotated results, and a powerful index system. The components of cTAKES are specifically trained for use in the clinical domain, and create rich linguistic and semantic annotations that can be utilized by clinical decision support systems, as well as in clinical and translational research [14].
Author Manuscript
Other than common tools in clinical NLP tasks, there are also many successful applications of machine learning algorithms [15–18] that are customized to support different scenarios and use cases. In recent years, the Conditional Random Fields (CRFs) algorithms demonstrated significant performance in the clinical NLP field in comparison with other machine learning algorithms [19–21]. In the CRFs applications, such as entity extraction and text classification, we usually wish to predict a vector Y = [y0; y1; : : : ; ym] of random variables given an observed feature vector X = [x0; x1; : : : ; xn], which requires us to label the words in a sentence with their corresponding features (i.e., contextual information) which subsequently used for training. The features can be part-of-speech (POS), neighboring words and word bigrams, prefixes and suffixes, capitalization, membership in domainspecific lexicons, semantic information of words, etc. Considering the advantage of CRFs in J Biomed Inform. Author manuscript; available in PMC 2017 October 01.
Hong et al.
Page 4
Author Manuscript
the contextual information understanding and decent NLP performance, we leveraged CRFs for the purpose of the attributes extraction and classification and the performance tuning in the present study.
Author Manuscript
Current efforts to develop international recommendation standard models in clinical domains have laid the foundation for modeling and representing computable diagnostic criteria. There are a number of clinical data models developed in related fields (eg, QDM, Clinical Element Models [CEMs] [22], and HL7 Fast Health Interoperable Resources [FHIR] [23]). QDM is designed to allow EHRs and other clinical electronic systems to share a common understanding and interpretation of the clinical data. It allows quality measure developers and many clinical researchers or performers to clearly and unambiguously describe the data required to calculate the quality measure. Different from CEM and FHIR, QDM contains both a data model module and a logic module. The latter handles logic expressions elegantly with a collection of functions, logic operators and temporal operators. Therefore, we chose QDM as the information model for standard representation of diagnostic criteria in the present study. 2.2 Clinical Text Computerization and Standardization The related studies on clinical text computerization mainly include the following three aspects.
Author Manuscript Author Manuscript
1.
Clinical guideline computerization and Computer Interpretable Guideline (CIG) Systems. Various computerized clinical guidelines and the decision support systems that incorporate the guidelines have been developed. Researchers have tried different approaches to computerizing clinical practice guidelines [24–27], but those guidelines cover many complex medical procedures; thus, the application of these studies in real-world clinical practice is still very limited. However, the methods used to computerize guidelines are valuable in addressing the issues in diagnostic criteria computerization.
2.
Clinical NLP technologies. Unstructured clinical text mainly exists in the form of clinical notes, eligibility criteria, and clinical guidelines. There is much precedent on the work of clinical NLP applications using machine learning, rule-based methods, and other novel methods [19, 28, 29]. These studies offer valuable contribution in exploring different methods to automatically process information in clinical text.
3.
Formalization method studies on clinical research data. Some previous studies investigated the eligibility criteria in clinical trial protocols, developed approaches for eligibility criteria extraction and semantic representation, and used hierarchical clustering for dynamic categorization of such criteria [28, 30]. For example, EliXR provided a corpus-based knowledge acquisition framework that uses the Unified Medical Language System (UMLS) to standardize eligibility-concept encoding and to enrich eligibility-concept relations for clinical research eligibility criteria from
J Biomed Inform. Author manuscript; available in PMC 2017 October 01.
Hong et al.
Page 5
Author Manuscript
text. QDM-based phenotyping methods used for identification of patient cohorts from EHR data also provide valuable reference on our work [31]. Although current studies on the computerization and standardization of diagnostic criteria are still immature, there are some studies that started working on the diagnostic criteria computerization and only focused on some particular diseases. Examples of such studies include the computerized diagnostic criterion of inclusion body myositis [32] or Brugadatype electrocardiograms [33]. However, few of the current studies are taken from the perspective of using a standard information model to build generalizable and scalable methods for the diagnostic criteria computerization.
3 Materials and Methods 3.1 Materials
Author Manuscript
3.1.1 Data Collection—In this study, our diagnostic criteria were collected from a number of sources, including medical textbooks, journal papers, documents issued by professional organizations (such as the World Health Organization [WHO] [34]), and the Internet. Table 1 shows an example of textual diagnostic criteria for diabetes mellitus [35].
Author Manuscript
In this study, we collected 237 textual diagnostic criteria using web crawler and manual retrieval. According to our previous study [36], most commonly used datatypes in diagnostic criteria include laboratory test, symptom, diagnostic study, diagnostic activity, physical exam, medication, recommended procedure and patient characteristic. In this study, for the experiments, we randomly selected 63 out of these 237 textual diagnostic criteria and focused on the computational methods for two typical datatypes: Symptom and Laboratory Test. Table 2 shows the number and source of the selected diagnostic criteria by 6 clinical topics. 3.1.2 The Multi-purpose Annotation Environment Annotator—We used the Multipurpose Annotation Environment (MAE) toolkit [37] for manually annotating attributes of individual diagnostic criteria [38]. The MAE tool allows defining flexible annotation objects in a document type definition (DTD) file in terms of the annotation task, and it also enables easy annotation by simply highlighting the word and selecting defined entity tags. The information a bout a tag is added to the appropriate tab at the bottom of the tool display window. The id, start, end, and text features are automatically generated by MAE, which makes the MAE fit well for our task. Figure 1 shows our manual annotation example for the attributes of a laboratory test in an individual criterion: Renal, Creatinine (mg/dL):>5.0 or
1), it will be classified into different categoties: Caterogy (Y1) to Caterogy (Yn). For example:
Author Manuscript
Sentence1 “Serum triglycerides 150 mg/dL (≥1.7 mmol/L) or HDL cholesterol
