TMUNSW: Identification of Disorders and Normalization to SNOMED-CT Terminology in Unstructured Clinical Notes

Jonnagaddala, Jitendra; Liaw, Siaw‐Teng; Ray, Pradeep; Kumar, M. Anand; Dai, Hong-Jie

doi:10.18653/v1/s15-2068

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…Mapping free text to concepts in an ontology has been done by Gobbels et al [29] who used a Naïve Bayes machine learning system to match phrases from clinical records to SNOMED ontology terms, whereas Kate [30] used learned edit distance patterns to normalize clinical text to UMLS IDs. Other approaches have used tools such as MetaMap [31] or cTAKES [13], to extract and map terms to concepts in the UMLS [32,33]. The tools are generally effective for clinical text, but are not portable: they miss relevant information when applied to colloquial language as the one used in social media [34].…”

Section: Concept Extraction and Normalizationmentioning

confidence: 99%

Capturing the Patient’s Perspective: a Review of Advances in Natural Language Processing of Health-Related Text

Gonzalez-Hernandez¹,

Sarker²,

O’Connor³

et al. 2017

Yearb Med Inform

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SummaryBackground: Natural Language Processing (NLP) methods are increasingly being utilized to mine knowledge from unstructured health-related texts. Recent advances in noisy text processing techniques are enabling researchers and medical domain experts to go beyond the information encapsulated in published texts (e.g., clinical trials and systematic reviews) and structured questionnaires, and obtain perspectives from other unstructured sources such as Electronic Health Records (EHRs) and social media posts. Objectives: To review the recently published literature discussing the application of NLP techniques for mining health-related information from EHRs and social media posts. Methods: Literature review included the research published over the last five years based on searches of PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers. We particularly focused on the techniques employed on EHRs and social media data. Results: A set of 62 studies involving EHRs and 87 studies involving social media matched our criteria and were included in this paper. We present the purposes of these studies, outline the key NLP contributions, and discuss the general trends observed in the field, the current state of research, and important outstanding problems.

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Section: Concept Extraction and Normalizationmentioning

confidence: 99%

Capturing the Patient’s Perspective: a Review of Advances in Natural Language Processing of Health-Related Text

Gonzalez-Hernandez¹,

Sarker²,

O’Connor³

et al. 2017

Yearb Med Inform

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“…In addition, all extracted relevant mentions from the dictionary used in our previous work [2] were merged. For the machine learning-based approach, the training dataset annotated with the occurrence of mention concepts was selected as the training set.…”

Section: Methodsmentioning

confidence: 99%

A context-aware approach for progression tracking of medical concepts in electronic medical records

Chang

Dai

Jonnagaddala

et al. 2015

Journal of Biomedical Informatics

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Electronic medical records (EMRs) for diabetic patients contain information about heart disease risk factors such as high blood pressure, cholesterol levels, smoking status, etc. Discovering the described risk factors and tracking their progression over time may support medical personnel in making clinical decisions, as well as facilitate data modeling and biomedical research. Such highly patient-specific knowledge is essential to driving the advancement of evidence-based practice, and can also help improve personalized medicine and care. One general approach for tracking the progression of diseases and their risk factors described in EMRs is to first recognize all temporal expressions, and then assign each of them to the nearest target medical concept. However, this method may not always provide the correct associations. In light of this, this work introduces a context-aware approach to assign the time attributes of the recognized risk factors by reconstructing contexts that contain more reliable temporal expressions. The evaluation results on the i2b2 test set demonstrate the efficacy of the proposed approach, which achieved an F-score of 0.897. To boost the approach’s ability to process unstructured clinical text and to allow for the reproduction of the demonstrated results, a set of developed .NET libraries used to develop the system is available at https://sites.google.com/site/hongjiedai/projects/nttmuclinicalnet.

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“…Another example is the rule-based FRSSystem capable of extracting Framingham risk factors used for predicting the risk of CAD [ 15 ]. Jonnagaddala et al developed a machine learning-based IE system to identify disease disorder mentions [ 8 ]. The mentioned IE systems can be reused to identify heart disease risk factors but often require customization or addition of new modules.…”

Section: Introductionmentioning

confidence: 99%

Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records

Jonnagaddala

Liaw

Ray

et al. 2015

BioMed Research International

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Heart disease is the leading cause of death worldwide. Therefore, assessing the risk of its occurrence is a crucial step in predicting serious cardiac events. Identifying heart disease risk factors and tracking their progression is a preliminary step in heart disease risk assessment. A large number of studies have reported the use of risk factor data collected prospectively. Electronic health record systems are a great resource of the required risk factor data. Unfortunately, most of the valuable information on risk factor data is buried in the form of unstructured clinical notes in electronic health records. In this study, we present an information extraction system to extract related information on heart disease risk factors from unstructured clinical notes using a hybrid approach. The hybrid approach employs both machine learning and rule-based clinical text mining techniques. The developed system achieved an overall microaveraged F-score of 0.8302.

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TMUNSW: Identification of Disorders and Normalization to SNOMED-CT Terminology in Unstructured Clinical Notes

Cited by 3 publications

References 8 publications

Capturing the Patient’s Perspective: a Review of Advances in Natural Language Processing of Health-Related Text

Capturing the Patient’s Perspective: a Review of Advances in Natural Language Processing of Health-Related Text

A context-aware approach for progression tracking of medical concepts in electronic medical records

Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records

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