HTNSystem: Hypertension Information Extraction System for Unstructured Clinical Notes

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

doi:10.1007/978-3-319-13987-6_21

Cited by 6 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These situations made it difficult for the text mining system to extract the risk factor data [18, 24]. However, these did not have much impact to the calculation of FRS; instead, missing data was the more challenging issue.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Coronary artery disease risk assessment from unstructured electronic health records using text mining

Jonnagaddala

Liaw

Ray

et al. 2015

Journal of Biomedical Informatics

Self Cite

View full text Add to dashboard Cite

Coronary artery disease (CAD) often leads to myocardial infraction, which may be fatal. Risk factors can be used to predict CAD, which may subsequently lead to prevention or early intervention. Patient data such as co-morbidities, medication history, social history and family history are required to determine the risk factors for a disease. However, risk factor data are usually embedded in unstructured clinical narratives if the data is not collected specifically for risk assessment purposes. Clinical text mining can be used to extract data related to risk factors from unstructured clinical notes. This study presents methods to extract Framingham risk factors from unstructured electronic health records using clinical text mining and to calculate 10-year coronary artery disease risk scores in a cohort of diabetic patients. We developed a rule-based system to extract risk factors: age, gender, total cholesterol, HDL-C, blood pressure, diabetes history and smoking history. The results showed that the output from the text mining system was reliable, but there was a significant amount of missing data to calculate the Framingham risk score. A systematic approach for understanding missing data was followed by implementation of imputation strategies. An analysis of the 10-year Framingham risk scores for coronary artery disease in this cohort has shown that the majority of the diabetic patients are at moderate risk of CAD.

show abstract

Section: Discussionmentioning

confidence: 99%

“…The system developed for determining coronary artery disease risk scores (figure 2) is an extension to our work for i2b2 2014 shared task track 2 [17, 18]. The developed system consists of three major components, namely FRS risk factor extraction component I, FRS risk factor extraction component II and post-processing component.…”

Section: Methodsmentioning

confidence: 99%

Coronary artery disease risk assessment from unstructured electronic health records using text mining

Jonnagaddala

Liaw

Ray

et al. 2015

Journal of Biomedical Informatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Work on hypertension has been principally focused on NLP to extract relevant indicators, comorbidities, and drug therapies [21]. Analysis of clinical narratives in the Bulgarian language of 100 million outpatient notes was used to extract numerical blood pressure values with a high sensitivity and recall [47], while term hypertension was extracted from free-text notes, using a rule-based, open-source tool [48]. Clinical notes and several types of medical documents were also used to identify hypertensive individuals using open-source medication information extraction (IE) system MedEx [49].…”

Section: Hypertensionmentioning

confidence: 99%

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Sheikhalishahi¹,

Miotto²,

Dudley³

et al. 2019

JMIR Med Inform

379

259

View full text Add to dashboard Cite

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...

show abstract

“…During CRF model development, the authors experimented with various n-grams on the training set and found that trigrams performed best, so trigram of word and trigram of word POS tags as a feature. The identification of disorder might have been improved further with post processing if custom dictionaries to handle abbreviations, acronyms and misspelled entities were employed (Jonnagaddala, Liaw, Ray, Kumar, & Dai, 2014).…”

Section: Discussionmentioning

confidence: 99%

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

Jonnagaddala

Liaw

Ray

et al. 2015

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

Self Cite

View full text Add to dashboard Cite

Unstructured clinical notes are rich sources for valuable patient information. Information extraction techniques can be employed to extract this valuable information, which in turn can be used to discover new knowledge. Named entity recognition and normalization are the basic tasks involved in information extraction. In this paper, identification of disorder named entities and the mapping of identified disorder entities to SNOMED-CT terminology using UMLS Metathesaurus is presented. A supervised linear chain conditional random field model based on sets of features to predict disorder mentions is used in conjunction with MetaMap to identify and normalize disorders. Error analysis conclude that recall of the developed system can be significantly increased by adding more features during model development and also by using a frame based approach for handling disjoint entities.

show abstract

HTNSystem: Hypertension Information Extraction System for Unstructured Clinical Notes

Cited by 6 publications

References 11 publications

Coronary artery disease risk assessment from unstructured electronic health records using text mining

Coronary artery disease risk assessment from unstructured electronic health records using text mining

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

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

Contact Info

Product

Resources

About