Representation of Information about Family Relatives as Structured Data in Electronic Health Records

Zhou, Li; Lu, Yun; Vitale, Christopher J.; Mar, Perry; Chang, Fenny; Dhopeshwarkar, Neil; Rocha, Roberto A.

doi:10.4338/aci-2013-10-ra-0080

Cited by 21 publications

(10 citation statements)

References 13 publications

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“…As for rule-based approaches, the methods in this review include dictionary lookup [110-112], terminology identification based on domain ontologies [3,42,45,58], various types of manually defined rules [37,113], and regular expressions patterns [114,115].…”

Section: Resultsmentioning

confidence: 99%

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

Sheikhalishahi¹,

Miotto²,

Dudley³

et al. 2019

JMIR Med Inform

379

259

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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...

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Section: Resultsmentioning

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

show abstract

“…We first manually reviewed a purposeful training sample and random test sample of clinical notes (including discharge summaries and outpatient notes), identified wound cases, and created a gold standard dataset with the help of domain experts (Steps 1-2). We then trained our NLP system (known as MTERMS) (Zhou et al, 2015(Zhou et al, , 2014(Zhou et al, , 2011 to process the wound information and applied our system on a randomly selected sample of clinical notes (Steps 3-4). Finally, we assessed our automated approach by comparing systemgenerated findings against the gold standard (Step 5).…”

Section: Methodsmentioning

confidence: 99%

“…MTERMS' engine generates structured output in a standard, interoperable documentation format that can be used for subsequent applications. The system was previously validated for identifying clinical terms within narrative health records in order to extract medications, clinical problems (e.g., presence of depression), family history, and so forth, with high accuracy metrics (Zhou et al, 2015(Zhou et al, , 2014(Zhou et al, , 2011. Several preparatory steps for this study included data collection and building a wound minimum-dataset information model.…”

Section: Natural Language Processing Engine Descriptionmentioning

confidence: 99%

“…One promising technique-an automated natural language processing (NLP)-can potentially assist in processing free-text wound information in clinical notes. In previous studies, NLP has been successfully used to extract family history data (Zhou et al, 2014), medication information (Zhou et al, 2011), radiology findings (Pham et al, 2014), and other health information from different types of clinical notes (Demner-Fushman et al, 2009;Meystre et al, 2008). To our knowledge, no studies have explicitly focused on the feasibility of using natural language processing methods to process nursing-sensitive data, such as wound information in clinical notes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated identification of wound information in clinical notes of patients with heart diseases: Developing and validating a natural language processing application

Topaz

Lai

Dowding

et al. 2016

International Journal of Nursing Studies

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The natural language processing system achieved good performance on a subset of randomly selected discharge summaries and outpatient notes. In more than half of the wound notes, there were no coded wound diagnoses, which highlight the significance of using natural language processing to enrich clinical decision making. Our future steps will include expansion of the application's information coverage to other relevant wound factors and validation of the model with external data.

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“…Currently, the patient’s family history in the medical record is the only documentation that we have to understand the health status and social habits of family members. Recent projects have attempted to gain a better view of family history using natural language processing, but these studies have not linked individual records to each other [ 5 ]. Recent literature has demonstrated the capability of linking children with their parents through electronic health records (EHRs) using guarantor and emergency contact information [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of Relationships Between Patients Through Elements in a Data Warehouse Using the Familial, Associational, and Incidental Relationship (FAIR) Initiative: A Pilot Study

English

Kinney²,

Davis³

et al. 2015

JMIR Med Inform

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BackgroundOver the last several years there has been widespread development of medical data warehouses. Current data warehouses focus on individual cases, but lack the ability to identify family members that could be used for dyadic or familial research. Currently, the patient’s family history in the medical record is the only documentation we have to understand the health status and social habits of their family members. Identifying familial linkages in a phenotypic data warehouse can be valuable in cohort identification and in beginning to understand the interactions of diseases among families.ObjectiveThe goal of the Familial, Associational, & Incidental Relationships (FAIR) initiative is to identify an index set of patients’ relationships through elements in a data warehouse.MethodsUsing a test set of 500 children, we measured the sensitivity and specificity of available linkage algorithm identifiers (eg, insurance identification numbers and phone numbers) and validated this tool/algorithm through a manual chart audit.ResultsOf all the children, 52.4% (262/500) were male, and the mean age of the cohort was 8 years old (SD 5). Of the children, 51.6% (258/500) were identified as white in race. The identifiers used for FAIR were available for the majority of patients: insurance number (483/500, 96.6%), phone number (500/500, 100%), and address (497/500, 99.4%). When utilizing the FAIR tool and various combinations of identifiers, sensitivity ranged from 15.5% (62/401) to 83.8% (336/401), and specificity from 72% (71/99) to 100% (99/99). The preferred method was matching patients using insurance or phone number, which had a sensitivity of 72.1% (289/401) and a specificity of 94% (93/99). Using the Informatics for Integrating Biology and the Bedside (i2b2) warehouse infrastructure, we have now developed a Web app that facilitates FAIR for any index population.ConclusionsFAIR is a valuable research and clinical resource that extends the capabilities of existing data warehouses and lays the groundwork for family-based research. FAIR will expedite studies that would otherwise require registry or manual chart abstraction data sources.

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Representation of Information about Family Relatives as Structured Data in Electronic Health Records

Cited by 21 publications

References 13 publications

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

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

Automated identification of wound information in clinical notes of patients with heart diseases: Developing and validating a natural language processing application

Identification of Relationships Between Patients Through Elements in a Data Warehouse Using the Familial, Associational, and Incidental Relationship (FAIR) Initiative: A Pilot Study

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