ACORN SDOH survey: Terminological representation for use with NLP and CDS.

Objective: Social Determinants of Health (SDOH) greatly influence health outcomes. SDOH surveys, such as the Assessing Circumstances & Offering Resources for Needs (ACORN) survey, have been developed to screen for SDOH in Veterans. The purpose of this study is to determine the terminological representation of the ACORN survey, to aid in natural language processing (NLP). Methods: Each ACORN survey question was read to determine its concepts. Next, Solor was searched for each of the concepts and for the appropriate attributes. If no attributes or concepts existed, they were proposed. Then, each question's concepts and attributes were arranged into subject-relation-object triples. Results: Eleven unique attributes and 18 unique concepts were proposed. These results demonstrate a gap in representing SDOH with terminologies. We believe that using these new concepts and relations will improve NLP, and thus, the care provided to Veterans.

Automated Modeling of Clinical Narrative with High Definition Natural Language Processing Using Solor and Analysis Normal Form.

OBJECTIVE: One important concept in informatics is data which meets the principles of Findability, Accessibility, Interoperability and Reusability (FAIR). Standards, such as terminologies (findability), assist with important tasks like interoperability, Natural Language Processing (NLP) (accessibility) and decision support (reusability). One terminology, Solor, integrates SNOMED CT, LOINC and RxNorm. We describe Solor, HL7 Analysis Normal Form (ANF), and their use with the high definition natural language processing (HD-NLP) program. METHODS: We used HD-NLP to process 694 clinical narratives prior modeled by human experts into Solor and ANF. We compared HD-NLP output to the expert gold standard for 20% of the sample. Each clinical statement was judged "correct" if HD-NLP output matched ANF structure and Solor concepts, or "incorrect" if any ANF structure or Solor concepts were missing or incorrect. Judgements were summed to give totals for "correct" and "incorrect". RESULTS: 113 (80.7%) correct, 26 (18.6%) incorrect, and 1 error. Inter-rater reliability was 97.5% with Cohen's kappa of 0.948. CONCLUSION: The HD-NLP software provides useable complex standards-based representations for important clinical statements designed to drive CDS.

Representing second opinion requests from primary care within the Brazilian tele-health program: international classification of primary care, second edition.

Standardization of second opinion question-answer pairs with a classification system can be used to facilitate data sharing and reuse. The Brazilian telehealth program faces the problem of representing biomedical knowledge from the primary care second opinion demands generated by rural health care teams. The objective is to determine if one of the medical classification systems has a superior ability to standardize Portuguese-language second opinion question-answer pairs. Data from 2,638 second opinions from 2010 were randomly reduced to a 264 question-answer pair data set. The semantic meaning of the question-answer pairs was manually assigned to an International Classification of Primary Care, Second edition (ICPC2) code. Eight question-answer pairs did not contain sufficient medical semantic meaning to allow for mapping to an ICPC2 code; 53 question-answer pairs did contain sufficient medical semantic meaning for mapping, however an appropriate ICPC2 code did not exist; and 203 question-answer pairs did contain sufficient medical semantic meaning for mapping to an ICPC2 code. A review of the literature indicates that there is no baseline to compare the 77% success rate against.

Extending SemRep to the Public Health Domain.

We describe the use of a domain-independent methodology to extend a natural language processing (NLP) application, SemRep (Rindflesch, Fiszman, & Libbus, 2005), based on the knowledge sources afforded by the Unified Medical Language System (UMLS®) (Humphreys, Lindberg, Schoolman, & Barnett, 1998) to support the area of health promotion within the public health domain. Public health professionals require good information about successful health promotion policies and programs that might be considered for application within their own communities. Our effort seeks to improve access to relevant information for the public health profession, to help those in the field remain an information-savvy workforce. NLP and semantic techniques hold promise to help public health professionals navigate the growing ocean of information by organizing and structuring this knowledge into a focused public health framework paired with a user-friendly visualization application as a way to summarize results of PubMed searches in this field of knowledge.