Development of an ontology to characterize mental functioning.

PURPOSE OF THE ARTICLE: This article describes a conceptual and methodological approach to integrating functional information into an ontology to categorize mental functioning, which to date is an under-developed area of classification, and supports our work with the United States (U.S.) Social Security Administration (SSA). DESIGN AND METHODOLOGICAL PROCEDURES: Conceptualizing and defining mental functioning was paramount to develop natural language processing (NLP) tools to support our use case. The International Classification of Functioning, Disability, and Health (ICF) was the framework used to conceptualize mental functioning at the activities and participation level in clinical records. To address challenges that arose when applying the ICF as to what should or should not be classified as mental functioning, a mental functioning domain ontology was developed that rearranged, reclassified and incorporated all ICF key components, concepts, classifications, and their definitions. CONCLUSIONS: Challenges emerged in the extent to which we could directly align components in the ICF into an applied ontology of mental functioning. These conceptual challenges required rearrangement of ICF components to adequately support our use case within the social security disability determination process. Findings also have implications to support future NLP efforts for behavioral health outcomes and policy research.

Mental functioning in everyday life is an important area of inquiry from the perspectives of public health, health policy, healthcare, and overall individual level health and well-being.A domain ontology of mental functioning that defines concepts and their relationships, and provides a common terminology with definitions, would enable interdisciplinary communication, research, and collaboration.A clearer conceptual model of mental functioning can improve the development of software that can identify, codify, and organize mental functioning information within clinical records into data that can be analyzed.The International Classification of Functioning, Disability and Health was utilized to conceptualize mental functioning and to guide the development of a proposed domain ontology of mental functioning.

Informatics Research on Mental Health Functioning: Decision Support for the Social Security Administration Disability Program.

The disability determination process of the Social Security Administration's (SSA's) disability program requires assessing work-related functioning for individual claimants alleging disability due to mental impairment. This task is particularly challenging because the determination process involves the review of a large file of information, including objective medical evidence and self-reports from claimants, families, and former employers. To improve this decision-making process, SSA entered an interagency agreement with the Rehabilitation Medicine Department, Epidemiology and Biostatistics Section, in the Clinical Center of the National Institutes of Health, intending to use data science and informatics to develop decision support tools. This collaborative effort over the past decade has led to the development of the Work Disability-Functional Assessment Battery and has initiated an approach to applying natural language processing to the review of claimants' files for information on mental health functioning. This informatics research collaboration holds promise for improving the process of disability determination for individuals with mental impairments who make claims at the SSA.

Linking Free Text Documentation of Functioning and Disability to the ICF With Natural Language Processing.

Background: Invaluable information on patient functioning and the complex interactions that define it is recorded in free text portions of the Electronic Health Record (EHR). Leveraging this information to improve clinical decision-making and conduct research requires natural language processing (NLP) technologies to identify and organize the information recorded in clinical documentation. Methods: We used natural language processing methods to analyze information about patient functioning recorded in two collections of clinical documents pertaining to claims for federal disability benefits from the U.S. Social Security Administration (SSA). We grounded our analysis in the International Classification of Functioning, Disability, and Health (ICF), and used the Activities and Participation domain of the ICF to classify information about functioning in three key areas: mobility, self-care, and domestic life. After annotating functional status information in our datasets through expert clinical review, we trained machine learning-based NLP models to automatically assign ICF categories to mentions of functional activity. Results: We found that rich and diverse information on patient functioning was documented in the free text records. Annotation of 289 documents for Mobility information yielded 2,455 mentions of Mobility activities and 3,176 specific actions corresponding to 13 ICF-based categories. Annotation of 329 documents for Self-Care and Domestic Life information yielded 3,990 activity mentions and 4,665 specific actions corresponding to 16 ICF-based categories. NLP systems for automated ICF coding achieved over 80% macro-averaged F-measure on both datasets, indicating strong performance across all ICF categories used. Conclusions: Natural language processing can help to navigate the tradeoff between flexible and expressive clinical documentation of functioning and standardizable data for comparability and learning. The ICF has practical limitations for classifying functional status information in clinical documentation but presents a valuable framework for organizing the information recorded in health records about patient functioning. This study advances the development of robust, ICF-based NLP technologies to analyze information on patient functioning and has significant implications for NLP-powered analysis of functional status information in disability benefits management, clinical care, and research.

The Association of Cognitive Reserve in Chronic-Phase Functional and Neuropsychological Outcomes Following Traumatic Brain Injury.

OBJECTIVE: Examine the association of cognitive reserve (CR) factors (estimated premorbid intelligence quotient [IQ], years of education, and occupational attainment) and traumatic brain injury (TBI) severity with functional and neuropsychological outcomes 1 to 5 years following TBI. PARTICIPANTS: Patients with mild (N = 58), moderate (N = 25), or severe (N = 17) TBI. MAIN MEASURES: Cognitive reserve factors (estimated premorbid IQ, years of education, and occupational attainment); neuropsychological test battery; Glasgow Outcome Scale-Extended; Short Form-36 Health Survey. ANALYSES: Spearman-Brown correlations, linear regression models, and analyses of covariance were used to analyze the relation between CR factors and outcome measures. RESULTS: Analyses revealed significant relations between estimated premorbid IQ and neuropsychological outcomes (P < .004): California Verbal Learning Test, Wechsler Adult Intelligence Scale-Fourth Edition working memory, Booklet Category Test, Trail Making Test B, and Grooved Pegboard Test. There was also a significant correlation between estimated premorbid IQ and Wechsler Adult Intelligence Scale-Fourth Edition processing speed. Years of education had significant relations with California Verbal Learning Test and Wechsler Adult Intelligence Scale-Fourth Edition working memory and processing speed scores. There were significant differences between TBI severity groups and performance on the Trail Making Test A, Grooved Pegboard Test, and Finger Tapping Test. CONCLUSIONS: Cognitive reserve factors may be associated with outcomes following TBI. Additional alternatives to TBI severity are needed to help guide rehabilitative planning postinjury.