JMIR Publications

ABSTRACT

Natural language processing (NLP) in health care enables transformation of complex narrative information into high value products such as clinical decision support and adverse event monitoring in real time via the Electronic Health Record (EHR). However, information technologies for mental health have consistently lagged behind due to the complexity of measuring and modeling mental health and illness. The use of NLP to support management of mental health conditions is a viable topic that has not been explored in depth. This article provides a framework for advancing NLP methods to identify, extract and organize information on mental health and functioning in order to inform the decision-making process applied to assessing mental health. We present a use case related to work disability, guided by the disability determination process of the U.S. Social Security Administration (SSA). From this perspective the following questions must be addressed about each problem leading to a disability benefits claim: when did the problem occur and how long has it existed? How severe is it? Does it affect the person’s ability to work? What is the source of the evidence about the problem? Our framework includes four dimensions of medical information that are central to assessing disability — temporal sequence and duration, severity, context, and the information source.