Stephen Luther

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Statistical text mining was used to supplement efforts to develop a clinical vocabulary for post-traumatic stress disorder (PTSD) in the VA. A set of outpatient progress notes was collected for a cohort of 405 unique veterans with PTSD and a comparison group of 392 with other psychological conditions at one VA hospital. Two methods were employed: (1)(More)
OBJECTIVE To determine how well statistical text mining (STM) models can identify falls within clinical text associated with an ambulatory encounter. MATERIALS AND METHODS 2241 patients were selected with a fall-related ICD-9-CM E-code or matched injury diagnosis code while being treated as an outpatient at one of four sites within the Veterans Health(More)
Unintentional injury due to falls is a serious and expensive health problem among the elderly. This is especially true in the Veterans Health Administration (VHA) ambulatory care setting, where nearly 40% of the male patients are 65 or older and at risk for falls. Health service researchers and clinicians can utilize VHA administrative data to identify and(More)
In 2007, suicide was the tenth leading cause of death in the U.S. Given the significance of this problem, suicide was the focus of the 2011 Informatics for Integrating Biology and the Bedside (i2b2) Natural Language Processing (NLP) shared task competition (track two). Specifically, the challenge concentrated on sentiment analysis, predicting the presence(More)
Literature shows that some health outcomes (e.g., eating, breathing, and speaking) are directly related to posture. Evidence of outcomes mediated by wheelchair seated posture is limited to interface pressure, physical function, and wheelchair skills and safety. This study's purpose was to develop and validate a rapid, low-burden, paper-pencil assessment of(More)
Text analytic methods are often aimed at extracting useful information from the vast array of unstructured, free format text documents that are created by almost all organizational processes. The success of any text mining application rests on the quality of the underlying data being analyzed, including both predictive features and outcome labels. In this(More)
Statistical text mining and natural language processing have been shown to be effective for extracting useful information from medical documents. However, neither technique is effective at extracting the information stored in semi-structure text elements. A prototype system (TagLine) was developed to extract information from the semi-structured text using(More)