Improving identification of fall-related injuries in ambulatory care using statistical text mining.

Objectives: We determined whether statistical text mining (STM) can identify fall-related injuries in electronic health record (EHR) documents and the impact on STM models of training on documents from a single or multiple facilities.

Methods: We obtained fiscal year 2007 records for Veterans Health Administration (VHA) ambulatory care clinics in the southeastern United States and Puerto Rico, resulting in a total of 26 010 documents for 1652 veterans treated for fall-related injury and 1341 matched controls. We used the results of an STM model to predict fall-related injuries at the visit and patient levels and compared them with a reference standard based on chart review.

Finding falls in ambulatory care clinical documents using statistical text mining.

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 Administration. All clinical documents within a 48-h window of the recorded E-code or injury diagnosis code for each patient were obtained (n=26 010; 611 distinct document titles) and annotated for falls.

Throw the bath water out, keep the baby: keeping medically-relevant terms for text mining.

The purpose of this research is to answer the question, can medically-relevant terms be extracted from text notes and text mined for the purpose of classification and obtain equal or better results than text mining the original note? A novel method is used to extract medically-relevant terms for the purpose of text mining. A dataset of 5,009 EMR text notes (1,151 related to falls) was obtained from a Veterans Administration Medical Center. The dataset was processed with a natural language processing (NLP) application which extracted concepts based on SNOMED-CT terms from the Unified Medical Language System (UMLS) Metathesaurus.

Using ontology network structure in text mining.

Statistical text mining treats documents as bags of words, with a focus on term frequencies within documents and across document collections. Unlike natural language processing (NLP) techniques that rely on an engineered vocabulary or a full-featured ontology, statistical approaches do not make use of domain-specific knowledge. The freedom from biases can be an advantage, but at the cost of ignoring potentially valuable knowledge.

Use of artificial neural networks to determine cognitive impairment and therapeutic effectiveness in Alzheimer's transgenic mice.

Behavioral testing of transgenic mouse models of Alzheimer's disease (AD) is the functional endpoint for determining the effectiveness of therapeutic interventions and elucidating AD pathogenesis. Utilizing these mouse models, there have been remarkably few attempts to analyze multiple behavioral measures/tasks with higher-level computation techniques, either to distinguish performance between transgenic groups or to reveal any "overall" cognitive benefit of a given therapeutic. The present study compared the classificatory accuracy of artificial neural networks (ANNs) versus more traditional discriminant function analysis (DFA) using multiple behavioral measures/tasks from two AD transgenic mouse investigations.

Special healthcare taxing districts: association with population health status.

Background: Locally generated special healthcare taxes are an important component of community infrastructure, but their impact on the health status of populations has not been systematically addressed.

Methods: Florida counties were segmented on the basis of the use/nonuse of locally generated tax dollars for health care during the 1992-1996 period and analyzed in 2004. Linear mixed-effects regression analysis was used to test a model in which taxing behavior served as the primary predictor variable for total age-adjusted and selected cause-specific mortality.

Hispanic health status in Orange County, Florida.

Purpose: To assess the health status of the Hispanic population of Orange County, Florida.

Methods: The methodology utilized secondary data for 66 ethnically identified indicators in a comparative framework applied for a 5-year period (1997-2001).

Findings: Orange County Hispanics are younger with lower per capita income than their Florida peers, less likely to be White, and much more likely to be of Puerto Rican origin.

The application of volume-outcome contouring in data warehousing.

Despite a compelling body of published research on the nature of provider volume and clinical outcomes, healthcare executives and policymakers have not managed to develop and implement systems that are useful in directing patients to higher volume providers via selective referral or avoidance. A specialized data warehouse application, utilizing hospital discharge data linked to physician biographical information, allows detailed analysis of physician and hospital volume and the resulting pattern (contour) of related outcomes such as mortality, complications, and medical errors. The approach utilizes a historical repository of hospital discharge data in which the outcomes of interest, important patient characteristics and risk factors used in severity-adjusting of the outcomes are derived from the coding structure of the data.