High levels of sarcospan are well tolerated and act as a sarcolemmal stabilizer to address skeletal muscle and pulmonary dysfunction in DMD.

Duchenne muscular dystrophy (DMD) is a genetic disorder that causes progressive muscle weakness, ultimately leading to early mortality in affected teenagers and young adults. Previous work from our lab has shown that a small transmembrane protein called sarcospan (SSPN) can enhance the recruitment of adhesion complex proteins to the cell surface. When human SSPN is expressed at three-fold levels in mdx mice, this increase in adhesion complex abundance improves muscle membrane stability, preventing many of the histopathological changes associated with DMD. However, expressing higher levels of human SSPN (ten-fold transgenic expression) causes a severe degenerative muscle phenotype in wild-type mice. Since SSPN-mediated stabilization of the sarcolemma represents a promising therapeutic strategy in DMD, it is important to determine whether SSPN can be introduced at high levels without toxicity. Here, we show that mouse SSPN (mSSPN) can be overexpressed at 30-fold levels in wild-type mice with no deleterious effects. In mdx mice, mSSPN overexpression improves dystrophic pathology and sarcolemmal stability. We show that these mice exhibit increased resistance to eccentric contraction-induced damage and reduced fatigue following exercise. mSSPN overexpression improved pulmonary function and reduced dystrophic histopathology in the diaphragm. Together, these results demonstrate that SSPN overexpression is well tolerated in mdx mice and improves sarcolemma defects that underlie skeletal muscle and pulmonary dysfunction in DMD.

Measuring use of evidence based psychotherapy for posttraumatic stress disorder.

To improve methods of estimating use of evidence-based psychotherapy for posttraumatic stress disorder in the Veteran's health administration, we evaluated administrative data and note text for patients newly enrolling in six VHA outpatient PTSD clinics in New England during the 2010 fiscal year (n = 1,924). Using natural language processing, we developed machine learning algorithms that mimic human raters in classifying note text. We met our targets for algorithm performance as measured by precision, recall, and F-measure. We found that 6.3 % of our study population received at least one session of evidence-based psychotherapy during the initial 6 months of treatment. Evidence-based psychotherapies appear to be infrequently utilized in VHA outpatient PTSD clinics in New England. Our method could support efforts to improve use of these treatments.

Using natural language processing on the free text of clinical documents to screen for evidence of homelessness among US veterans.

Information retrieval algorithms based on natural language processing (NLP) of the free text of medical records have been used to find documents of interest from databases. Homelessness is a high priority non-medical diagnosis that is noted in electronic medical records of Veterans in Veterans Affairs (VA) facilities. Using a human-reviewed reference standard corpus of clinical documents of Veterans with evidence of homelessness and those without, an open-source NLP tool (Automated Retrieval Console v2.0, ARC) was trained to classify documents. The best performing model based on document level work-flow performed well on a test set (Precision 94%, Recall 97%, F-Measure 96). Processing of a naïve set of 10,000 randomly selected documents from the VA using this best performing model yielded 463 documents flagged as positive, indicating a 4.7% prevalence of homelessness. Human review noted a precision of 70% for these flags resulting in an adjusted prevalence of homelessness of 3.3% which matches current VA estimates. Further refinements are underway to improve the performance. We demonstrate an effective and rapid lifecycle of using an off-the-shelf NLP tool for screening targets of interest from medical records.

Automated concept-level information extraction to reduce the need for custom software and rules development.

OBJECTIVE: Despite at least 40 years of promising empirical performance, very few clinical natural language processing (NLP) or information extraction systems currently contribute to medical science or care. The authors address this gap by reducing the need for custom software and rules development with a graphical user interface-driven, highly generalizable approach to concept-level retrieval. MATERIALS AND METHODS: A 'learn by example' approach combines features derived from open-source NLP pipelines with open-source machine learning classifiers to automatically and iteratively evaluate top-performing configurations. The Fourth i2b2/VA Shared Task Challenge's concept extraction task provided the data sets and metrics used to evaluate performance. RESULTS: Top F-measure scores for each of the tasks were medical problems (0.83), treatments (0.82), and tests (0.83). Recall lagged precision in all experiments. Precision was near or above 0.90 in all tasks. Discussion With no customization for the tasks and less than 5 min of end-user time to configure and launch each experiment, the average F-measure was 0.83, one point behind the mean F-measure of the 22 entrants in the competition. Strong precision scores indicate the potential of applying the approach for more specific clinical information extraction tasks. There was not one best configuration, supporting an iterative approach to model creation. CONCLUSION: Acceptable levels of performance can be achieved using fully automated and generalizable approaches to concept-level information extraction. The described implementation and related documentation is available for download.

Evaluation of a generalizable approach to clinical information retrieval using the automated retrieval console (ARC).

Reducing custom software development effort is an important goal in information retrieval (IR). This study evaluated a generalizable approach involving with no custom software or rules development. The study used documents "consistent with cancer" to evaluate system performance in the domains of colorectal (CRC), prostate (PC), and lung (LC) cancer. Using an end-user-supplied reference set, the automated retrieval console (ARC) iteratively calculated performance of combinations of natural language processing-derived features and supervised classification algorithms. Training and testing involved 10-fold cross-validation for three sets of 500 documents each. Performance metrics included recall, precision, and F-measure. Annotation time for five physicians was also measured. Top performing algorithms had recall, precision, and F-measure values as follows: for CRC, 0.90, 0.92, and 0.89, respectively; for PC, 0.97, 0.95, and 0.94; and for LC, 0.76, 0.80, and 0.75. In all but one case, conditional random fields outperformed maximum entropy-based classifiers. Algorithms had good performance without custom code or rules development, but performance varied by specific application.