Mcnallymorrow6134

A substantial percentage of prostate cancer cases are overdiagnosed and overtreated due to the challenge in deter- mining aggressiveness. Multi-parametric MR is a powerful imaging technique to capture distinct characteristics of prostate lesions that are informative for aggressiveness assessment. However, manual interpretation requires a high level of expertise, is time-consuming, and significant inter-observer variation exists for radiologists. We propose a completely automated approach to assessing pixel-level aggressiveness of prostate cancer in multi-parametric MRI. Our model efficiently combines traditional computer vision and deep learning algorithms, to remove reliance on manual features, prostate segmentation, and prior lesion detection and identified optimal combinations of MR pulse sequences for assessment. Using ADC and DWI, our proposed model achieves ROC-AUC of 0.86 and ROC-AUC of 0.88 for the diagnosis of aggressive and non-aggressive prostate lesions, respectively. In performing pixel-level clas- sification, our model's classifications are easily interpretable and allow clinicians to infer localized analyses of the lesion.The Clinical Classifications Software (CCS), by grouping International Classification of Diseases (ICD), provides the capacity to better account for clinical conditions for payers, policy makers, and researchers to analyze outcomes, costs, and utilization. There is a critical need for additional research on application of CCS categories to validate the clinical condition representation and to prevent gaps in research. This study compared the event frequency and ICD codes of CCS categories with significant changes from the first three quarters of 2015 to 2016 using National Inpatient Sample data. A total of 63 of the 285 diagnostics CCS were identified with greater than 20% change, of which 32 had increased and 31 decreased over time. Due to the complexity associated with the transition from ICD-9 to ICD-10, more studies are needed to identify the reason for the changes to improve CCS use for ICD-10 and its comparability with ICD-9 based data.The Department of Veteran's Affairs (VA) archives one of the largest corpora of clinical notes in their corporate data warehouse as unstructured text data. Unstructured text easily supports keyword searches and regular expressions. Often these simple searches do not adequately support the complex searches that need to be performed on notes. For example, a researcher may want all notes with a Duke Treadmill Score of less than five or people that smoke more than one pack per day. Range queries like this and more can be supported by modelling text as semi-structured documents. In this paper, we implement a scalable machine learning pipeline that models plain medical text as useful semi-structured documents. We improve on existing models and achieve an F1-score of 0.912 and scale our methods to the entire VA corpus.This project aims to assess usability and acceptance of a customized Epic-based flowsheet designed to streamline the complex workflows associated with care of patients with implanted Deep Brain Stimulators (DBS). DBS patient care workflows are markedly fragmented, requiring providers to switch between multiple disparate systems. This is the first attempt to systematically evaluate usability of a unified solution built as a flowsheet in Epic. Iterative development processes were applied, collecting formal feedback throughout. Evaluation consisted of cognitive walkthroughs, heuristic analysis, and 'think-aloud' technique. Participants completed 3 tasks and multiple questionnaires with Likert-like questions and long-form written feedback. Results demonstrate that the strengths of the flowsheet are its consistency, mapping, and affordance. System Usability Scale scores place this first version of the flowsheet above the 70th percentile with an 'above average' usability rating. selleck kinase inhibitor Most importantly, a copious amount of actionable feedback was captured to inform the next iteration of this build.While using data standards can facilitate research by making it easier to share data, manually mapping to data standards creates an obstacle to their adoption. Semi-automated mapping strategies can reduce the manual mapping burden. Machine learning approaches, such as artificial neural networks, can predict mappings between clinical data standards but are limited by the need for training data. We developed a graph database that incorporates the Biomedical Research Integrated Domain Group (BRIDG) model, Common Data Elements (CDEs) from the National Cancer Institute's (NCI) cancer Data Standards Registry and Repository, and the NCI Thesaurus. We then used a shortest path algorithm to predict mappings from CDEs to classes in the BRIDG model. The resulting graph database provides a robust semantic framework for analysis and quality assurance testing. Using the graph database to predict CDE to BRIDG class mappings was limited by the subjective nature of mapping and data quality issues.Half a million people die every year from smoking-related issues across the United States. It is essential to identify individuals who are tobacco-dependent in order to implement preventive measures. In this study, we investigate the effectiveness of deep learning models to extract smoking status of patients from clinical progress notes. A Natural Language Processing (NLP) Pipeline was built that cleans the progress notes prior to processing by three deep neural networks a CNN, a unidirectional LSTM, and a bidirectional LSTM. Each of these models was trained with a pre- trained or a post-trained word embedding layer. Three traditional machine learning models were also employed to compare against the neural networks. Each model has generated both binary and multi-class label classification. Our results showed that the CNN model with a pre-trained embedding layer performed the best for both binary and multi- class label classification.An important function of the patient record is to effectively and concisely communicate patient problems. In many cases, these problems are represented as short textual summarizations and appear in various sections of the record including problem lists, diagnoses, and chief complaints. While free-text problem descriptions effectively capture the clinicians' intent, these unstructured representations are problematic for downstream analytics. We present an automated approach to converting free-text problem descriptions into structured Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) expressions. Our methods focus on incorporating new advances in deep learning to build formal semantic representations of summary level clinical problems from text. We evaluate our methods against current approaches as well as against a large clinical corpus. We find that our methods outperform current techniques on the important relation identification sub-task of this conversion, and highlight the challenges of applying these methods to real-world clinical text.