Notes

Evaluation associated with Predictive Factors for Diarrhea as soon as the Administration regarding Naldemedine.
Research has demonstrated cohort misclassification when studies of suicidal thoughts and behaviors (STBs) rely on ICD-9/10-CM diagnosis codes. Electronic health record (EHR) data are being explored to better identify patients, a process called EHR phenotyping. Most STB phenotyping studies have used structured EHR data, but some are beginning to incorporate unstructured clinical text. In this study, we used a publicly-accessible natural language processing (NLP) program for biomedical text (MetaMap) and iterative elastic net regression to extract and select predictive text features from the discharge summaries of 810 inpatient admissions of interest. Initial sets of 5,866 and 2,709 text features were reduced to 18 and 11, respectively. The two models fit with these features obtained an area under the receiver operating characteristic curve of 0.866-0.895 and an area under the precision-recall curve of 0.800-0.838, demonstrating the approach's potential to identify textual features to incorporate in phenotyping models.Identification of comorbidity subgroups linked with Autism Spectrum Disorder (ASD) could provide promising insight into learning more about this disorder. This study sought to use the Rhode Island All-Payer Claims Database to examine mental health conditions linked to ASD. Medical claims data for ASD patients and one or more mental health conditions were analyzed using descriptive statistics, association rule mining (ARM), and sequential pattern mining (SPM). The results indicated that patients with ASD have a higher proportion of mental health diagnoses than the general pediatric population. ARM and SPM methods identified patterns of comorbidities commonly seen among ASD patients. Based on the observed patterns and temporal sequences, suicidal ideation, mood disorders, anxiety, and conduct disorders may need focused attention prospectively. Understanding more about groupings of ASD patients and their comorbidity burden can help bridge gaps in knowledge and make strides toward improved outcomes for patients with ASD.Due to the fast pace at which randomized controlled trials are published in the health domain, researchers, consultants and policymakers would benefit from more automatic ways to process them by both extracting relevant information and automating the meta-analysis processes. In this paper, we present a novel methodology based on natural language processing and reasoning models to 1) extract relevant information from RCTs and 2) predict potential outcome values on novel scenarios, given the extracted knowledge, in the domain of behavior change for smoking cessation.Dietary supplements (DSs) have been widely used in the U.S. and evaluated in clinical trials as potential interventions for various diseases. However, many clinical trials face challenges in recruiting enough eligible patients in a timely fashion, causing delays or even early termination. Using electronic health records to find eligible patients who meet clinical trial eligibility criteria has been shown as a promising way to assess recruitment feasibility and accelerate the recruitment process. In this study, we analyzed the eligibility criteria of 100 randomly selected DS clinical trials and identified both computable and non-computable criteria. We mapped annotated entities to OMOP Common Data Model (CDM) with novel entities (e.g., DS). We also evaluated a deep learning model (Bi-LSTM-CRF) for extracting these entities on CLAMP platform, with an average F1 measure of 0.601. This study shows the feasibility of automatic parsing of the eligibility criteria following OMOP CDM for future cohort identification.Opioid use disorder (OUD) represents a global public health crisis that challenges classic clinical decision making. As existing hospital screening methods are resource-intensive, patients with OUD are significantly under-detected. An automated and accurate approach is needed to improve OUD identification so that appropriate care can be provided to these patients in a timely fashion. In this study, we used a large-scale clinical database from Mass General Brigham (MGB; formerly Partners HealthCare) to develop an OUD patient identification algorithm, using multiple machine learning methods. Working closely with an addiction psychiatrist, we developed a set of hand-crafted rules for identifying information suggestive of OUD from free-text clinical notes. We implemented a natural language processing (NLP)-based classification algorithm within the Medical Text Extraction, Reasoning and Mapping System (MTERMS) tool suite to automatically label patients as positive or negative for OUD based on these rules. We further used the NLP output as features to build multiple machine learning and a neural classifier. Our methods yielded robust performance for classifying hospitalized patients as positive or negative for OUD, with the best performing feature set and model combination achieving an F1 score of 0.97. These results show promise for the future development of a real-time tool for quickly and accurately identifying patients with OUD in the hospital setting.Left ventricular non-compaction (LVNC) is defined by an increase of trabeculations in left ventricular endo-myocardium. Although LVNC can be in isolation, an increase in hypertrabeculation often accompanies genetic cardiomyopthies. Several enhancements are proposed and implemented to improve a software tool for the automatic quantification of the exact hyper-trabeculation degree in the left ventricular myocardium for a population of patients with LVNC cardiomyopathy (QLVTHC-NC). The software tool is developed and evaluated for a population of 18 patients (133 cardiac images). An end-diastolic cardiac magnetic resonance images of the patients are the input of the software, whereas the left ventricular mass, volumes and proportion of trabeculation produced by the compacted zone and the trabeculated zone are the outputs. Significant improvements are obtained with respect to the manual process, so saving valuable diagnosis time. Comparing the method proposed with the fractal proposal to differentiate LVNC and non-LVNC patients in subjects with previously diagnosed LVNC cardiomyophaty, QLVTHC-NC presents higher diagnostic accuracy and lower complexity and cost than the fractal criterio.Current treatments for major depressive disorder are either less effective for older adults (i.e. pharmacotherapy) or are challenging to extend to community settings (i.e. psychotherapy). To improve and extend mental health treatment for older adults, our team has expanded a previously developed streamlined talk-therapy model to incorporate a technology package that includes patient-reported outcome questions (sent via SMS) and a smartwatch. The goal of this pilot study was to assess and improve the usability, usefulness, and acceptability of the technology package. We completed a pilot feasibility and usability assessment with 15 older adults. Participants demonstrated the feasibility of use of the intervention, successfully completing 99% of their assigned tasks during the pilot. Findings were used to address usability barriers in preparation for future clinical trials. Our results highlight the importance completing usability assessment and involving older adults in the intervention design process when incorporating technology into care.Parkinson's disease (PD) patients require frequent office visits where they are assessed for health state changes using Unified Parkinson's Disease Rating Scale (UPDRS). Inertial wearable sensor devices present a unique opportunity to supplement these assessments with continuous monitoring. In this work, we analyze kinematic features from sensor devices located on feet, wrists, lumbar and sternum for 35 PD subjects as they performed walk trials in two clinical visits, one for each of their self-reported ON and OFF motor states. Our results show that a few features related to subject's whole-body turns and pronation-supination motor events can accurately infer cardinal features of PD like bradykinesia and posture instability and gait disorder (PIGD). In addition, these features can be measured from only two sensors, one located on the affected wrist and one on the lumbar region, thus potentially reducing patient burden of wearing sensors while supporting continuous monitoring in out of office settings.Sepsis, a life-threatening organ dysfunction, is a clinical syndrome triggered by acute infection and affects over 1 million Americans every year. Untreated sepsis can progress to septic shock and organ failure, making sepsis one of the leading causes of morbidity and mortality in hospitals. Early detection of sepsis and timely antibiotics administration is known to save lives. GW788388 In this work, we design a sepsis prediction algorithm based on data from electronic health records (EHR) using a deep learning approach. While most existing EHR-based sepsis prediction models utilize structured data including vitals, labs, and clinical information, we show that incorporation of features based on clinical texts, using a pre-trained neural language representation model, allows for incorporation of unstructured data without an explicit need for ontology-based named-entity recognition and classification. The proposed model is trained on a large critical care database of over 40,000 patients, including 2805 septic patients, and is compared against competing baseline models. In comparison to a baseline model based on structured data alone, incorporation of clinical texts improved AUC from 0.81 to 0.84. Our findings indicate that incorporation of clinical text features via a pre-trained language representation model can improve early prediction of sepsis and reduce false alarms.Texting is ubiquitous with a text frequency of 145 billion/day worldwide. This paper provides partial results of the national demonstration project called the Missouri Quality Improvement Initiative (MOQI). MOQI goals were to reduce avoidable hospitalizations using APRNs to infuse evidence-based practices, model appropriate decisions and improve communication among workers responsible for nursing home resident care. This is a retrospective content analysis of text messages sent and received via a secure, password protected, encrypted mobile text message platform called Mediprocity. Text messages were created by 15 APRNs and a PhD-RN project supervisor working in 16 nursing homes over 6 months (January 1-June 30 2018). During the 6 months of data collection 8,946 text messages were captured, coded and analyzed. Data included 1,018 sent messages and 7,928 received messages. The most common messages sent (n=324) and received (n=2319) were about patient updates. The second most common texts included messages confirming information (n=1312).An increasing number of people survive longer ages leading to a growing population of people 65 years of age or older. A large percentage of this population is afflicted with multiple acute diseases (multi-morbidity). Clinicians need new tools to quantify the relative risk of an adverse event due to each competing disease and prioritize treatment among various diseases affecting a patient. Currently available deep learning survival analysis models have limited ability to incorporate multiple risks. Also, deep learning survival analysis models in current literature work predominantly in the discrete-time domain, while all biochemical processes continuously happen in the body. In this work, we introduce a novel architecture for a continuous-time deep learning model to combat these two issues, DeepCompete, aimed at survival analysis for competing risks. Our model learns the risk of each disease in an entirely data-driven fashion without making strong assumptions about the underlying stochastic processes. Further, we demonstrate that our model has superior results compared to state of the art continuous-time statistical models for survival analysis.
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