15,795 research outputs found

    Predicting Skin Permeability by means of Computational Approaches : Reliability and Caveats in Pharmaceutical Studies

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    © 2019 American Chemical Society.The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, data set building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues that have arisen, and to address the challenges to develop in future research.Peer reviewedFinal Accepted Versio

    Econometrics meets sentiment : an overview of methodology and applications

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    The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

    Predicting Factors of Re-Hospitalization After Medically Managed Intensive Inpatient Services in Opioid Use Disorder

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    IntroductionOpioid use disorder has continued to rise in prevalence across the United States, with an estimated 2.5 million Americans ailing from the condition (NIDA, 2020). Medically managed detoxification incurs substantial costs and, when used independently, may not be effective in preventing relapse (Kosten & Baxter, 2019). While numerous studies have focused on predicting the factors of developing opioid use disorder, few have identified predictors of readmission to medically managed withdrawal at an inpatient level of care. Utilizing a high-fidelity dataset from a large multi-site behavioral health hospital, these predictors are explored. MethodsPatients diagnosed with Opioid Use Disorder and hospitalized in the inpatient level of care were analyzed to identify readmission predictors. Factors including patient demographics, patient-reported outcome measures, and post-discharge treatment interventions were included. Patients re-hospitalized to the inpatient level of care were binary labeled in the dataset, and various machine learning algorithms were tested, including machine learning techniques. Methods include random forest, gradient boosting, and deep learning techniques. Evaluation statistics include specificity, accuracy, precision, and Matthew\u27s Coefficient. ResultsOverall, there was a wide variation if correctly predicting the class of patients that would readmit to a medically managed level of inpatient detoxification. Out of the six models evaluated, three of the six did not converge, thus not producing a viable feature ranking. However, of the other three models that did converge, the deep learning model produced almost perfect classification, producing an accuracy of .98. AdaBoost and the logistic regression model produced an accuracy of .97 and .61, respectively. Each of these models produced a similar set of features that were important to predicting which patient profile would readmit to medically managed inpatient detoxification. ConclusionsThe results indicate that overall reduction in the Quick Inventory of Depressive Symptomology, discharge disposition, age, length of stay, and a patient\u27s total number of diagnoses were important features at predicting readmission. Additionally, deep learning algorithms vastly outperformed other machine learning algorithms

    The pictures we like are our image: continuous mapping of favorite pictures into self-assessed and attributed personality traits

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    Flickr allows its users to tag the pictures they like as “favorite”. As a result, many users of the popular photo-sharing platform produce galleries of favorite pictures. This article proposes new approaches, based on Computational Aesthetics, capable to infer the personality traits of Flickr users from the galleries above. In particular, the approaches map low-level features extracted from the pictures into numerical scores corresponding to the Big-Five Traits, both self-assessed and attributed. The experiments were performed over 60,000 pictures tagged as favorite by 300 users (the PsychoFlickr Corpus). The results show that it is possible to predict beyond chance both self-assessed and attributed traits. In line with the state-of-the art of Personality Computing, these latter are predicted with higher effectiveness (correlation up to 0.68 between actual and predicted traits)

    Improving Emergency Department Patient Flow Through Near Real-Time Analytics

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    ABSTRACT IMPROVING EMERGENCY DEPARTMENT PATIENT FLOW THROUGH NEAR REAL-TIME ANALYTICS This dissertation research investigates opportunities for developing effective decision support models that exploit near real-time (NRT) information to enhance the operational intelligence within hospital Emergency Departments (ED). Approaching from a systems engineering perspective, the study proposes a novel decision support framework for streamlining ED patient flow that employs machine learning, statistical and operations research methods to facilitate its operationalization. ED crowding has become the subject of significant public and academic attention, and it is known to cause a number of adverse outcomes to the patients, ED staff as well as hospital revenues. Despite many efforts to investigate the causes, consequences and interventions for ED overcrowding in the past two decades, scientific knowledge remains limited in regards to strategies and pragmatic approaches that actually improve patient flow in EDs. Motivated by the gaps in research, we develop a near real-time triage decision support system to reduce ED boarding and improve ED patient flow. The proposed system is a novel variant of a newsvendor modeling framework that integrates patient admission probability prediction within a proactive ward-bed reservation system to improve the effectiveness of bed coordination efforts and reduce boarding times for ED patients along with the resulting costs. Specifically, we propose a cost-sensitive bed reservation policy that recommends optimal bed reservation times for patients right during triage. The policy relies on classifiers that estimate the probability that the ED patient will be admitted using the patient information collected and readily available at triage or right after. The policy is cost-sensitive in that it accounts for costs associated with patient admission prediction misclassification as well as costs associated with incorrectly selecting the reservation time. To achieve the objective of this work, we also addressed two secondary objectives: first, development of models to predict the admission likelihood and target admission wards of ED patients; second, development of models to estimate length-of-stay (LOS) of ED patients. For the first secondary objective, we develop an algorithm that incorporates feature selection into a state-of-the-art and powerful probabilistic Bayesian classification method: multi-class relevance vector machine. For the second objective, we investigated the performance of hazard rate models (in particual, the non-parametric Cox proportional hazard model, parametric hazard rate models, as well as artificial neural networks for modeling the hazard rate) to estimate ED LOS by using the information that is available at triage or right after as the covariates in the models. The proposed models are tested using extensive historical data from several U.S. Department of Veterans Affairs Medical Centers (VAMCs) in the Mid-West. The Case Study using historical data from a VAMC demonstrates that applying the proposed framework leads to significant savings associated with reduced boarding times, in particular, for smaller wards with high levels of utilization. For theory, our primary contribution is the development of a cost sensitive ward-bed reservation model that effectively accounts for various costs and uncertainties. This work also contributes to the development of an integrated feature selection method for classification by developing and validating the mathematical derivation for feature selection during mRVM learning. Another contribution stems from investigating how much the ED LOS estimation can be improved by incorporating the information regarding ED orderable item lists. Overall, this work is a successful application of mixed methods of operation research, machine learning and statistics to the important domain of health care system efficiency improvement

    Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes The 2019 Literature Year in Review

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    Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science\u27s ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploratio
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