BackgroundHeart failure is one of the leading causes of hospitalization in the United States. Advances in big data solutions allow for storage, management, and mining of large volumes of structured and semi-structured data, such as complex healthcare data. Applying these advances to complex healthcare data has led to the development of risk prediction models to help identify patients who would benefit most from disease management programs in an effort to reduce readmissions and healthcare cost, but the results of these efforts have been varied. The primary aim of this study was to develop a 30-day readmission risk prediction model for heart failure patients discharged from a hospital admission.MethodsWe used longitudinal electronic medical record data of heart failure patients admitted within a large healthcare system. Feature vectors included structured demographic, utilization, and clinical data, as well as selected extracts of un-structured data from clinician-authored notes. The risk prediction model was developed using deep unified networks (DUNs), a new mesh-like network structure of deep learning designed to avoid over-fitting. The model was validated with 10-fold cross-validation and results compared to models based on logistic regression, gradient boosting, and maxout networks. Overall model performance was assessed using concordance statistic. We also selected a discrimination threshold based on maximum projected cost saving to the Partners Healthcare system.ResultsData from 11,510 patients with 27,334 admissions and 6369 30-day readmissions were used to train the model. After data processing, the final model included 3512 variables. The DUNs model had the best performance after 10-fold cross-validation. AUCs for prediction models were 0.664 ± 0.015, 0.650 ± 0.011, 0.695 ± 0.016 and 0.705 ± 0.015 for logistic regression, gradient boosting, maxout networks, and DUNs respectively. The DUNs model had an accuracy of 76.4% at the classification threshold that corresponded with maximum cost saving to the hospital.ConclusionsDeep learning techniques performed better than other traditional techniques in developing this EMR-based prediction model for 30-day readmissions in heart failure patients. Such models can be used to identify heart failure patients with impending hospitalization, enabling care teams to target interventions at their most high-risk patients and improving overall clinical outcomes.Electronic supplementary materialThe online version of this article (10.1186/s12911-018-0620-z) contains supplementary material, which is available to authorized users.

Perception of the smell of a food precedes its ingestion and perception of its flavor. The neurobiological underpinnings of this association are not well understood. Of central interest is whether the same neural circuits code for anticipatory and consummatory phases. Here, we show that the amygdala and mediodorsal thalamus respond preferentially to food odors that predict immediate arrival of their associated drink (FO+) compared to food odors that predict delivery of a tasteless solution (FO-) and compared to the receipt of the drink. In contrast, the left insula/operculum responds preferentially to the drink, whereas the right insula/operculum and left orbitofrontal cortex respond to FO+ and drink. These findings indicate separable and overlapping representation of anticipatory and consummatory chemosensation. Moreover, since ratings of perceived pleasantness of FO+, FO-, and drink were similar, the response in the amygdala and thalamus cannot reflect acquired affective value but rather predictive meaning or biological relevance.

Previous investigations consistently report a negative association between body mass index (BMI) and response in the caudate nucleus during the consumption of palatable and energy dense food. Since this response has also been linked to weight gain, we sought to replicate this finding and determine if the reduced response is associated with measures of impulsivity or food reward. Two studies were conducted in which fMRI was used to measure brain response to milkshake and a tasteless control solution. In study 1 (n = 25) we also assessed self-reported impulsivity, willingness to work for food, and subjective experiences of the pleasantness of milkshake taste and aroma. Replicating prior work, we report a negative association between BMI and brain response to milkshake vs. tasteless in the caudate nucleus. The opposite pattern was observed in the ventral putamen, with greater response observed in the 13 overweight compared to the 12 healthy weight subjects. Regression of brain response against impulsivity and food reward measures revealed one significant association: in the overweight but not healthy weight group self-reported impulsivity was negatively associated with caudate response to milkshake. In study 2 (n = 14), in addition to assessing brain response to milkshake and tasteless solutions subjects completed a go/no-go task outside the scanner. As predicted, we identified an inverse relationship between caudate response to milkshake vs. tasteless and failure to inhibit responses on the no go trials. We conclude that the inverse correlation between BMI and caudate response to milkshake is associated with impulsivity but not food reward. These findings suggest that response to milkshake in the dorsal striatum may be related to weight gain by promoting impulsive eating behavior.

BackgroundThe uptake of digital health technology (DHT) has been surprisingly low in clinical practice. Despite showing great promise to improve patient outcomes and disease management, there is limited information on the factors that contribute to the limited adoption of DHT, particularly for hypertension management.ObjectiveThis scoping review provides a comprehensive summary of barriers to and facilitators of DHT adoption for hypertension management reported in the published literature with a focus on provider- and patient-related barriers and facilitators.MethodsThis review followed the methodological framework developed by Arskey and O’Malley. Systematic literature searches were conducted on PubMed or Medical Literature Analysis and Retrieval System Online, Cumulative Index to Nursing and Allied Health Literature, and Excerpta Medica database. Articles that reported on barriers to and/or facilitators of digital health adoption for hypertension management published in English between 2008 and 2017 were eligible. Studies not reporting on barriers or facilitators to DHT adoption for management of hypertension were excluded. A total of 2299 articles were identified based on the above criteria after removing duplicates, and they were assessed for eligibility. Of these, 2165 references did not meet the inclusion criteria. After assessing 134 studies in full text, 98 studies were excluded (full texts were either unavailable or studies did not fulfill the inclusion criteria), resulting in a final set of 32 articles. In addition, 4 handpicked articles were also included in the review, making it a total of 36 studies.ResultsA total of 36 studies were selected for data extraction after abstract and full-text screening by 2 independent reviewers. All conflicts were resolved by a third reviewer. Thematic analysis was conducted to identify major themes pertaining to barriers and facilitators of DHT from both provider and patient perspectives. The key facilitators of DHT adoption by physicians that were identified include ease of integration with clinical workflow, improvement in patient outcomes, and technology usability and technical support. Technology usability and timely technical support improved self-management and patient experience, and positive impact on patient-provider communication were most frequently reported facilitators for patients. Barriers to use of DHTs reported by physicians include lack of integration with clinical workflow, lack of validation of technology, and lack of technology usability and technical support. Finally, lack of technology usability and technical support, interference with patient-provider relationship, and lack of validation of technology were the most commonly reported barriers by patients.ConclusionsFindings suggest the settings and context in which DHTs are implemented and individuals involved in implementation influence adoption. Finally, to fully realize the potential of digitally enabled hypertension management, there is a greater need to validate these technologies to provide...

Background: Smoking cessation is often followed by weight gain. Eating behaviors and weight change have been linked to the brain response to food, but it is unknown whether smoking influences this response. Objective: We determined the influence of smoking status (smokers compared with nonsmokers) on the brain response to food in regions associated with weight changes in nonsmokers. Design: In study 1, we used functional MRI (fMRI) to identify regions of the brain associated with weight change in nonsmokers. BMI and the brain response to a milk shake, which is a palatable and energy-dense food, were measured in a group of 27 nonsmokers (5 men). Sixteen subjects (3 men) returned 1 y later for BMI reassessment. The change in BMI was regressed against the brain response to isolate regions associated with weight change. In study 2, to determine whether smokers showed altered responses in regions associated with weight change, we assessed the brain response to a milk shake in 11 smokers. The brain response to a milk shake compared with a tasteless control solution was assessed in 11 smokers (5 men) in comparison with a group of age-, sex-and body weight-matched nonsmokers selected from the pool of nonsmokers who participated in study 1. Results: The response in the midbrain, hypothalamus, thalamus, and ventral striatum was positively associated with weight change at the 1-y follow-up in 16 nonsmokers. Compared with nonsmokers, smokers had a greater response to milk shakes in the hypothalamus. Conclusion: Smokers display an altered brain response to food in the hypothalamus, which is an area associated with long-term weight change in nonsmokers.Am J Clin Nutr 2013;97:15-22.