Hybrid statistical and mechanistic mathematical model guides mobile health intervention for chronic pain

Nearly a quarter of visits to the Emergency Department are for conditions that could have been managed via outpatient treatment; improvements that allow patients to quickly recognize and receive appropriate treatment are crucial. The growing popularity of mobile technology creates new opportunities for real-time adaptive medical intervention, and the simultaneous growth of big data sources allows for preparation of personalized recommendations. Here we focus on the reduction of chronic suffering in the sickle cell disease community. Sickle cell disease is a chronic blood disorder in which pain is the most frequent complication. There currently is no standard algorithm or analytical method for real-time adaptive treatment recommendations for pain. Furthermore, current state-of-the-art methods have difficulty in handling continuous-time decision optimization using big data. Facing these challenges, in this study we aim to develop new mathematical tools for incorporating mobile technology into personalized treatment plans for pain. We present a new hybrid model for the dynamics of subjective pain that consists of a dynamical systems approach using differential equations to predict future pain levels, as well as a statistical approach tying system parameters to patient data (both personal characteristics and medication response history). Pilot testing of our approach suggests that it has significant potential to predict pain dynamics given patients' reported pain levels and medication usages. With more abundant data, our hybrid approach should allow physicians to make personalized, data driven recommendations for treating chronic pain.Comment: 13 pages, 15 figures, 5 table

An oncocytic adrenal tumour in a patient with Birt‐Hogg‐Dubé syndrome

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106917/1/cen12292.pd

Incidence of Heart Failure or Cardiomyopathy After Adjuvant Trastuzumab Therapy for Breast Cancer

ObjectivesThe purpose of this study was to estimate heart failure (HF) and cardiomyopathy (CM) rates after adjuvant trastuzumab therapy and chemotherapy in a population of older women with early-stage breast cancer.BackgroundNewer biologic therapies for breast cancer such as trastuzumab have been reported to increase HF and CM in clinical trials, especially in combination with anthracycline chemotherapy. Elderly patients, however, typically have a higher prevalence of cardiovascular risk factors and have been underrepresented in trastuzumab clinical trials.MethodsUsing Surveillance, Epidemiology, and End Results-Medicare data from 2000 through 2007, we identified women 67 to 94 years of age with early-stage breast cancer. We calculated 3-year incidence rates of HF or CM for the following mutually exclusive treatment groups: trastuzumab (with or without nonanthracycline chemotherapy), anthracycline plus trastuzumab, anthracycline (without trastuzumab and with or without nonanthracycline chemotherapy), other nonanthracycline chemotherapy, or no adjuvant chemotherapy or trastuzumab therapy. HF or CM events were ascertained from administrative Medicare claims. Poisson regression was used to quantify risk of HF or CM, adjusting for sociodemographic factors, cancer characteristics, and cardiovascular conditions.ResultsWe identified 45,537 older women (mean age: 76.2 years, standard deviation: 6.2 years) with early-stage breast cancer. Adjusted 3-year HF or CM incidence rates were higher for patients receiving trastuzumab (32.1 per 100 patients) and anthracycline plus trastuzumab (41.9 per 100 patients) compared with no adjuvant therapy (18.1 per 100 patients, p < 0.001). Adding trastuzumab to anthracycline therapy added 12.1, 17.9, and 21.7 HF or CM events per 100 patients over 1, 2, and 3 years of follow-up, respectively.ConclusionsHF or CM are common complications after trastuzumab therapy for older women, with higher rates than those reported from clinical trials

Annual report for active IDOT wetland mitigation and hydrologic monitoring sites, September 1, 2014 through August 31, 2015

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Deep Learning for Neuroimaging: a Validation Study

Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager’s toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. These methods include deep belief networks and their building block the restricted Boltzmann machine. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data