A simple model of coupled individual behavior and its impact on epidemic dynamics.

Containing infectious disease outbreaks is a complex challenge that usually requires the deployment of multiple intervention strategies. While mathematical modeling of infectious diseases is a widely accepted tool to evaluate intervention strategies, most models and studies overlook the interdependence between individuals' reactions to simultaneously implemented interventions. Intervention modeling efforts typically assume that individual adherence decisions are independent of each other.

Clinical Review of Smartphone Applications in Parkinson's Disease.

Background: Parkinson's disease (PD) is the second leading neurodegenerative disease worldwide. Important advances in monitoring and treatment have been made in recent years. This article reviews literature on utility of smartphone applications in monitoring PD symptoms that may ultimately facilitate improved patient care, and on movement modulation as a potential therapeutic.

Identifying sub-phenotypes of acute kidney injury using structured and unstructured electronic health record data with memory networks.

Acute Kidney Injury (AKI) is a common clinical syndrome characterized by the rapid loss of kidney excretory function, which aggravates the clinical severity of other diseases in a large number of hospitalized patients. Accurate early prediction of AKI can enable in-time interventions and treatments. However, AKI is highly heterogeneous, thus identification of AKI sub-phenotypes can lead to an improved understanding of the disease pathophysiology and development of more targeted clinical interventions.

Feature Selection in Predictive Modeling: A Systematic Study on Drug Response Heterogeneity for Type II Diabetic Patients.

With the rapid development of computer hardware and software technologies, more and more electronic health data from insurance claims, clinical trials and hospitals are becoming readily available. These data provide a rich resource for developing various healthcare analytics algorithms, among which predictive modeling is of key importance in many real health problems. One important issue for data-driven predictive modeling is high dimensionality, and feature selection is one effective strategy to reduce the number of independent variables and control the confounding factors.

Current state of educational compensation in academic neurology: Results of a US national survey.

In the current medical climate, medical education is at risk of being de-emphasized, leading to less financial support and compensation for faculty. A rise in compensation plans that reward clinical or research productivity fails to incentivize and threatens to erode the educational missions of our academic institutions. Aligning compensation with the all-encompassing mission of academic centers can lead to increased faculty well-being, clinical productivity, and scholarship.

Data-Driven Subtyping of Parkinson's Disease Using Longitudinal Clinical Records: A Cohort Study.

Parkinson's disease (PD) is associated with diverse clinical manifestations including motor and non-motor signs and symptoms, and emerging biomarkers. We aimed to reveal the heterogeneity of PD to define subtypes and their progression rates using an automated deep learning algorithm on the top of longitudinal clinical records. This study utilizes the data collected from the Parkinson's Progression Markers Initiative (PPMI), which is a longitudinal cohort study of patients with newly diagnosed Parkinson's disease.