Learning Using Concave and Convex Kernels: Applications in Predicting Quality of Sleep and Level of Fatigue in Fibromyalgia.

Fibromyalgia is a medical condition characterized by widespread muscle pain and tenderness and is often accompanied by fatigue and alteration in sleep, mood, and memory. Poor sleep quality and fatigue, as prominent characteristics of fibromyalgia, have a direct impact on patient behavior and quality of life. As such, the detection of extreme cases of sleep quality and fatigue level is a prerequisite for any intervention that can improve sleep quality and reduce fatigue level for people with fibromyalgia and enhance their daytime functionality. In this study, we propose a new supervised machine learning method called Learning Using Concave and Convex Kernels (LUCCK). This method employs similarity functions whose convexity or concavity can be configured so as to determine a model for each feature separately, and then uses this information to reweight the importance of each feature proportionally during classification. The data used for this study was collected from patients with fibromyalgia and consisted of blood volume pulse (BVP), 3-axis accelerometer, temperature, and electrodermal activity (EDA), recorded by an Empatica E4 wristband over the courses of several days, as well as a self-reported survey. Experiments on this dataset demonstrate that the proposed machine learning method outperforms conventional machine learning approaches in detecting extreme cases of poor sleep and fatigue in people with fibromyalgia.

Snapshot of hospital quality reporting and pay-for-performance under Medicare.

This paper examines the impact that Medicare pay-for-performance (P4P) might have upon hospital payment. It uses the initial two quarters of a national quality database to model financial gains or losses using the Premier Hospital Quality Incentive Demonstration rules, as well as the P4P approach recommended by the Medicare Payment Advisory Commission (MedPAC). Findings reveal variation among all types of hospitals and across all measures within each of the three conditions studied: heart attack, heart failure, and pneumonia. Initially, hospitals' financial gains and losses likely will be marginal using the Premier demonstration payment rules and somewhat larger under the MedPAC recommendations as modeled.