Machine Learning-Based Identification of Obesity from Positive and Unlabelled Electronic Health Records.

Introduction: Prevalence of overweight and obesity are increas- ing in the last decades, and with them, diseases and health conditions such as diabetes, hypertension or cardiovascular diseases. However, hos- pital databases usually do not record such conditions in adults, neither anthropomorfic measures that facilitate their identification.

Methods: We implemented a machine learning method based on PU (Positive and Unlabelled) Learning to identify obese patients without a diagnose code of obesity in the health records.

All-optical decrementing of a packet's time-to-live (TTL) field using logic XOR gates.

A novel optical time-to-live (TTL) method based on the use of logic XOR gates is proposed. All-optical decrementing of a packet's TTL field is demonstrated employing a cascade of two SOA-based Mach-Zehnder interferometers. The principle of operation is validated by means of simulations, showing a good extinction ratio (higher than 10 dB) at the output of the 1-bit subtractor.