Design of a bi-directional methodology for automated assessment of compliance to continuous application of clinical guidelines, and its evaluation in the type 2 diabetes domain.

We introduce a new approach for automated guideline-based-care quality assessment, the bidirectional knowledge-based assessment of compliance (BiKBAC) method, and the DiscovErr system, which implements it. Our methodology compares the guideline's Asbru-based formal representation, including its intentions, with the longitudinal medical record, using a top-down and bottom-up approach. Partial matches are resolved using fuzzy temporal logic.

A community-of-practice-based evaluation methodology for knowledge intensive computational methods and its application to multimorbidity decision support.

Objective: The study has dual objectives. Our first objective (1) is to develop a community-of-practice-based evaluation methodology for knowledge-intensive computational methods. We target a whitebox analysis of the computational methods to gain insight on their functional features and inner workings.

Do patients read emails from their physician containing tips on improving lifestyle habits? A pilot study.

Introduction: Healthcare providers and organizations occasionally use electronic messages to provide information to patients. There is insufficient data on whether patients actually read the emails they receive. In this study, we aimed to assess the cooperation of patients in reading multiple information pages sent over 6 months from their diabetologist via email.

A Large-Scale Observational Analysis of Social Media Data Reveals Major Public Misperception of the Attainability of Drastic Weight Loss by Dieting.

Introduction: Diet forums in social media websites provide an opportunity to glimpse the experience of different weight loss diet strategies reported by tens of thousands of individuals.

Methods: We analyzed all postings with weight information from the six major Reddit weight loss diet forums ("subreddits") as reported by forum participants.

Results: Data were collected from January 2011 to April 2020 from all 55,900 users posting weight information.

Electroencephalography functional connectivity-A biomarker for painful polyneuropathy.

Background And Purpose: Advanced analysis of electroencephalography (EEG) data has become an essential tool in brain research. Based solely on resting state EEG signals, a data-driven, predictive and explanatory approach is presented to discriminate painful from non-painful diabetic polyneuropathy (DPN) patients.

Methods: Three minutes long, 64 electrode resting-state recordings were obtained from 180 DPN patients.