Artificial Intelligence in Medicine

OBJECTIVE: The study introduces and elaborates on a certain perspective of biomedical data analysis where data structure is revealed through fuzzy clustering. The key objective of the study is to develop a characterization of the content of the clusters by offering a number of their descriptors established on the basis of membership grades of patterns included there, as well as on the basis of their class membership. Next, a design of a cluster-based classifier is presented in which the structure of the classifier is based on a collection of clusters...

BACKGROUND: A highly sensitive real-time syndrome surveillance system is critical to detect, monitor, and control infectious disease outbreaks, such as influenza. Direct comparisons of diagnostic accuracy of various surveillance systems are scarce. OBJECTIVE: To statistically compare sensitivity and specificity of multiple proprietary and open source syndrome surveillance systems to detect influenza-like illness (ILI). METHODS: A retrospective, cross-sectional study was conducted utilizing data from 1122 patients seen during November 1–7, 2009 in the emergency department of a single urban academic medical center...

OBJECTIVE: The main purpose of this research is the novel use of artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining tool for prediction the outcome of patients with acquired brain injury (ABI) after cognitive rehabilitation. The final goal aims at increasing knowledge in the field of rehabilitation theory based on cognitive affectation. METHODS AND MATERIALS: The data set used in this study contains records belonging to 123 ABI patients with moderate to severe cognitive affectation (according to Glasgow Coma Scale) that underwent rehabilitation at Institut Guttmann Neurorehabilitation Hospital (IG) using the tele-rehabilitation platform PREVIRNEC(©)...

OBJECTIVE: To assess whether a user-centred prototype clinical decision support system (CDSS) providing patient-specific advice better supports healthcare practitioners in terms of (a) types of usability problems detected and (b) effective and efficient retrieval of childhood cancer survivor's follow-up screening procedures compared to an expert-driven paper-based guideline. METHODS AND MATERIALS: A user-centred design (UCD) process was employed to design a prototype CDSS...

OBJECTIVES: The body mass index (BMI) provides essential medical information related to body weight for the treatment and prognosis prediction of diseases such as cardiovascular disease, diabetes, and stroke. We propose a method for the prediction of normal, overweight, and obese classes based only on the combination of voice features that are associated with BMI status, independently of weight and height measurements. MATERIALS AND METHODS: A total of 1568 subjects were divided into 4 groups according to age and gender differences...

OBJECTIVE: Implement and perform pilot testing of web-based clinical decision support services using a novel framework for creating and managing clinical knowledge in a distributed fashion using the cloud. The pilot sought to (1) develop and test connectivity to an external clinical decision support (CDS) service, (2) assess the exchange of data to and knowledge from the external CDS service, and (3) capture lessons to guide expansion to more practice sites and users. MATERIALS AND METHODS: The Clinical Decision Support Consortium created a repository of shared CDS knowledge for managing hypertension, diabetes, and coronary artery disease in a community cloud hosted by Partners HealthCare...