Frequency and Outcomes of BI-RADS Category 3 Assessments in Patients With a Personal History of Breast Cancer: Full-Field Digital Mammography Versus Digital Breast Tomosynthesis.

Studies establishing the validity of BI-RADS category 3 excluded patients with personal history of breast cancer (PHBC). Use of category 3 in patients with PHBC may be impacted not only by this population's increased breast cancer risk, but also by adoption of digital breast tomosynthesis (DBT) over full-field digital mammography (FFDM). The purpose of this article was to compare the frequency, outcomes, and additional characteristics of BI-RADS category 3 assessments between FFDM and DBT in patients with PHBC.

Use of machine-learning algorithms to determine features of systolic blood pressure variability that predict poor outcomes in hypertensive patients.

Background: We re-analyzed data from the Systolic Blood Pressure Intervention Trial (SPRINT) trial to identify features of systolic blood pressure (SBP) variability that portend poor cardiovascular outcomes using a nonlinear machine-learning algorithm.

Methods: We included all patients who completed 1 year of the study without reaching any primary endpoint during the first year, specifically: myocardial infarction, other acute coronary syndromes, stroke, heart failure or death from a cardiovascular event ( = 8799; 94%). In addition to clinical variables, features representing longitudinal SBP trends and variability were determined and combined in a random forest algorithm, optimized using cross-validation, using 70% of patients in the training set.

Automatic analysis of medical dialogue in the home hemodialysis domain: structure induction and summarization.

Spoken medical dialogue is a valuable source of information for patients and caregivers. This work presents a first step towards automatic analysis and summarization of spoken medical dialogue. We first abstract a dialogue into a sequence of semantic categories using linguistic and contextual features integrated in a supervised machine-learning framework.