Publications by authors named "Aline F Pedroso"
Background: Digital remote patient monitoring (RPM) enables longitudinal care outside traditional healthcare settings, especially in the vulnerable period after hospitalizations, with broad coverage of the service by payers. We sought to evaluate patterns of RPM service availability at US hospitals and the association of these services with 30-day readmissions for two key cardiovascular conditions, heart failure (HF) and acute myocardial infarction (AMI).
Methods: We used contemporary national data from the American Hospital Association (AHA) Annual Survey to ascertain US hospitals offering RPM services for post-discharge or chronic care and used census-based county-level data to define the characteristics of the communities they serve.
Background: Identifying structural heart diseases (SHDs) early can change the course of the disease, but their diagnosis requires cardiac imaging, which is limited in accessibility.
Objective: To leverage images of 12-lead ECGs for automated detection and prediction of multiple SHDs using an ensemble deep learning approach.
Methods: We developed a series of convolutional neural network models for detecting a range of individual SHDs from images of ECGs with SHDs defined by transthoracic echocardiograms (TTEs) performed within 30 days of the ECG at the Yale New Haven Hospital (YNHH).
Article Synopsis
- Rich cardiovascular data is often hidden in unstructured reports, making it difficult to use in real-time patient care and research due to manual abstraction requirements.
- A two-step process was developed using generative and interpretative large language models (LLMs) to convert these unstructured reports into usable formats, specifically focusing on transthoracic echocardiograms (TTE).
- The HeartDX-LM model demonstrated impressive accuracy, extracting 98.7% of values from unstructured reports across various datasets, proving its effectiveness in improving data accessibility for clinical analysis.
Purpose: To investigate the association between statins and muscle problems in a highly diverse sample of Brazilian civil servants.
Methods: We conducted a cross-sectional data analysis at baseline of the ELSA-Brasil MSK cohort. Pain was identified through self-reported symptoms in large muscle groups (lower back and/or hips/thighs).