Using large language models for extracting stressful life events to assess their impact on preventive colon cancer screening adherence.

Background: Increase in early onset colorectal cancer makes adherence to screening a significant public health concern, with various social determinants playing a crucial role in its incidence, diagnosis, treatment, and outcomes. Stressful life events, such as divorce, marriage, or sudden loss of job, have a unique position among the social determinants of health.

Methods: We applied a large language model (LLM) to social history sections of clinical notes in the health records database of the Medical University of South Carolina to extract recent stressful life events and assess their impact on colorectal cancer screening adherence.

Accuracy and consistency of online large language model-based artificial intelligence chat platforms in answering patients' questions about heart failure.

Background: Heart failure (HF) is a prevalent condition associated with significant morbidity. Patients may have questions that they feel embarrassed to ask or will face delays awaiting responses from their healthcare providers which may impact their health behavior. We aimed to investigate the potential of large language model (LLM) based artificial intelligence (AI) chat platforms in complementing the delivery of patient-centered care.

Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system.

Background: Referral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems.

Objective: To assess the performance and ease of implementation of Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a machine-learning model that uses structured data that is readily available in the EHR, and compare it with two commonly used risk scores: the Seattle Heart Failure Model (SHFM) and Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score.

Deep Learning for Cardiovascular Imaging: A Review.

Importance: Artificial intelligence (AI), driven by advances in deep learning (DL), has the potential to reshape the field of cardiovascular imaging (CVI). While DL for CVI is still in its infancy, research is accelerating to aid in the acquisition, processing, and/or interpretation of CVI across various modalities, with several commercial products already in clinical use. It is imperative that cardiovascular imagers are familiar with DL systems, including a basic understanding of how they work, their relative strengths compared with other automated systems, and possible pitfalls in their implementation.

DeepCOVID-Fuse: A Multi-Modality Deep Learning Model Fusing Chest X-rays and Clinical Variables to Predict COVID-19 Risk Levels.

The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, highlighting the need for accurate and timely risk prediction models that can prioritize patient care and allocate resources effectively. This study presents DeepCOVID-Fuse, a deep learning fusion model that predicts risk levels in patients with confirmed COVID-19 by combining chest radiographs (CXRs) and clinical variables. The study collected initial CXRs, clinical variables, and outcomes (i.

Could an artificial intelligence approach to prior authorization be more human?

Prior authorization (PA) may be a necessary evil within the healthcare system, contributing to physician burnout and delaying necessary care, but also allowing payers to prevent wasting resources on redundant, expensive, and/or ineffective care. PA has become an "informatics issue" with the rise of automated methods for PA review, championed in the Health Level 7 International's (HL7's) DaVinci Project. DaVinci proposes using rule-based methods to automate PA, a time-tested strategy with known limitations.

A comparative study of pretrained language models for long clinical text.

Objective: Clinical knowledge-enriched transformer models (eg, ClinicalBERT) have state-of-the-art results on clinical natural language processing (NLP) tasks. One of the core limitations of these transformer models is the substantial memory consumption due to their full self-attention mechanism, which leads to the performance degradation in long clinical texts. To overcome this, we propose to leverage long-sequence transformer models (eg, Longformer and BigBird), which extend the maximum input sequence length from 512 to 4096, to enhance the ability to model long-term dependencies in long clinical texts.

A Simple Score to Identify Increased Risk of Transthyretin Amyloid Cardiomyopathy in Heart Failure With Preserved Ejection Fraction.

Importance: Transthyretin amyloid cardiomyopathy (ATTR-CM) is a form of heart failure (HF) with preserved ejection fraction (HFpEF). Technetium Tc 99m pyrophosphate scintigraphy (PYP) enables ATTR-CM diagnosis. It is unclear which patients with HFpEF have sufficient risk of ATTR-CM to warrant PYP.

Hyponatremia Is a Powerful Predictor of Poor Prognosis in Left Ventricular Assist Device Patients.

Serum sodium is an established prognostic marker in heart failure (HF) patients and is associated with an increased risk of morbidity and mortality. We sought to study the prognostic value of serum sodium in left ventricular assist device (LVAD) patients and whether hyponatremia reflects worsening HF or an alternative mechanism. We identified HF patients that underwent LVAD implantation between 2008 and 2019.

Advances in Machine Learning Approaches to Heart Failure with Preserved Ejection Fraction.

Heart failure with preserved ejection fraction (HFpEF) represents a prototypical cardiovascular condition in which machine learning may improve targeted therapies and mechanistic understanding of pathogenesis. Machine learning, which involves algorithms that learn from data, has the potential to guide precision medicine approaches for complex clinical syndromes such as HFpEF. It is therefore important to understand the potential utility and common pitfalls of machine learning so that it can be applied and interpreted appropriately.

DeepCOVID-XR: An Artificial Intelligence Algorithm to Detect COVID-19 on Chest Radiographs Trained and Tested on a Large U.S. Clinical Data Set.

Background There are characteristic findings of coronavirus disease 2019 (COVID-19) on chest images. An artificial intelligence (AI) algorithm to detect COVID-19 on chest radiographs might be useful for triage or infection control within a hospital setting, but prior reports have been limited by small data sets, poor data quality, or both. Purpose To present DeepCOVID-XR, a deep learning AI algorithm to detect COVID-19 on chest radiographs, that was trained and tested on a large clinical data set.

Predicting High-Risk Patients and High-Risk Outcomes in Heart Failure.

Identifying patients with heart failure at high risk for poor outcomes is important for patient care, resource allocation, and process improvement. Although numerous risk models exist to predict mortality, hospitalization, and patient-reported health status, they are infrequently used for several reasons, including modest performance, lack of evidence to support routine clinical use, and barriers to implementation. Artificial intelligence has the potential to enhance the performance of risk prediction models, but has its own limitations and remains unproved.

A Left Atrial Thrombus Mimic: Value of Ultrasound Enhancing Agents during Transesophageal Echocardiography.

• Fat in the transverse pericardial sinus can mimic thrombus on TEE. • Epicardial fat is more common in patients with AF. • UEAs can differentiate extracardiac from intracardiac structures.

Cases from a busy nuclear cardiology laboratory: Potential pitfalls in the interpretation of cardiac scintigraphy for ATTR cardiac amyloidosis.

Cardiac scintigraphy has emerged as a key diagnostic test for transthyretin cardiac amyloidosis (ATTR-CA). However, there are potential limitations and pitfalls in the interpretation of cardiac scintigraphy for ATTR-CA that are worth noting. We present here a series of three cases which illustrate some of these important principles.

Targeting the Correct Population When Designing Transitional Care Programs for Medicare Patients Hospitalized With Heart Failure.

This study uses data from a large academic medical center to estimate the accuracy of the criteria used by Medicare to represent the burden of hospitalizations for heart failure.

When to tell and test for genetic carrier status: perspectives of adolescents and young adults from fragile X families.

We report here on our findings from adolescent and young adult females (ages 14-25) with a family history of fragile X syndrome regarding their perceptions of the optimal ages for (1) learning fragile X is inherited, (2) learning one could be a carrier for fragile X, and (3) offering carrier testing for fragile X. Three groups were enrolled: those who knew they were carriers or noncarriers and those who knew only they were at-risk to be a carrier. Only 2 of the 53 participants felt that offering carrier testing should be delayed until the age of 18 years.