Danger, Danger, Gaston Labat! Does zero-shot artificial intelligence correlate with anticoagulation guidelines recommendations for neuraxial anesthesia?

Introduction: Artificial intelligence and large language models (LLMs) have emerged as potentially disruptive technologies in healthcare. In this study GPT-3.5, an accessible LLM, was assessed for its accuracy and reliability in performing guideline-based evaluation of neuraxial bleeding risk in hypothetical patients on anticoagulation medication.

Clinical Phenotyping with an Outcomes-driven Mixture of Experts for Patient Matching and Risk Estimation.

Observational medical data present unique opportunities for analysis of medical outcomes and treatment decision making. However, because these datasets do not contain the strict pairing of randomized control trials, matching techniques are to draw comparisons among patients. A key limitation to such techniques is verification that the variables used to model treatment decision making are also relevant in identifying the risk of major adverse events.

Would doctors dream of electric blood bankers? Large language model-based artificial intelligence performs well in many aspects of transfusion medicine.

Background: Large language models (LLMs) excel at answering knowledge-based questions. Many aspects of blood banking and transfusion medicine involve no direct patient care and require only knowledge and judgment. We hypothesized that public LLMs could perform such tasks with accuracy and precision.

A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders.

Cardiovascular disorders cause nearly one in three deaths in the United States. Short- and long-term care for these disorders is often determined in short-term settings. However, these decisions are made with minimal longitudinal and long-term data.

Clinical characteristics and outcomes for 7,995 patients with SARS-CoV-2 infection.

Objective: Severe acute respiratory syndrome virus (SARS-CoV-2) has infected millions of people worldwide. Our goal was to identify risk factors associated with admission and disease severity in patients with SARS-CoV-2.

Design: This was an observational, retrospective study based on real-world data for 7,995 patients with SARS-CoV-2 from a clinical data repository.

Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction.

Importance: Accurate prediction of adverse outcomes after acute myocardial infarction (AMI) can guide the triage of care services and shared decision-making, and novel methods hold promise for using existing data to generate additional insights.

Objective: To evaluate whether contemporary machine learning methods can facilitate risk prediction by including a larger number of variables and identifying complex relationships between predictors and outcomes.

Design, Setting, And Participants: This cohort study used the American College of Cardiology Chest Pain-MI Registry to identify all AMI hospitalizations between January 1, 2011, and December 31, 2016.

Developing Personalized Models of Blood Pressure Estimation from Wearable Sensors Data Using Minimally-trained Domain Adversarial Neural Networks.

Blood pressure monitoring is an essential component of hypertension management and in the prediction of associated comorbidities. Blood pressure is a dynamic vital sign with frequent changes throughout a given day. Capturing blood pressure remotely and frequently (also known as ambulatory blood pressure monitoring) has traditionally been achieved by measuring blood pressure at discrete intervals using an inflatable cuff.

Clinical Characteristics and Outcomes for 7,995 Patients with SARS-CoV-2 Infection.

Objective: Severe acute respiratory syndrome virus (SARS-CoV-2) has infected millions of people worldwide. Our goal was to identify risk factors associated with admission and disease severity in patients with SARS-CoV-2.

Design: This was an observational, retrospective study based on real-world data for 7,995 patients with SARS-CoV-2 from a clinical data repository.

Sparse Embedding for Interpretable Hospital Admission Prediction.

This paper introduces a sparse embedding for electronic health record (EHR) data in order to predict hospital admission. We use a k-sparse autoencoder to embed the original registry data into a much lower dimension, with sparsity as a goal. Then, t-SNE is used to show the embedding of each patient's data in a 2D plot.