Hospitalists

Generative Artificial Intelligence to Transform Inpatient Discharge Summaries to Patient-Friendly Language and Format.

IMPORTANCE: By law, patients have immediate access to discharge notes in their medical records. Technical language and abbreviations make notes difficult to read and understand for a typical patient. Large language models (LLMs [eg, GPT-4]) have the potential to transform these notes into patient-friendly language and format.

Utilizing imaging parameters for functional outcome prediction in acute ischemic stroke: A machine learning study.

BACKGROUND AND PURPOSE: We aimed to predict the functional outcome of acute ischemic stroke patients with anterior circulation large vessel occlusions (LVOs), irrespective of how they were treated or the severity of the stroke at admission, by only using imaging parameters in machine learning models.

A comparative study of CNN-capsule-net, CNN-transformer encoder, and Traditional machine learning algorithms to classify epileptic seizure.

INTRODUCTION: Epilepsy is a disease characterized by an excessive discharge in neurons generally provoked without any external stimulus, known as conv...

Study of medium and long-term free flow capacity and queue discharge rates on roads.

With the rise in vehicle ownership, traffic congestion has emerged as a major barrier to urban progress, making the study and optimization of urban ro...

Classification of Action Potentials With High Variability Using Convolutional Neural Network for Motor Unit Tracking.

The reliable classification of motor unit action potentials (MUAPs) provides the possibility of tracking motor unit (MU) activities. However, the vari...

Rapid classification of coffee origin by combining mass spectrometry analysis of coffee aroma with deep learning.

Mislabeling the geographical origin of coffee is a prevalent form of fraud. In this study, a rapid, nondestructive, and high-throughput method combini...

Machine learning decision support model for discharge planning in stroke patients.

BACKGROUND/AIM: Efficient discharge for stroke patients is crucial but challenging. The study aimed to develop early predictive models to explore whic...

Integration of deep learning and improved multi-objective algorithm to optimize reservoir operation for balancing human and downstream ecological needs.

Dam (reservoir)-induced alterations of flow and water temperature regimes can threaten downstream fish habitats and native aquatic ecosystems. Allevia...

Single-Port Multiport Robot-Assisted Partial Nephrectomy: A Meta-Analysis.

Several centers have reported their experience with single-port robot-assisted partial nephrectomy (SP-RAPN); however, it is uncertain if utilization...

Comparing the Quality of Domain-Specific Versus General Language Models for Artificial Intelligence-Generated Differential Diagnoses in PICU Patients.

OBJECTIVES: Generative language models (LMs) are being evaluated in a variety of tasks in healthcare, but pediatric critical care studies are scant. O...

The Intersections of COVID-19, HIV, and Race/Ethnicity: Machine Learning Methods to Identify and Model Risk Factors for Severe COVID-19 in a Large U.S. National Dataset.

We investigate risk factors for severe COVID-19 in persons living with HIV (PWH), including among racialized PWH, using the U.S. population-sampled Na...

Use of Temporally Validated Machine Learning Models To Predict Outcomes of Percutaneous Nephrolithotomy Using Data from the British Association of Urological Surgeons Percutaneous Nephrolithotomy Audit.

BACKGROUND AND OBJECTIVE: Machine learning (ML) is a subset of artificial intelligence that uses data to build algorithms to predict specific outcomes...

Application of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variables.

Identifying patients who may develop severe COVID-19 has been of interest to clinical physicians since it facilitates personalized treatment and optim...

Building Prediction Models for 30-Day Readmissions Among ICU Patients Using Both Structured and Unstructured Data in Electronic Health Records.

ICU readmissions are associated with poor outcomes for patients and poor performance of hospitals. Patients who are readmitted have an increased risk ...

Patient satisfaction analysis of robot-assisted minimally invasive adrenalectomy: a single-center retrospective study.

The objective of this study is to compare the satisfaction of patients undergoing robot-assisted retroperitoneal laparoscopy adrenalectomy under the a...

Evaluation of robot-assisted laparoscopic versus conventional laparoscopic hiatal hernia repair in children.

Laparoscopic approach is the gold standard for repairing hiatal hernia (HH). Robotic technology is now widely accepted, but it is rarely used in child...

Incidence, Determinants, and Outcome of Contrast-induced Acute Kidney Injury following Percutaneous Coronary Intervention at a Tertiary Care Hospital.

Contrast-induced acute kidney injury (CI-AKI) after percutaneous coronary intervention (PCI) is the common cause of in-hospital acquired AKI and is as...

Factors affecting the efficiency of walking independence in patients with subacute stroke following robot-assisted gait training with conventional rehabilitation.

Factors affecting the efficiency of walking independence in patients with subacute stroke following robot-assisted gait training (RAGT) and convention...

The clinical course of hospitalized COVID-19 patients and aggravation risk prediction models: a retrospective, multi-center Korean cohort study.

BACKGROUND: Understanding the clinical course and pivotal time points of COVID-19 aggravation is critical for enhancing patient monitoring. This retro...

Prediction of patient admission and readmission in adults from a Colombian cohort with bipolar disorder using artificial intelligence.

INTRODUCTION: Bipolar disorder (BD) is a chronically progressive mental condition, associated with a reduced quality of life and greater disability. P...