US Health Policy

Application of Deep Learning to Predict Standardized Uptake Value Ratio and Amyloid Status on F-Florbetapir PET Using ADNI Data.

BACKGROUND AND PURPOSE: Cortical amyloid quantification on PET by using the standardized uptake valu...

Supervised mixture of experts models for population health.

We propose a machine learning driven approach to derive insights from observational healthcare data ...

Database Mining of Genes of Prognostic Value for the Prostate Adenocarcinoma Microenvironment Using the Cancer Gene Atlas.

BACKGROUND: Prostate adenocarcinoma (PRAD) is a common malignant tumor in elderly men. Our research ...

Improvement of electrocardiographic diagnostic accuracy of left ventricular hypertrophy using a Machine Learning approach.

The electrocardiogram (ECG) is the most common tool used to predict left ventricular hypertrophy (LV...

Denoising of multi b-value diffusion-weighted MR images using deep image prior.

The clinical value of multiple b-value diffusion-weighted (DW) magnetic resonance imaging (MRI) has ...

Analysis of Drug Effects on iPSC Cardiomyocytes with Machine Learning.

Patient-specific induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) offer an attractive...

Comparison of statistical and machine learning models for healthcare cost data: a simulation study motivated by Oncology Care Model (OCM) data.

BACKGROUND: The Oncology Care Model (OCM) was developed as a payment model to encourage participatin...

Enhancing Top-Down Proteomics Data Analysis by Combining Deconvolution Results through a Machine Learning Strategy.

Top-down mass spectrometry (MS) is a powerful tool for the identification and comprehensive characte...

Machine-learning-based quality control of contractility of cultured human-induced pluripotent stem-cell-derived cardiomyocytes.

The precise and early assessment of cardiotoxicity is fundamental to bring forward novel drug candid...

Developing an Interactive Environment Through the Teaching of Mathematics with Small Robots.

The article is the product of the study "Development of innovative resources to improve logical-math...

Machine learning and the future of Medicare fraud detection.

Value of a Machine Learning Approach for Predicting Clinical Outcomes in Young Patients With Hypertension.

Risk stratification of young patients with hypertension remains challenging. Generally, machine lear...

Artificial intelligence and radiomics enhance the positive predictive value of digital chest tomosynthesis for lung cancer detection within SOS clinical trial.

OBJECTIVE: To enhance the positive predictive value (PPV) of chest digital tomosynthesis (DTS) in th...

Lessons Learned About Autonomous AI: Finding a Safe, Efficacious, and Ethical Path Through the Development Process.

Artificial intelligence (AI) describes systems capable of making decisions of high cognitive complex...

Noninvasive Diagnosis of Nonalcoholic Fatty Liver Disease and Quantification of Liver Fat with Radiofrequency Ultrasound Data Using One-dimensional Convolutional Neural Networks.

Background Radiofrequency ultrasound data from the liver contain rich information about liver micros...

Separation of HCM and LQT Cardiac Diseases with Machine Learning of Ca2+ Transient Profiles.

BACKGROUND:  Modeling human cardiac diseases with induced pluripotent stem cells not only enables to...

Natural language processing of clinical mental health notes may add predictive value to existing suicide risk models.

BACKGROUND: This study evaluated whether natural language processing (NLP) of psychotherapy note tex...

[The value of artificial and human intelligence - the example of bone scintigraphy].

We present a possible method of Artificial Intelligence (AI) based applications that can effectively...

Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer.

INTRODUCTION: The aim of the study was to extract anthropometric measures from CT by deep learning a...

Machine Learning Algorithms for Predicting the Recurrence of Stage IV Colorectal Cancer After Tumor Resection.

The aim of this study is to explore the feasibility of using machine learning (ML) technology to pre...

Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning.

: The primary care service in Catalonia has operated an asynchronous teleconsulting service between ...