Medicare

Enhanced recognition and counting of high-coverage Amorphophallus konjac by integrating UAV RGB imagery and deep learning.

Accurate counting of Amorphophallus konjac (Konjac) plants can offer valuable insights for agricultural management and yield prediction. While current studies have primarily focused on detecting and counting crop plants during the early stages of low coverage, there is limited investigation into the later stages of high coverage, which could impact the accuracy of forecasting yield. High canopy co...

Deep learning imputes DNA methylation states in single cells and enhances the detection of epigenetic alterations in schizophrenia.

DNA methylation (DNAm) is a key epigenetic mark with essential roles in gene regulation, mammalian development, and human diseases. Single-cell technologies enable profiling DNAm at cytosines in individual cells, but they often suffer from low coverage for CpG sites. We introduce scMeFormer, a transformer-based deep learning model for imputing DNAm states at each CpG site in single cells. Comprehe...

Long-tailed medical diagnosis with relation-aware representation learning and iterative classifier calibration.

Recently computer-aided diagnosis has demonstrated promising performance, effectively alleviating the workload of clinicians. However, the inherent sa...

Predicting mental health disparities using machine learning for African Americans in Southeastern Virginia.

This study examined mental health disparities among African Americans using AI and machine learning for outcome prediction. Analyzing data from Africa...

Projections of single-level indirect lumbar interbody fusion volume and associated costs for Medicare patients to 2050.

BACKGROUND: Anterior, Oblique, and Lateral Lumbar Interbody Fusions (ALIF/OLIF/LLIF) are powerful modalities for achieving indirect decompression of l...

Machine Learning Approach to Identifying Wrong-Site Surgeries Using Centers for Medicare and Medicaid Services Dataset: Development and Validation Study.

BACKGROUND: Wrong-site surgery (WSS) is a critical but preventable medical error, often resulting in severe patient harm and substantial financial cos...

Toward Machine Learning Electrospray Ionization Sensitivity Prediction for Semiquantitative Lipidomics in Stem Cells.

Specificity, sensitivity, and high metabolite coverage make mass spectrometry (MS) one of the most valuable tools in metabolomics and lipidomics. Howe...

Deep reinforcement learning for time-critical wilderness search and rescue using drones.

Traditional search and rescue methods in wilderness areas can be time-consuming and have limited coverage. Drones offer a faster and more flexible sol...

Sexual dimorphism of the humerus bones in a French sample: comparison of several statistical models including machine learning models.

Sex estimation is an important part of skeletal analysis and forensic identification. Traditionally pelvic traits are utilized for accurate sex estima...

Eeg Microstates and Balance Parameters for Stroke Discrimination: A Machine Learning Approach.

Electroencephalography microstates (EEG-MS) show promise to be a neurobiological biomarker in stroke. Thus, the aim of the study was to identify bioma...

Ecological risks of PFAS in China's surface water: A machine learning approach.

The persistence of per- and polyfluoroalkyl substances (PFAS) in surface water can pose risks to ecosystems, while due to data limitations, the occurr...

The advantages of lexicon-based sentiment analysis in an age of machine learning.

Assessing whether texts are positive or negative-sentiment analysis-has wide-ranging applications across many disciplines. Automated approaches make i...

Coverage bias in small molecule machine learning.

Small molecule machine learning aims to predict chemical, biochemical, or biological properties from molecular structures, with applications such as t...

ImpACT Project: Improving Access to Clinical Trials in Victoria, an Artificial Intelligence-Based Approach.

PURPOSE: Enhancing the speed and efficiency of clinical trial recruitment is a key objective across international health systems. This study aimed to ...

Forecasting O and NO concentrations with spatiotemporally continuous coverage in southeastern China using a Machine learning approach.

Ozone (O) is a significant contributor to air pollution and the main constituent ofphotochemical smog that plagues China. Nitrogen dioxide (NO) is a s...

Automated treatment planning with deep reinforcement learning for head-and-neck (HN) cancer intensity modulated radiation therapy (IMRT).

To develop a deep reinforcement learning (DRL) agent to self-interact with the treatment planning system to automatically generate intensity modulated...

Optimizing sequence data analysis using convolution neural network for the prediction of CNV bait positions.

BACKGROUND: Accurate prediction of copy number variations (CNVs) from targeted capture next-generation sequencing (NGS) data relies on effective norma...

Cooperative control of self-learning traffic signal and connected automated vehicles for safety and efficiency optimization at intersections.

Cooperative control of intersection signals and connected automated vehicles (CAVs) possess the potential for safety enhancement and congestion allevi...

Validating knowledge-based volumetric modulated arc therapy plans with a multi-institution model (broad model) using a complete open-loop dataset for prostate cancer.

This study examined the characteristics of the broad model (KBP) through a complete open-loop evaluation of volumetric modulated arc therapy (VMAT) pl...

Robust long-tailed recognition with distribution-aware adversarial example generation.

Confronting adversarial attacks and data imbalances, attaining adversarial robustness under long-tailed distribution presents a challenging problem. A...