Primary Care

Microfluidics guided by deep learning for cancer immunotherapy screening.

Immunocyte infiltration and cytotoxicity play critical roles in both inflammation and immunotherapy. However, current cancer immunotherapy screening methods overlook the capacity of the T cells to penetrate the tumor stroma, thereby significantly limiting the development of effective treatments for solid tumors. Here, we present an automated high-throughput microfluidic platform for simultaneous t...

A multi-granularity convolutional neural network model with temporal information and attention mechanism for efficient diabetes medical cost prediction.

As the cost of diabetes treatment continues to grow, it is critical to accurately predict the medical costs of diabetes. Most medical cost studies based on convolutional neural networks (CNNs) ignore the importance of multi-granularity information of medical concepts and time interval characteristics of patients' multiple visit sequences, which reflect the frequency of patient visits and the sever...

Predicting risk of sepsis, comparison between machine learning methods: a case study of a Virginia hospital.

Sepsis is an inflammation caused by the body's systemic response to an infection. The infection could be a result of many diseases, such as pneumonia,...

An automated unsupervised deep learning-based approach for diabetic retinopathy detection.

As per the International Diabetes Federation (IDF) report, 35-60% of people suffering from diabetic retinopathy (DR) have a history of diabetes. DR is...

An Approach for Fall Prediction Based on Kinematics of Body Key Points Using LSTM.

Many studies have used sensors attached to adults in order to collect signals by which one can carry out analyses to predict falls. In addition, there...

Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans.

Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The earl...

The application of artificial intelligence to support biliary atresia screening by ultrasound images: A study based on deep learning models.

PURPOSE: Early confirmation or ruling out biliary atresia (BA) is essential for infants with delayed onset of jaundice. In the current practice, percu...

Towards Online Ageing Detection in Transformer Oil: A Review.

Transformers play an essential role in power networks, ensuring that generated power gets to consumers at the safest voltage level. However, they are ...

Validation of Combined Deep Learning Triaging and Computer-Aided Diagnosis in 2901 Breast MRI Examinations From the Second Screening Round of the Dense Tissue and Early Breast Neoplasm Screening Trial.

OBJECTIVES: Computer-aided triaging (CAT) and computer-aided diagnosis (CAD) of screening breast magnetic resonance imaging have shown potential to re...

Identifying the presence and severity of dementia by applying interpretable machine learning techniques on structured clinical records.

BACKGROUND: Dementia develops as cognitive abilities deteriorate, and early detection is critical for effective preventive interventions. However, mai...

Advances in Deep Learning for Tuberculosis Screening using Chest X-rays: The Last 5 Years Review.

There has been an explosive growth in research over the last decade exploring machine learning techniques for analyzing chest X-ray (CXR) images for s...

Comprehensive analysis of epigenomics and transcriptome data to identify potential target genes associated with obesity.

DNA methylation is closely related to the occurrence and development of many diseases, but its role in obesity is still unclear. This study aimed to f...

Predicting risk of obesity and meal planning to reduce the obese in adulthood using artificial intelligence.

BACKGROUND: An unhealthy diet or excessive amount of food intake creates obesity issues in human beings that further may cause several diseases such a...

Analysis of Characteristic Factors of Nursing Safety Incidents in ENT Surgery by Deep Learning-Based Medical Data Association Rules Method.

It is of great significance to explore the characteristic factors of postoperative nursing safety events in patients with otolaryngology surgery and t...

Predictive Analysis of Diabetes-Risk with Class Imbalance.

Diabetes type 2 (T2DM) is a common chronic disease, increasingly leading to many complications and affecting vital organs. Hyperglycemia is the main c...

Delirium screening in an acute care setting with a machine learning classifier based on routinely collected nursing data: A model development study.

Delirium screening in acute care settings is a resource intensive process with frequent deviations from screening protocols. A predictive model relyin...

From promise to practice: towards the realisation of AI-informed mental health care.

In this Series paper, we explore the promises and challenges of artificial intelligence (AI)-based precision medicine tools in mental health care from...

Machine learning-based prediction of disability risk in geriatric patients with hypertension for different time intervals.

BACKGROUND: The risk of disability in older adults with hypertension is substantially high, and prediction of disability risk is crucial for subsequen...

Longitudinal deep learning clustering of Type 2 Diabetes Mellitus trajectories using routinely collected health records.

Type 2 diabetes mellitus (T2DM) is a highly heterogeneous chronic disease with different pathophysiological and genetic characteristics affecting its ...

Prevalence and Early Prediction of Diabetes Using Machine Learning in North Kashmir: A Case Study of District Bandipora.

Diabetes is one of the biggest health problems that affect millions of people across the world. Uncontrolled diabetes can increase the risk of heart a...