Primary Care

Global diabetes burden: analysis of regional differences to improve diabetes care.

INTRODUCTION: The current evaluation processes of the burden of diabetes are incomplete and subject to bias. This study aimed to identify regional differences in the diabetes burden on a universal level from the perspective of people with diabetes.

Clinical significance of hepatic function in Graves disease with type 2 diabetic mellitus: A single-center retrospective cross-sectional study in Taiwan.

Graves disease (GD) and type 2 diabetes mellitus (T2DM) both impair liver function; we therefore explored the possibility of a relationship among diabetic control, thyroid function, and liver function. This retrospective, cross-sectional study compared serum liver function biomarkers of primary GD patients in a single center between 2016 and 2020, derived from clinical databases, and clarified the...

Machine fault detection methods based on machine learning algorithms: A review.

Preventive identification of mechanical parts failures has always played a crucial role in machine maintenance. Over time, as the processing cycles ar...

Introducing Artificial Intelligence into the Preventive Medicine Visit.

[Comparison of the predictive performance of Logistic regression, BP neural network and support vector machine model for the risk of acute exacerbation of readmission in elderly patients with chronic obstructive pulmonary disease within 30 days].

OBJECTIVE: To compare the effectiveness of Logistic regression, BP neural network and support vector machine models in the prediction of 30-day risk o...

Glucose trajectory prediction by deep learning for personal home care of type 2 diabetes mellitus: modelling and applying.

Glucose management for people with type 2 diabetes mellitus is essential but challenging due to the multi-factored and chronic disease nature of diabe...

Identifying daily activities of patient work for type 2 diabetes and co-morbidities: a deep learning and wearable camera approach.

OBJECTIVE: People are increasingly encouraged to self-manage their chronic conditions; however, many struggle to practise it effectively. Most studies...

A Generic Semi-Supervised and Active Learning Framework for Biomedical Text Classification.

Biomedical text classification requires having training examples labeled by clinical specialists, a process that can be costly. To address this proble...

A novel multi-view deep learning approach for BI-RADS and density assessment of mammograms.

Advanced deep learning (DL) algorithms may predict the patient's risk of developing breast cancer based on the Breast Imaging Reporting and Data Syste...

Normal and Abnormal Classification of Electrocardiogram: A Primary Screening Tool Kit.

Cardiovascular diseases (CVDs) are one of the principal causes of death. Cardiac arrhythmia, a critical CVD, can be easily detected from an electrocar...

Automatic Wound Type Classification with Convolutional Neural Networks.

Chronic wounds are ulcerations of the skin that fail to heal because of an underlying condition such as diabetes mellitus or venous insufficiency. The...

Hospital Readmission Prediction via Keyword Extraction and Sentiment Analysis on Clinical Notes.

Unplanned hospital readmission is a problem that affects hospitals worldwide and is due to different factors. The identification of those factors can ...

Early Diabetes Prediction: A Comparative Study Using Machine Learning Techniques.

Most screening tests for Diabetes Mellitus (DM) in use today were developed using electronically collected data from Electronic Health Record (EHR). H...

Natural Language Processing to Identify Abnormal Breast, Lung, and Cervical Cancer Screening Test Results from Unstructured Reports to Support Timely Follow-up.

Cancer screening and timely follow-up of abnormal results can reduce mortality. One barrier to follow-up is the failure to identify abnormal results. ...

A Machine Learning-Based Predictive Model to Identify Patients Who Failed to Attend a Follow-up Visit for Diabetes Care After Recommendations From a National Screening Program.

OBJECTIVE: Reportedly, two-thirds of the patients who were positive for diabetes during screening failed to attend a follow-up visit for diabetes care...

Scaling AI Projects for Radiology - Causes and Consequences.

Artificial intelligence (AI) for radiology has the potential to handle an ever-increasing volume of imaging examinations. However, the implementation ...

Characterization of Type 2 Diabetes Using Counterfactuals and Explainable AI.

Type 2 diabetes mellitus is a metabolic disorder of glucose management, whose prevalence is increasing inexorably worldwide. Adherence to therapies, a...

D3AI-CoV: a deep learning platform for predicting drug targets and for virtual screening against COVID-19.

Target prediction and virtual screening are two powerful tools of computer-aided drug design. Target identification is of great significance for hit d...

Influence of Robot-Assisted Partial Nephrectomy on Long-Term Renal Function as Assessed Using 99m-Tc DTPA Renal Scintigraphy.

The long-term split renal function after robot-assisted partial nephrectomy (RAPN) is yet to be elucidated. This study aimed to assess long-term rena...

Systematic TB screening using WHO radiograph categorisation and care outcomes.

An appropriate screening approach and quality care are crucial for TB programmes in prisons. This study assessed crude TB prevalence, accuracy of the...