AIMC Topic: Follow-Up Studies

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A neural network model for predicting the effectiveness of treatment in patients with neovascular glaucoma associated with diabetes mellitus.

Romanian journal of ophthalmology
INTRODUCTION: The study hypothesizes that neural networks can be an effective tool for predicting treatment outcomes in patients with diabetic neovascular glaucoma (NVG), considering not only baseline intraocular pressure (IOP) values but also inflam...

Deep Learning for Automated Triaging of Stable Chest Radiographs in a Follow-up Setting.

Radiology
Background Most artificial intelligence algorithms that interpret chest radiographs are restricted to an image from a single time point. However, in clinical practice, multiple radiographs are used for longitudinal follow-up, especially in intensive ...

Using a Social Robot to Engage Older Adults Living in Residential Care Homes in Cognitive Training: Preliminary Results from the SHAPES Project.

Studies in health technology and informatics
The employment of socially assistive robotics (SAR) is increasingly being considered a credible solution to support healthcare systems in dealing with an aging society. In this contribution, we explore the experience of older adults (n = 11) living i...

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

Studies in health technology and informatics
Cancer screening and timely follow-up of abnormal results can reduce mortality. One barrier to follow-up is the failure to identify abnormal results. While EHRs have coded results for certain tests, cancer screening results are often stored in free-t...

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.

Diabetes care
OBJECTIVE: Reportedly, two-thirds of the patients who were positive for diabetes during screening failed to attend a follow-up visit for diabetes care in Japan. We aimed to develop a machine-learning model for predicting people's failure to attend a ...

Robot-Assisted Laparoscopic Distal Ureteroureterostomy for Distal Benign Ureteral Strictures with Long-Term Follow-Up.

Journal of endourology
To demonstrate feasibility of robot-assisted laparoscopic (RAL) ureteroureterostomy (UU) for benign distal ureteral strictures (DUS) in our robotic reconstruction series with long-term follow-up. In a retrospective review of our prospectively maint...

Deep learning-based prediction of heart failure rehospitalization during 6, 12, 24-month follow-ups in patients with acute myocardial infarction.

Health informatics journal
Heart failure is a clinical syndrome that occurs when the heart is too weak or stiff and cannot pump enough blood that our body needs. It is one of the most expensive diseases due to frequent hospitalizations and emergency room visits. Reducing unnec...