Early Detection of Lung Cancer Using a Convolutional Neural Network Integrating Multidimensional Clinical Information.

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, primarily due to late-stage detection. Early diagnosis significantly improves survival rates, yet current screening methods face limitations in cost, accessibility, and predictive accuracy. This study leverages the comprehensive data from Taiwan's National Health Insurance Research Database (NHIRD) to develop a predictive model for early lung cancer detection.

Web-based artificial intelligence to predict cognitive impairment following stroke: A multicenter study.

Background And Purpose: Post-stroke cognitive impairment (PSCI) is highly prevalent in modern society. However, there is limited study implying an accurate and explainable machine learning model to predict PSCI. The aim of this study is to develop and validate a web-based artificial intelligence (AI) tool for predicting PSCI.

Facial Mimicry and Doctor-Patient Satisfaction: The Feasibility of Artificial Empathy in a Clinical Video Data.

Good nonverbal communication between doctor and patient is essential for achieving a successful and therapeutic doctor-patient relationship. Increasing evidence has shown that nonverbal communication mimicry, particularly facial mimicry, where one mirrors another's facial expressions, is linked to empathy and emotion recognition. Empathy is also the key driver of patient satisfaction.

Artificial Intelligence Approach for Severe Dengue Early Warning System.

Dengue fever is a viral infectious disease transmitted through mosquito bites, and has symptoms ranging from mild flu-like symptoms to deadly complications. Dengue fever is one of the global burden diseases which annually have 50-100 million cases with 500,000 cases of severe dengue fever, of which 22,000 deaths occur mostly in children. Despite the discovery of vaccines, vector control is still the main approach for prevention efforts.

Machine Learning Approaches for Predicting Psoriatic Arthritis Risk Using Electronic Medical Records: Population-Based Study.

Background: Psoriasis (PsO) is a chronic, systemic, immune-mediated disease with multiorgan involvement. Psoriatic arthritis (PsA) is an inflammatory arthritis that is present in 6%-42% of patients with PsO. Approximately 15% of patients with PsO have undiagnosed PsA.

Post-Stroke Insomnia Increased the Risk of Cognitive Impairments: A Hospital-Based Retrospective Cohort Study.

Objectives/background: Insomnia is a common sleep complaint among patients who had a stroke and has been recognized as an independent risk factor for cognitive impairment. However, the relationship between poststroke insomnia and cognitive impairment over time is under-researched. Therefore, we examined the association between poststroke insomnia and the risk of cognitive impairment.

SlimMe, a Chatbot With Artificial Empathy for Personal Weight Management: System Design and Finding.

As the obesity rate continues to increase persistently, there is an urgent need to develop an effective weight loss management strategy. Nowadays, the development of artificial intelligence (AI) and cognitive technologies coupled with the rapid spread of messaging platforms and mobile technology with easier access to internet technology offers professional dietitians an opportunity to provide extensive monitoring support to their clients through a chatbot with artificial empathy. This study aimed to design a chatbot with artificial empathic motivational support for weight loss called "SlimMe" and investigate how people react to a diet bot.

Machine Learning Approach to Reduce Alert Fatigue Using a Disease Medication-Related Clinical Decision Support System: Model Development and Validation.

Background: Computerized physician order entry (CPOE) systems are incorporated into clinical decision support systems (CDSSs) to reduce medication errors and improve patient safety. Automatic alerts generated from CDSSs can directly assist physicians in making useful clinical decisions and can help shape prescribing behavior. Multiple studies reported that approximately 90%-96% of alerts are overridden by physicians, which raises questions about the effectiveness of CDSSs.