AIMC Topic: Middle Aged

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Raman spectroscopy and machine learning for early detection of gastric cancer and Helicobacter pylori with gastric juice.

Scientific reports
Gastric cancer is a leading cause of cancer-related mortality and highlights the need for early detection of gastric cancer and Helicobacter pylori (HP) infection, which is a major risk factor. Early non-invasive and convenient diagnostic tools capab...

Burden and risk factors of depression in seniors from 1990 to 2021: a multi-database study based on EMR mining methods.

Translational psychiatry
Depression in seniors is a growing public health concern worldwide. Despite the rising prevalence of depression in this demographic, comprehensive data on its burden and trends over an extended period remain limited. This study aims to assess the tre...

Rapid discrimination of and non-tuberculous mycobacteria disease via interpretive machine learning analysis of routine laboratory tests.

BMJ health & care informatics
OBJECTIVES: Rapid discrimination of infections caused by (MTB) and non-tuberculous mycobacteria (NTM) is crucial in clinical settings. Despite overlapping clinical and radiological features, the two require markedly different therapeutic approaches ...

Serial 12-Lead Electrocardiogram-Based Deep-Learning Model for Hospital Admission Prediction in Emergency Department Cardiac Presentations: Retrospective Cohort Study.

JMIR cardio
BACKGROUND: Emergency department (ED) crowding is often attributed to a slow hospitalization process, leading to reduced quality of care. Predicting early disposition in patients presenting with cardiac issues is challenging: most are ultimately disc...

Automated Esophageal Cancer Staging From Free-Text Radiology Reports: Large Language Model Evaluation Study.

JMIR medical informatics
BACKGROUND: Accurate staging of esophageal cancer is crucial for determining prognosis and guiding treatment strategies, but manual interpretation of radiology reports by clinicians is prone to variability and limited accuracy, resulting in reduced s...

Use of Artificial Intelligence-Assisted Conversational Agents to Improve Patient Experience Related to Physicians: Cross-Sectional Study in China.

Journal of medical Internet research
BACKGROUND: Artificial intelligence-assisted conversational agents have been applied and developed in outpatient departments to improve health services in China. However, there has been little research that evaluates the effect of artificial intellig...

Human-Delivered Conversation Versus AI Chatbot Conversation in Increasing Heart Attack Knowledge in Women in the United States: Quasi-Experimental Studies.

Journal of medical Internet research
BACKGROUND: Artificial intelligence (AI) chatbots, driven by advances in natural language processing, can analyze and generate human language through computational linguistics and machine learning. Despite the rapid development of large language mode...

Clinically interpretable electrovectorcardiographic machine learning criteria for the detection of echocardiographic left ventricular hypertrophy.

PloS one
Echocardiographic left ventricular hypertrophy (Echo-LVH) is frequently underdetected by traditional electrocardiogram (ECG) criteria due to limited sensitivity. We investigated whether integrating ECG with vectorcardiography (VCG) using a clinically...

AI-driven 3D CT imaging prediction model for improving preoperative detection of visceral pleural invasion in early-stage lung cancer.

PloS one
Visceral pleural invasion (VPI) is a critical prognostic factor in early-stage non-small-cell lung cancer (NSCLC), significantly affecting patient outcomes. Conventional computed tomography (CT) often fails to diagnose VPI accurately. This retrospect...

Neurophysiological mechanisms and predictive modeling of SSRI treatment response in depression disorder based on multidimensional EEG features.

Journal of affective disorders
BACKGROUND: Depression exhibits significant heterogeneity in antidepressant treatment response. This study aimed to develop an Electroencephalography (EEG)-based machine learning model integrating multidimensional features to predict selective seroto...