AIMC Topic: Humans

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Exploring Young Adults' Attitudes Toward AI-Driven mHealth Apps: Qualitative Study.

JMIR human factors
BACKGROUND: Artificial intelligence (AI)-driven mobile health (mHealth) apps are emerging as a promising tool for health management, yet little is known about users' psychological perceptions and attitudes toward these technologies. Understanding the...

Preferences of Patients With Tuberculosis for AI-Assisted Remote Health Management: Discrete Choice Experiment.

Journal of medical Internet research
BACKGROUND: Tuberculosis remains a major global public health challenge, especially in low-resource settings where long-term treatment adherence and regular follow-up are critical. The integration of artificial intelligence (AI) into remote health ma...

Harnessing Geospatial Artificial Intelligence (GeoAI) for Environmental Epidemiology: A Narrative Review.

Current environmental health reports
PURPOSE OF REVIEW: Geospatial analysis is an essential tool for research on the role of environmental exposures and health, and critical for understanding impacts of environmental risk factors on diseases with long latency (e.g. cardiovascular diseas...

Artificial intelligence and the future of patient-centered outcomes.

Journal of patient-reported outcomes
BACKGROUND: Terheyden et al. recently described a compelling vision for large language model-enabled patient-reported outcome measures (LLM-PROMs).

Multi-omics based consensus subtypes, development of prognostic signature, and identification of INHBB as a potential therapeutic target in colorectal cancer.

Functional & integrative genomics
This study aims to refine molecular subtypes via multi-omics data, develop a prognostic signature, and identify novel biomarkers in colorectal cancer (CRC). On the basis of the multi-omics data, the MOVICS R package was used to divide patients with C...

Enhancing accuracy of virtual kinase profiling via application of graph neural network to 3D pharmacophore ensembles.

Journal of computer-aided molecular design
Kinase profiling is an essential step in both hit identification and selectivity evaluation. Since in vitro testing of large chemical libraries is costly and time-consuming, a computational approach can be applied to narrow down the reasonable chemic...

Metabolic remodeling and its hidden heterogeneity in uterine fibroids: comprehensive metabolomic profiling and mass spectrometry imaging.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION: As the most common benign gynecological tumor in women, uterine fibroids not only pose a serious threat to reproductive health but also directly impair fertility. The structural abnormalities of the uterus and metabolic disturbances the...

Development and validation of machine-learning model based on dynamic tumor markers in predicting pathological complete response after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer: a multicenter cohort study.

International journal of colorectal disease
OBJECTIVE: In this study, we constructed a new pCR predictor based on dynamic tumor marker changes before and after NCRT, the dynamic tumor marker score (DTMS), and combined it with other clinicopathological features to build a machine-learning model...

Molecular dynamics simulations of proteins: an in-depth review of computational strategies, structural insights, and their role in medicinal chemistry and drug development.

Biological cybernetics
Molecular dynamics (MD) simulations have emerged as a powerful and extensively employed tool in biomedical research, offering insights into intricate biomolecular processes such as structural flexibility and molecular interactions, and playing a pivo...

Construction of a predictive model for the risk of moderate-to-severe cancer-related fatigue in colorectal cancer chemotherapy patients: an interpretable machine learning approach.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
PURPOSE: This study aimed to analyze the influencing factors of moderate-to-severe cancer-related fatigue (CRF) in colorectal cancer (CRC) chemotherapy patients and to develop a predictive risk stratification model.