AIMC Topic: Machine Learning

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Artificial intelligence for identification of candidates for device-aided therapy in Parkinson's disease: DELIST-PD study.

Computers in biology and medicine
INTRODUCTION: In Parkinson's Disease (PD), despite available treatments focusing on symptom alleviation, the effectiveness of conventional therapies decreases over time. This study aims to enhance the identification of candidates for device-aided the...

Machine learning-based analysis of programmed cell death types and key genes in intervertebral disc degeneration.

Apoptosis : an international journal on programmed cell death
Intervertebral disc degeneration (IVDD) is intricately associated with various forms of programmed cell death (PCD). Identifying key PCD types and associated genes is essential for understanding the molecular mechanisms underlying IVDD and discoverin...

Integrated explainable machine learning and multi-omics analysis for survival prediction in cancer with immunotherapy response.

Apoptosis : an international journal on programmed cell death
To demonstrate the efficacy of machine learning models in predicting mortality in melanoma cancer, we developed an interpretability model for better understanding the survival prediction of cancer. To this end, the optimal features were identified, t...

Training humans to supplement a machine learning system: The role of guides in a digital mental health intervention.

Social science & medicine (1982)
Machine learning (ML) is increasingly prevalent in mental health care, with contemporary initiatives leveraging these technologies, sometimes in combination with wearable devices, toward novel interventions. This paper investigates the development of...

Harnessing machine learning in diagnosing complex hoarseness cases.

American journal of otolaryngology
PURPOSE: Traditional vocal fold pathology recognition typically requires expertise of laryngologists and advanced instruments, primarily through direct visualization. This study aims to augment this conventional paradigm by introducing a parallel dia...

Harnessing machine learning and multi-omics to explore tumor evolutionary characteristics and the role of AMOTL1 in prostate cancer.

International journal of biological macromolecules
Although recent advancements have shed light on the crucial role of coordinated evolution among cell subpopulations in influencing disease progression, the full potential of these insights has not yet been fully harnessed in the clinical application ...

AI-based medication adherence prediction in patients with schizophrenia and attenuated psychotic disorders.

Schizophrenia research
OBJECTIVE: The capacity of machine-learning algorithms to predict medication adherence was assessed using data from AiCure, a computer vision-assisted smartphone application, which records the medication ingestion event.

Tensor Coupled Learning of Incomplete Longitudinal Features and Labels for Clinical Score Regression.

IEEE transactions on pattern analysis and machine intelligence
Longitudinal data with incomplete entries pose a significant challenge for clinical score regression over multiple time points. Although many methods primarily estimate longitudinal scores with complete baseline features (i.e., features collected at ...

Medical Federated Model With Mixture of Personalized and Shared Components.

IEEE transactions on pattern analysis and machine intelligence
Although data-driven methods usually have noticeable performance on disease diagnosis and treatment, they are suspected of leakage of privacy due to collecting data for model training. Recently, federated learning provides a secure and trustable alte...