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Towards the automatic detection of activities of daily living using eye-movement and accelerometer data with neural networks.
Computers in biology and medicine
Jan 1, 2025
Early diagnosis of neurodegenerative diseases, such as Alzheimer's disease, improves treatment and care outcomes for patients. Early signs of cognitive decline can be detected using functional scales, which are written records completed by a clinicia...
Identifying proteomic prognostic markers for Alzheimer's disease with survival machine learning: The Framingham Heart Study.
The journal of prevention of Alzheimer's disease
Jan 1, 2025
BACKGROUND: Protein abundance levels, sensitive to both physiological changes and external interventions, are useful for assessing the Alzheimer's disease (AD) risk and treatment efficacy. However, identifying proteomic prognostic markers for AD is c...
Prognostic Features for Overall Survival in Male Diabetic Patients Undergoing Hemodialysis Using Elastic Net Penalized Cox Regression; A Machine Learning Approach.
Archives of Iranian medicine
Jan 1, 2025
BACKGROUND: Diabetics constitute a significant percentage of hemodialysis (HD) patients with higher mortality, especially among male patients. A machine learning algorithm was used to optimize the prediction of time to death in male diabetic hemodial...
F-18 FDG PET/CT based Preoperative Machine Learning Prediction Models for Evaluating Regional Lymph Node Metastasis Status of Patients with Colon Cancer.
Asian Pacific journal of cancer prevention : APJCP
Jan 1, 2025
OBJECTIVE: This study aimed to develop a simple machine-learning model incorporating lymph node metastasis status with F-18 Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) and clinical information for predicting regio...
Artificial intelligence-enabled safety monitoring in Alzheimer's disease clinical trials.
The journal of prevention of Alzheimer's disease
Jan 1, 2025
BACKGROUND: Investigators conducting clinical trials have an ethical, scientific, and regulatory obligation to protect the safety of trial participants. Traditionally, safety monitoring includes manual review and coding of adverse event data by exper...
Leveraging Artificial Intelligence/Machine Learning Models to Identify Potential Palliative Care Beneficiaries: A Systematic Review.
Journal of gerontological nursing
Jan 1, 2025
PURPOSE: The current review examined the application of artificial intelligence (AI) and machine learning (ML) techniques in palliative care, specifically focusing on models used to identify potential beneficiaries of palliative services among indivi...
Identification of genomic alteration and prognosis using pathomics-based artificial intelligence in oral leukoplakia and head and neck squamous cell carcinoma: a multicenter experimental study.
International journal of surgery (London, England)
Jan 1, 2025
BACKGROUND: Loss of chromosome 9p is an important biomarker in the malignant transformation of oral leukoplakia (OLK) to head and neck squamous cell carcinoma (HNSCC), and is associated with the prognosis of HNSCC patients. However, various challenge...
A machine learning model utilizing Delphian lymph node characteristics to predict contralateral central lymph node metastasis in papillary thyroid carcinoma: a prospective multicenter study.
International journal of surgery (London, England)
Jan 1, 2025
BACKGROUND: This study aimed to use artificial intelligence (AI) to integrate various radiological and clinical pathological data to identify effective predictors of contralateral central lymph node metastasis (CCLNM) in patients with papillary thyro...
Clinical Validation of a Deep Learning Algorithm for Automated Coronary Artery Disease Detection and Classification Using a Heterogeneous Multivendor Coronary Computed Tomography Angiography Data Set.
Journal of thoracic imaging
Jan 1, 2025
PURPOSE: We sought to clinically validate a fully automated deep learning (DL) algorithm for coronary artery disease (CAD) detection and classification in a heterogeneous multivendor cardiac computed tomography angiography data set.
Artificial intelligence-based multi-modal multi-tasks analysis reveals tumor molecular heterogeneity, predicts preoperative lymph node metastasis and prognosis in papillary thyroid carcinoma: a retrospective study.
International journal of surgery (London, England)
Jan 1, 2025
BACKGROUND: Papillary thyroid carcinoma (PTC) is the predominant form of thyroid cancer globally, especially when lymph node metastasis (LNM) occurs. Molecular heterogeneity, driven by genetic alterations and tumor microenvironment components, contri...
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