AIMC Topic: Humans

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Artificial intelligence-enabled safety monitoring in Alzheimer's disease clinical trials.

The journal of prevention of Alzheimer's disease
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...

Vision Sensor for Automatic Recognition of Human Activities via Hybrid Features and Multi-Class Support Vector Machine.

Sensors (Basel, Switzerland)
Over recent years, automated Human Activity Recognition (HAR) has been an area of concern for many researchers due to its widespread application in surveillance systems, healthcare environments, and many more. This has led researchers to develop cohe...

Leveraging Artificial Intelligence/Machine Learning Models to Identify Potential Palliative Care Beneficiaries: A Systematic Review.

Journal of gerontological nursing
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...

Analysis of User-Generated Posts on Social Media of Adjuvant Analgesics: A Machine Learning Study.

International journal of medical sciences
Antiepileptics and antidepressants are frequently prescribed for chronic pain, but their efficacy and potential adverse effects raise concerns, including dependency issues. Increased prescriptions, sometimes fraudulent, prompted reclassification of ...

Identification of common diagnostic genes and molecular pathways in endometriosis and systemic lupus erythematosus by machine learning approach and in vitro experiment.

International journal of medical sciences
Growing research suggests that endometriosis and systemic lupus erythematosus (SLE) are both chronic inflammatory diseases and closely related, but no studies have explored their common molecular characteristics and underlying mechanisms. Based on GE...

Development of Time-Aggregated Machine Learning Model for Relapse Prediction in Pediatric Crohn's Disease.

Clinical and translational gastroenterology
INTRODUCTION: Pediatric Crohn's disease (CD) easily progresses to an active disease compared with adult CD, making it important to predict and minimize CD relapses. However, prediction of relapse at various time points (TPs) during pediatric CD remai...

A Systematic Review of Features Forecasting Patient Arrival Numbers.

Computers, informatics, nursing : CIN
Adequate nurse staffing is crucial for quality healthcare, necessitating accurate predictions of patient arrival rates. These forecasts can be determined using supervised machine learning methods. Optimization of machine learning methods is largely a...

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)
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)
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...