AIMC Topic: United States

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Machine Learning Potential for Identifying and Forecasting Complex Environmental Drivers of Infections in the United States.

Environmental health perspectives
BACKGROUND: Environmental change in coastal areas can drive marine bacteria and resulting infections, such as those caused by , with both foodborne and nonfoodborne exposure routes and high mortality. Although ecological drivers of in the environmen...

Artificial Intelligence In Health And Health Care: Priorities For Action.

Health affairs (Project Hope)
The field of artificial intelligence (AI) has entered a new cycle of intense opportunity, fueled by advances in deep learning, including generative AI. Applications of recent advances affect many aspects of everyday life, yet nowhere is it more impor...

Robust Automated Harmonization of Heterogeneous Data Through Ensemble Machine Learning: Algorithm Development and Validation Study.

JMIR medical informatics
BACKGROUND: Cohort studies contain rich clinical data across large and diverse patient populations and are a common source of observational data for clinical research. Because large scale cohort studies are both time and resource intensive, one alter...

Classifying Unstructured Text in Electronic Health Records for Mental Health Prediction Models: Large Language Model Evaluation Study.

JMIR medical informatics
BACKGROUND: Prediction models have demonstrated a range of applications across medicine, including using electronic health record (EHR) data to identify hospital readmission and mortality risk. Large language models (LLMs) can transform unstructured ...

Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and Australia.

Translational psychiatry
Existing dementia prediction models using non-neuroimaging clinical measures have been limited in their ability to identify disease. This study used machine learning to re-examine the diagnostic potential of clinical measures for dementia. Data was s...

Integrating Remote Sensing and Soil Features for Enhanced Machine Learning-Based Corn Yield Prediction in the Southern US.

Sensors (Basel, Switzerland)
Efficient and reliable corn ( L.) yield prediction is important for varietal selection by plant breeders and management decision-making by growers. Unlike prior studies that focus mainly on county-level or controlled laboratory-scale areas, this stud...

Mitigating bias in AI mortality predictions for minority populations: a transfer learning approach.

BMC medical informatics and decision making
BACKGROUND: The COVID-19 pandemic has highlighted the crucial role of artificial intelligence (AI) in predicting mortality and guiding healthcare decisions. However, AI models may perpetuate or exacerbate existing health disparities due to demographi...

Reimbursement and Regulatory Landscape for Artificial Intelligence in Medical Technology.

Gastrointestinal endoscopy clinics of North America
Integration of artificial intelligence (AI) into medical devices and services promises significant improvements in the diagnosis and treatment of disease. This article reviews current payment pathways for AI medical technology and the regulatory issu...

Artificial intelligence and machine learning in veterinary medicine: a regulatory perspective on current initiatives and future prospects.

American journal of veterinary research
The US FDA's Center for Veterinary Medicine (CVM) is advancing its leadership in veterinary science by integrating AI and machine learning (ML) into its regulatory framework and scientific initiatives. This paper explores the CVM's strategic approach...