AIMC Topic: Machine Learning

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A machine learning screening model for identifying the risk of high-frequency hearing impairment in a general population.

BMC public health
BACKGROUND: Hearing impairment (HI) has become a major public health issue in China. Currently, due to the limitations of primary health care, the gold standard for HI diagnosis (pure-tone hearing test) is not suitable for large-scale use in communit...

Multimodal workflows optimally predict response to repetitive transcranial magnetic stimulation in patients with schizophrenia: a multisite machine learning analysis.

Translational psychiatry
The response variability to repetitive transcranial magnetic stimulation (rTMS) challenges the effective use of this treatment option in patients with schizophrenia. This variability may be deciphered by leveraging predictive information in structura...

Predictive Model to Identify the Long Time Survivor in Patients with Glioblastoma: A Cohort Study Integrating Machine Learning Algorithms.

Journal of molecular neuroscience : MN
We aimed to develop and validate a predictive model for identifying long-term survivors (LTS) among glioblastoma (GB) patients, defined as those with an overall survival (OS) of more than 3 years. A total of 293 GB patients from CGGA and 169 from TCG...

Evaluating seismic risk by MCDM and machine learning for the eastern coast of India.

Environmental monitoring and assessment
Natural disasters such as earthquakes endanger human lives and infrastructure, particularly in urban areas. With the advancements in science and technology in understanding natural hazards, recent studies have attempted to mitigate them by mapping th...

Application of machine learning algorithms to identify people with low bone density.

Frontiers in public health
BACKGROUND: Osteoporosis is becoming more common worldwide, imposing a substantial burden on individuals and society. The onset of osteoporosis is subtle, early detection is challenging, and population-wide screening is infeasible. Thus, there is a n...

Predicting clinical outcomes of SARS-CoV-2 infection during the Omicron wave using machine learning.

PloS one
The Omicron SARS-CoV-2 variant continues to strain healthcare systems. Developing tools that facilitate the identification of patients at highest risk of adverse outcomes is a priority. The study objectives are to develop population-scale predictive ...

Identification of Age-Related Characteristic Genes Involved in Severe COVID-19 Infection Among Elderly Patients Using Machine Learning and Immune Cell Infiltration Analysis.

Biochemical genetics
Elderly patients infected with severe acute respiratory syndrome coronavirus 2 are at higher risk of severe clinical manifestation, extended hospitalization, and increased mortality. Those patients are more likely to experience persistent symptoms an...

A systematic review of prediction models on arteriovenous fistula: Risk scores and machine learning approaches.

The journal of vascular access
OBJECTIVE: Failure-to-mature and early stenosis remains the Achille's heel of hemodialysis arteriovenous fistula (AVF) creation. The maturation and patency of an AVF can be influenced by a variety of demographic, comorbidity, and anatomical factors. ...

Diagnosis and Severity Assessment of COPD Using a Novel Fast-Response Capnometer and Interpretable Machine Learning.

COPD
INTRODUCTION: Spirometry is the gold standard for COPD diagnosis and severity determination, but is technique-dependent, nonspecific, and requires administration by a trained healthcare professional. There is a need for a fast, reliable, and precise ...

Artificial Intelligence in Radiology: What Is Its True Role at Present, and Where Is the Evidence?

Radiologic clinics of North America
The integration of artificial intelligence (AI) in radiology has brought about substantial advancements and transformative potential in diagnostic imaging practices. This study presents an overview of the current research on the application of AI in ...