AIMC Topic: Machine Learning

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Identification of glycolysis genes signature for predicting prognosis in malignant pleural mesothelioma by bioinformatics and machine learning.

Frontiers in endocrinology
BACKGROUND: Glycolysis-related genes as prognostic markers in malignant pleural mesothelioma (MPM) is still unclear. We hope to explore the relationship between glycolytic pathway genes and MPM prognosis by constructing prognostic risk models through...

Epileptic Seizure Detection Using a Hybrid 1D CNN-Machine Learning Approach from EEG Data.

Journal of healthcare engineering
Electroencephalography (EEG) is a widely used technique for the detection of epileptic seizures. It can be recorded in a noninvasive manner to present the electrical activity of the brain. The visual inspection of nonlinear and highly complex EEG sig...

Artificial intelligence techniques for neuropathological diagnostics and research.

Neuropathology : official journal of the Japanese Society of Neuropathology
Artificial intelligence (AI) research began in theoretical neurophysiology, and the resulting classical paper on the McCulloch-Pitts mathematical neuron was written in a psychiatry department almost 80 years ago. However, the application of AI in dig...

Towards digital diagnosis of malaria: How far have we reached?

Journal of microbiological methods
The need for precise and early diagnosis of malaria and its distinction from other febrile illnesses is no doubt a prerequisite, primarily when standard rapid diagnostic tests (RDTs) cannot be totally relied upon. At the time of disease outbreaks, th...

Emulate randomized clinical trials using heterogeneous treatment effect estimation for personalized treatments: Methodology review and benchmark.

Journal of biomedical informatics
Big data and (deep) machine learning have been ambitious tools in digital medicine, but these tools focus mainly on association. Intervention in medicine is about the causal effects. The average treatment effect has long been studied as a measure of ...

Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques.

Sensors (Basel, Switzerland)
The treatment and diagnosis of colon cancer are considered to be social and economic challenges due to the high mortality rates. Every year, around the world, almost half a million people contract cancer, including colon cancer. Determining the grade...

Forecasting medical state transition using machine learning methods.

Scientific reports
Early circulatory failure detection is an effective way to reduce medical fatigue and improve state pre-warning ability. Instead of using 0-1 original state, a transformed state is proposed in this research, which reflects how the state is transforme...

Development and validation of a machine learning-augmented algorithm for diabetes screening in community and primary care settings: A population-based study.

Frontiers in endocrinology
BACKGROUND: Opportunely screening for diabetes is crucial to reduce its related morbidity, mortality, and socioeconomic burden. Machine learning (ML) has excellent capability to maximize predictive accuracy. We aim to develop ML-augmented models for ...

Mortality prediction in ICU Using a Stacked Ensemble Model.

Computational and mathematical methods in medicine
Artificial intelligence (AI) technology has huge scope in developing models to predict the survival rate of critically ill patients in the intensive care unit (ICU). The availability of electronic clinical data has led to the widespread use of variou...