Through machine learning algorithms, the most influencing factors for UD and BD classification were recombined and applied for rapid diagnosis. This highly feasible method holds the potential for convenient and accurate diagnosis of young patients in research and clinical practice.
Keyphrases
- machine learning
- bipolar disorder
- artificial intelligence
- end stage renal disease
- deep learning
- clinical practice
- big data
- ejection fraction
- chronic kidney disease
- newly diagnosed
- young adults
- physical activity
- depressive symptoms
- major depressive disorder
- peritoneal dialysis
- high resolution
- patient reported outcomes
- mass spectrometry
- sensitive detection
- loop mediated isothermal amplification
- quantum dots