logo
ResearchBunny Logo
Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection

Medicine and Health

Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection

Y. Takahashi, M. Ueki, et al.

Discover the groundbreaking research by Yuta Takahashi and colleagues on a novel prediction model for depressive symptoms using the HSIC Lasso algorithm! This innovative study leverages a vast metabolomic dataset from the population affected by the Great East Japan Earthquake, revealing key metabolites that enhance predictive power.

00:00
00:00
Playback language: English
Citation Metrics
Citations
0
Influential Citations
0
Reference Count
0

Note: The citation metrics presented here have been sourced from Semantic Scholar and OpenAlex.

Related Publications

Explore these studies to deepen your understanding of the subject.

Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms
Medicine and Health

Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms

P. Amiri, M. Montazeri, et al.

Machine learning-based prediction of COVID-19 diagnosis based on symptoms
Medicine and Health

Machine learning-based prediction of COVID-19 diagnosis based on symptoms

Y. Zoabi, S. Deri-rozov, et al.

Machine learning-based prediction of in-hospital death for patients with takotsubo syndrome: The InterTAK-ML model
Medicine and Health

Machine learning-based prediction of in-hospital death for patients with takotsubo syndrome: The InterTAK-ML model

O. D. Filippo, V. L. Cammann, et al.

Reliability of Supervised Machine Learning Using Synthetic Data in Health Care: Model to Preserve Privacy for Data Sharing
Computer Science

Reliability of Supervised Machine Learning Using Synthetic Data in Health Care: Model to Preserve Privacy for Data Sharing

D. Rankin, M. Black, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny