Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis

no code implementations • 20 Dec 2022 • Shaoxiong Sun, Amos A. Folarin, Yuezhou Zhang, NIcholas Cummins, Rafael Garcia-Dias, Callum Stewart, Yatharth Ranjan, Zulqarnain Rashid, Pauline Conde, Petroula Laiou, Heet Sankesara, Faith Matcham, Daniel Leightley, Katie M. White, Carolin Oetzmann, Alina Ivan, Femke Lamers, Sara Siddi, Sara Simblett, Raluca Nica, Aki Rintala, David C. Mohr, Inez Myin-Germeys, Til Wykes, Josep Maria Haro, Brenda W. J. H. Penninx, Srinivasan Vairavan, Vaibhav A. Narayan, Peter Annas, Matthew Hotopf, Richard J. B. Dobson

Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model.

Machine learning in APOGEE: Identification of stellar populations through chemical abundances

no code implementations • 30 Jul 2019 • Rafael Garcia-Dias, Carlos Allende Prieto, Jorge Sánchez Almeida, Pedro Alonso Palicio

We find that some of the algorithms we explored are capable of blindly identify stellar populations with similar ages and chemical distributions in the APOGEE data.

BIG-bench Machine Learning Clustering +1

Machine learning in APOGEE: Unsupervised spectral classification with $K$-means

no code implementations • 24 Jan 2018 • Rafael Garcia-Dias, Carlos Allende Prieto, Jorge Sánchez Almeida, Ignacio Ordovás-Pascual

We find a lack of obvious groups in flux space, and identify limitations of the $K$-means algorithm in dealing with this kind of data.

BIG-bench Machine Learning Classification +2