BACKGROUND Early detection of mental disorders symptoms can lead to prompt and correct diagnosis and reduce the recurrence of these symptoms and associated disabilities. Creating a tool to detect early symptoms is crucial for taking the necessary measures to prevent major onsets of mental diseases. Early indicators of mental health disorders can be detected through changes in daily activity patterns, which activity trackers and speech data can capture. OBJECTIVE We aim to compare the accuracy of personalized machine-learning models with population-level models and evaluate the robustness of these models across various languages. Additionally, investigate the significance of speech data when the user reads a neutral text versus reflecting on their daily life experiences while predicting mental disorders. METHODS Our research is based on longitudinal data from each participant. Hence, we designed the collection process to capture several data points in time that could aid machine learning algorithms to capture patterns of mental disorder symptoms better. This research uses machine learning models to predict the levels of anxiety, stress, and depression in participants based on data collected from wearable devices and voice recordings. The data includes daily activity from smartwatches and voice data collected through text reading and free-form speech. RESULTS The study is ongoing, and data are collected from at least 50 participants attending two major universities, and the data collection complies with ethical and personal data privacy requirements. CONCLUSIONS The study aims to advance personalized machine learning for mental health, generate a dataset to predict DASS21 results, and deploy a framework to detect onsets of depression, anxiety, and stress, with the final goal of developing a non-invasive and objective method for collecting mental health data and prompt detection of mental disorder symptoms.

Background: To understand and approach the COVID-19 spread, Machine Learning offers fundamental tools. This study presents the use of machine learning techniques for the projection of COVID-19 infections and deaths in Mexico. The research has three main objectives: first, to identify which function adjusts the best to the infected population growth in Mexico; second, to determine the feature importance of climate and mobility; third, to compare the results of a traditional time series statistical model with a modern approach in machine learning. The motivation for this work is to support health care providers in their preparation and planning. Methods: The methods used are linear, polynomial, and generalized logistic regression models to evaluate the growth of the COVID-19 incidents in the country. Additionally, machine learning and time-series techniques are used to identify feature importance and perform forecasting for daily cases and fatalities. The study uses the publicly available data sets from the John Hopkins University of Medicine in conjunction with mobility rates obtained from Google’s Mobility Reports and climate variables acquired from Weather Online. Results: The results suggest that the logistic growth model fits best the behavior of the pandemic in Mexico, that there is a significant correlation of climate and mobility variables with the disease numbers, and that LSTM is a more suitable approach for the prediction of daily cases. Conclusion: We hope that this study can make some contributions to the world’s response to this epidemic as well as give some references for future research.