Healthcare

Automatic Triage and Analysis of Online Suicide Risk with Document Embeddings and Latent Dirichlet Allocation

To predict which users are at risk of suicide based on a small dataset of online posts, we leverage pre-trained sentence embeddings from large language models, and achieve high F1 scores (.83-.92). We further analyze users’ posts to determine which topics are most associated with suicidal users.

N. Jones, Natasha Jaques, P. Pataranutaporn, A. Ghandeharioun, R. Picard

2019 In Affective Computing and Intelligence Interaction (ACII) workshop on Machine Learning for Mental Health

Automatic Triage and Analysis of Online Suicide Risk with Document Embeddings and Latent Dirichlet Allocation

Importance of Sleep Data in Predicting Next-Day Stress, Happiness, and Health in College Students

We train personalized hierarchical Bayes models to predict individual’s next-day stress, happiness, and health, and examine the effect of including features related to sleep in the model. Including sleep features significantly improves performance when predicting happiness.

S. Taylor, Natasha Jaques, Sano, A. E. Nosakhare, E. B. Klerman, R. Picard

2017 In Journal of Sleep and Sleep Disorders Research (suppl_1)

Importance of Sleep Data in Predicting Next-Day Stress, Happiness, and Health in College Students

Multimodal Autoencoder: A Deep Learning Approach to Filling in Missing Sensor Data and Enabling Better Mood Prediction

Predicting signals like stress and health depends on collecting noisy data from a number of modalities, e.g. smartphone data, or physiological data from a wrist-worn sensor. Our method can continue making accurate predictions even when a modality goes missing; for example, if the person forgets to wear their sensor.

Natasha Jaques, S. Taylor, A. Sano, R. Picard

2017 In International Conference on Affective Computing and Intelligent Interaction (ACII)

Multimodal Autoencoder: A Deep Learning Approach to Filling in Missing Sensor Data and Enabling Better Mood Prediction

Personalized Multitask Learning for Predicting Tomorrow's Mood, Stress, and Health

Traditional, one-size-fits-all machine learning models fail to account for individual differences in predicting wellbeing outcomes like stress, mood, and health. Instead, we personalize models to the individual using multi-task learning (MTL), employing hierarchical Bayes, kernel-based and deep neural network MTL models to improve prediction accuracy by 13-23%.

Natasha Jaques*, S. Taylor*, E. Nosakhare, A. Sano, R. Picard

2017 In IEEE Transactions on Affective Computing (TAFFC) Best Paper; NeurIPS Machine Learning for Healthcare (ML4HC) Workshop Best Paper

Personalized Multitask Learning for Predicting Tomorrow's Mood, Stress, and Health

Predicting Tomorrow’s Mood, Health, and Stress Level using Personalized Multitask Learning and Domain Adaptation

Modeling measures like mood, stress, and health using a monolithic machine learning model leads to low prediction accuracy. Instead, we develop personalized regression models using multi-task learning and Gaussian Processes, leading to dramatic improvements in next-day predictions.

Natasha Jaques, O. Rudovic, S. Taylor, A. Sano, R. Picard

2017 In Proceedings of Machine Learning Research

Predicting Tomorrow’s Mood, Health, and Stress Level using Personalized Multitask Learning and Domain Adaptation

Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control

To combine supervised learning on data with reinforcement learning, we pre-train a supervised data prior, and penalize KL-divergence from this model using RL training. This enables effective learning of complex sequence-modeling problems for which we wish to match the data while optimizing external metrics like drug effectiveness. The approach produces compelling results in the disparate domains of music generation and drug discovery.

Natasha Jaques, S. Gu, D. Bahdanau, J. M. Hernandez-Lobato, R. E. Turner, D. Eck

2017 In International Conference on Machine Learning (ICML)

Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control

Machine Learning of Sleep and Wake Behaviors to Classify Self-Reported Evening Mood

Machine learning applied to nightly data from sensors and smartphones, shows value for predicting college student’s mood the following evening. Using multi-task learning to simultaneously predicted related wellbeing factors like health, energy, stress, and alertness improves performance.

S. Taylor, Natasha Jaques, A. Sano, A. Azaria, A. Ghandeharioun, R. Picard

2016 In Sleep

Machine Learning of Sleep and Wake Behaviors to Classify Self-Reported Evening Mood

Multi-task Multi-Kernel Learning for Estimating Individual Wellbeing

Wellbeing is a complex internal state consisting of several related dimensions, such as happiness, stress, energy, and health. We use Multi-task Multi-kernel learning to classify them simultaneously, leading to significant performance approvements.

Natasha Jaques*, S. Taylor*, A. Sano, R. Picard

2015 In Neural Information Processing Systems (NeurIPS) Workshop on Multimodal Machine Learning

Multi-task Multi-Kernel Learning for Estimating Individual Wellbeing

Predicting students' happiness from physiology, phone, mobility, and behavioral data

We train machine learning models to predict students’ happiness from extensive data comprising physiological signals, location, smartphone logs, and behavioral questions. Analyzing which features provide the highest information gain reveals that skin conductance during sleep, social interaction, exercise, and fewer phone screen hours are all positively associated with happiness.

Natasha Jaques*, S. Taylor*, A. Azaria, A. Ghandeharioun, A. Sano, R. Picard

2015 In International Conference on Affective Computing and Intelligent Interaction (ACII)

Predicting students' happiness from physiology, phone, mobility, and behavioral data

Prediction of happy-sad mood from daily behaviors and previous sleep history

We trained machine learning models to classify happy vs. sad moods in college students using data from surveys and wearable sensors. Factors such as poor health-related behavior, more academic activity hours, and more neutral social interactions were highly predictive of mood.

A. Sano, A. Z. Yu, A. W. McHill, A.J. Phillips, S. Taylor, Natasha Jaques, C. A. Czeisler, E. B. Klerman, R. Picard

2015 In International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Prediction of happy-sad mood from daily behaviors and previous sleep history