Deep Sequential Models for Suicidal Ideation from Multiple Source Data

This paper presents a novel method for predicting suicidal ideation from electronic health records (EHR) and ecological momentary assessment (EMA) data using deep sequential models. Both EHR longitudinal data and EMA question forms are defined by asynchronous, variable length, randomly sampled data sequences. In our method, we model each of them with a recurrent neural network, and both sequences are aligned by concatenating the hidden state of each of them using temporal marks. Furthermore, we incorporate attention schemes to improve performance in long sequences and time-independent pre-trained schemes to cope with very short sequences. Using a database of 1023 patients, our experimental results show that the addition of EMA records boosts the system recall to predict the suicidal ideation diagnosis from 48.13% obtained exclusively from EHR-based state-of-the-art methods to 67.78%. Additionally, our method provides interpretability through the t-distributed stochastic neighbor embedding (t-SNE) representation of the latent space. Furthermore, the most relevant input features are identified and interpreted medically.This work was supported in part by the Spanish MINECO under Grants TEC2015-69868-C2-1-R, TEC2016-78434-C3-3-R, and TEC2017-92552-EXP, in part by Spanish MICINN under Grant RTI2018-099655-B-I00, in part by Comunidad de Madrid under Grants IND2017/TIC-7618, IND2018/TIC-9649, Y2018/TCS-4705, and B2017/BMD-3740 AGES-CM 2CM, in part by BBVA Foundation under Deep-DARWiN - FBBVA Grant for scientific research teams 2018, in part by ISCIII under Grant PI16/01852, and in part by AFSP under Grant LSRG-1-005-16

Unsupervised learning of global factors in deep generative models

We present a novel deep generative model based on non i.i.d. variational autoencoders that captures global dependencies among observations in a fully unsupervised fashion. In contrast to the recent semi-supervised alternatives for global modeling in deep generative models, our approach combines a mixture model in the local or data-dependent space and a global Gaussian latent variable, which lead us to obtain three particular insights. First, the induced latent global space captures interpretable disentangled representations with no user-defined regularization in the evidence lower bound (as in beta-VAE and its generalizations). Second, we show that the model performs domain alignment to find correlations and interpolate between different databases. Finally, we study the ability of the global space to discriminate between groups of observations with non-trivial underlying structures, such as face images with shared attributes or defined sequences of digits images.This work has been partly supported by Spanish government (AEI/MCI) under grants PID2021-123182OB-I00, PID2021-125159NB-I00 and RTI2018-099655-B-100, by Comunidad de Madrid under grant IND2022/TIC-23550, by the European Union (FEDER) and the European Research Council (ERC) through the European Union's Horizon 2020 research and innovation program under Grant 714161, and by Comunidad de Madrid and FEDER through IntCARE-CM. The work of Ignacio Peis has been also supported by by Spanish government (MIU) under grant FPU18/00516