Actigraphic recording of motor activity in depressed inpatients: a novel computational approach to prediction of clinical course and hospital discharge.

Depressed patients present with motor activity abnormalities, which can be easily recorded using actigraphy. The extent to which actigraphically recorded motor activity may predict inpatient clinical course and hospital discharge remains unknown. Participants were recruited from the acute psychiatric inpatient ward at Hospital Rey Juan Carlos (Madrid, Spain). They wore miniature wrist wireless inertial sensors (actigraphs) throughout the admission. We modeled activity levels against the normalized length of admission-'Progress Towards Discharge' (PTD)-using a Hierarchical Generalized Linear Regression Model. The estimated date of hospital discharge based on early measures of motor activity and the actual hospital discharge date were compared by a Hierarchical Gaussian Process model. Twenty-three depressed patients (14 females, age: 50.17 ± 12.72 years) were recruited. Activity levels increased during the admission (mean slope of the linear function: 0.12 ± 0.13). For n = 18 inpatients (78.26%) hospitalised for at least 7 days, the mean error of Prediction of Hospital Discharge Date at day 7 was 0.231 ± 22.98 days (95% CI 14.222-14.684). These n = 18 patients were predicted to need, on average, 7 more days in hospital (for a total length of stay of 14 days) (PTD = 0.53). Motor activity increased during the admission in this sample of depressed patients and early patterns of actigraphically recorded activity allowed for accurate prediction of hospital discharge date.

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.

A Heavy Tailed Expectation Maximization Hidden Markov Random Field Model with Applications to Segmentation of MRI.

A wide range of segmentation approaches assumes that intensity histograms extracted from magnetic resonance images (MRI) have a distribution for each brain tissue that can be modeled by a Gaussian distribution or a mixture of them. Nevertheless, intensity histograms of White Matter and Gray Matter are not symmetric and they exhibit heavy tails. In this work, we present a hidden Markov random field model with expectation maximization (EM-HMRF) modeling the components using the α-stable distribution. The proposed model is a generalization of the widely used EM-HMRF algorithm with Gaussian distributions. We test the α-stable EM-HMRF model in synthetic data and brain MRI data. The proposed methodology presents two main advantages: Firstly, it is more robust to outliers. Secondly, we obtain similar results than using Gaussian when the Gaussian assumption holds. This approach is able to model the spatial dependence between neighboring voxels in tomographic brain MRI.