Closing the Gap in High-Risk Pregnancy Care Using Machine Learning and Human-AI Collaboration

Health insurers often use algorithms to identify members who would benefit from care and condition management programs, which provide personalized, high-touch clinical support. Timely, accurate, and seamless integration between algorithmic identification and clinical intervention depends on effective collaboration between the system designers and nurse care managers. We focus on a high-risk pregnancy (HRP) program designed to reduce the likelihood of adverse prenatal, perinatal, and postnatal events and describe how we overcome three challenges of HRP programs as articulated by nurse care managers; (1) early detection of pregnancy, (2) accurate identification of impactable high-risk members, and (3) provision of explainable indicators to supplement predictions. We propose a novel algorithm for pregnancy identification that identifies pregnancies 57 days earlier than previous code-based models in a retrospective study. We then build a model to predict impactable pregnancy complications that achieves an AUROC of 0.760. Models for pregnancy identification and complications are then integrated into a proposed user interface. In a set of user studies, we collected quantitative and qualitative feedback from nurses on the utility of the predictions combined with clinical information driving the predictions on triaging members for the HRP program

Culturenet: a deep learning approach for engagement intensity estimation from face images of children with autism

© 2018 IEEE. Many children on autism spectrum have atypical behavioral expressions of engagement compared to their neu-rotypical peers. In this paper, we investigate the performance of deep learning models in the task of automated engagement estimation from face images of children with autism. Specifically, we use the video data of 30 children with different cultural backgrounds (Asia vs. Europe) recorded during a single session of a robot-assisted autism therapy. We perform a thorough evaluation of the proposed deep architectures for the target task, including within- and across-culture evaluations, as well as when using the child-independent and child-dependent settings. We also introduce a novel deep learning model, named CultureNet, which efficiently leverages the multi-cultural data when performing the adaptation of the proposed deep architecture to the target culture and child. We show that due to the highly heterogeneous nature of the image data of children with autism, the child-independent models lead to overall poor estimation of target engagement levels. On the other hand, when a small amount of data of target children is used to enhance the model learning, the estimation performance on the held-out data from those children increases significantly. This is the first time that the effects of individual and cultural differences in children with autism have empirically been studied in the context of deep learning performed directly from face images

Knowledge Distillation via Constrained Variational Inference

Knowledge distillation has been used to capture the knowledge of a teacher model and distill it into a student model with some desirable characteristics such as being smaller, more efficient, or more generalizable. In this paper, we propose a framework for distilling the knowledge of a powerful discriminative model such as a neural network into commonly used graphical models known to be more interpretable (e.g., topic models, autoregressive Hidden Markov Models). Posterior of latent variables in these graphical models (e.g., topic proportions in topic models) is often used as feature representation for predictive tasks. However, these posterior-derived features are known to have poor predictive performance compared to the features learned via purely discriminative approaches. Our framework constrains variational inference for posterior variables in graphical models with a similarity preserving constraint. This constraint distills the knowledge of the discriminative model into the graphical model by ensuring that input pairs with (dis)similar representation in the teacher model also have (dis)similar representation in the student model. By adding this constraint to the variational inference scheme, we guide the graphical model to be a reasonable density model for the data while having predictive features which are as close as possible to those of a discriminative model. To make our framework applicable to a wide range of graphical models, we build upon the Automatic Differentiation Variational Inference (ADVI), a black-box inference framework for graphical models. We demonstrate the effectiveness of our framework on two real-world tasks of disease subtyping and disease trajectory modeling