Inferring Concept Prerequisite Relations from Online Educational Resources

The Internet has rich and rapidly increasing sources of high quality educational content. Inferring prerequisite relations between educational concepts is required for modern large-scale online educational technology applications such as personalized recommendations and automatic curriculum creation. We present PREREQ, a new supervised learning method for inferring concept prerequisite relations. PREREQ is designed using latent representations of concepts obtained from the Pairwise Latent Dirichlet Allocation model, and a neural network based on the Siamese network architecture. PREREQ can learn unknown concept prerequisites from course prerequisites and labeled concept prerequisite data. It outperforms state-of-the-art approaches on benchmark datasets and can effectively learn from very less training data. PREREQ can also use unlabeled video playlists, a steadily growing source of training data, to learn concept prerequisites, thus obviating the need for manual annotation of course prerequisites.Comment: Accepted at the AAAI Conference on Innovative Applications of Artificial Intelligence (IAAI-19

Deep Recurrent Neural Networks for Mortality Prediction in Intensive Care using Clinical Time Series at Multiple Resolutions

Mortality models in Intensive Care Units (ICU) are important for clinical decision support tasks such as identifying high-risk patients and prioritizing their care. Previous mortality models have used predictive variables mainly from Electronic Medical Records (EMR) where each patient observation can be represented as a sparse multivariate time series. Bedside monitors are another common data source in ICUs containing high-resolution time series, which have not been explored in combination with EMR data for mortality modelling. We take the first step towards building such a model. Specialized techniques developed for sparse time series cannot be used to model multiple time series at different resolutions. To address this problem, we develop MTS-RNN, a new deep recurrent neural network architecture. Our preliminary experiments on real clinical data show that MTS-RNN outperforms state-of-the-art mortality models in predictive accuracy, highlighting the importance of using clinical time series at multiple resolutions for ICU mortality prediction

Towards a Theory-Based Evaluation of Explainable Predictions in Healthcare

Modern Artificial Intelligence (AI) models offer high predictive accuracy but often lack interpretability with respect to reasons for predictions. Explanations for predictions are usually necessary in making high-stakes clinical decisions. Hence, many Explainable AI (XAI) techniques have been designed to generate explanations for predictions from black-box models. However, there are no rigorous metrics to evaluate these explanations, especially with respect to their usefulness to clinicians. We develop a principled method to evaluate explanations by drawing on theories from social science and accounting for specific requirements of the clinical context. As a case study, we use our metric to evaluate explanations generated by two popular XAI algorithms in the task of predicting the onset of Alzheimer\u27s disease using genetic data. Our preliminary findings are promising and illustrate the versatility and utility of our metric. Our work contributes to the practical and theoretical development of XAI techniques and Clinical Decision Support Systems

Exact Pareto Optimal Search for Multi-Task Learning and Multi-Criteria Decision-Making

Given multiple non-convex objective functions and objective-specific weights, Chebyshev scalarization (CS) is a well-known approach to obtain an Exact Pareto Optimal (EPO), i.e., a solution on the Pareto front (PF) that intersects the ray defined by the inverse of the weights. First-order optimizers that use the CS formulation to find EPO solutions encounter practical problems of oscillations and stagnation that affect convergence. Moreover, when initialized with a PO solution, they do not guarantee a controlled trajectory that lies completely on the PF. These shortcomings lead to modeling limitations and computational inefficiency in multi-task learning (MTL) and multi-criteria decision-making (MCDM) methods that utilize CS for their underlying non-convex multi-objective optimization (MOO). To address these shortcomings, we design a new MOO method, EPO Search. We prove that EPO Search converges to an EPO solution and empirically illustrate its computational efficiency and robustness to initialization. When initialized on the PF, EPO Search can trace the PF and converge to the required EPO solution at a linear rate of convergence. Using EPO Search we develop new algorithms: PESA-EPO for approximating the PF in a posteriori MCDM, and GP-EPO for preference elicitation in interactive MCDM; experiments on benchmark datasets confirm their advantages over competing alternatives. EPO Search scales linearly with the number of decision variables which enables its use for training deep networks. Empirical results on real data from personalized medicine, e-commerce and hydrometeorology demonstrate the efficacy of EPO Search for deep MTL