Interpretable Categorization of Heterogeneous Time Series Data

Understanding heterogeneous multivariate time series data is important in many applications ranging from smart homes to aviation. Learning models of heterogeneous multivariate time series that are also human-interpretable is challenging and not adequately addressed by the existing literature. We propose grammar-based decision trees (GBDTs) and an algorithm for learning them. GBDTs extend decision trees with a grammar framework. Logical expressions derived from a context-free grammar are used for branching in place of simple thresholds on attributes. The added expressivity enables support for a wide range of data types while retaining the interpretability of decision trees. In particular, when a grammar based on temporal logic is used, we show that GBDTs can be used for the interpretable classi cation of high-dimensional and heterogeneous time series data. Furthermore, we show how GBDTs can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply GBDTs to analyze the classic Australian Sign Language dataset as well as data on near mid-air collisions (NMACs). The NMAC data comes from aircraft simulations used in the development of the next-generation Airborne Collision Avoidance System (ACAS X).Comment: 9 pages, 5 figures, 2 tables, SIAM International Conference on Data Mining (SDM) 201

POEMS Syndrome: Real World Experience in Diagnosis and Systemic Therapy - 108 Patients Multicenter Analysis

POEMS syndrome, a rare plasma cell disorder, is challenging both in the diagnostic and therapeutic management. We present real word retrospective analysis of 108 cases analyzing clinical features and therapeutic modes. We compare our results with the available literature. This is the first description with such wide use of proteasome inhibitors in first line treatment. POEMS (Polyneuropathy, organomegaly, endocrinopathy, M-protein, skin changes) syndrome is a rare and challenging plasma cell disorder, both in the diagnostic and therapeutic management of the disease. Currently, the literature on POEMS is sparse with most evidence being case reports and small case studies. We present a retrospective real world experience of 108 patients with POEMS. We analyzed the clinical features and therapeutic interventions. Regarding clinical features, our findings demonstrated that skin lesions, thrombocythemia and polycythemia were present less frequently than reported previously. Regarding clinical interventions, this is one of the largest analyses of front line treatment in POEMS and the first one to include frequent utilization of proteasome inhibitors (37%). Bortezomib monotherapy was the most effective therapy achieving complete remission/very good partial remissions (CR/VGPR) in 69% of patients. Thirty percent of patients proceeded to planned autologous stem cell transplant (ASCT) as part of the front-line treatment resulting in statistically superior progression-free (PFS) and overall survival (OS) compared to non-ASCT treated patients (P= .003). In multivariate analysis, anemia, thrombocytopenia, and as age over 60 were associated with a negative impact on patient outcomes

Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning

Finding the most likely path to a set of failure states is important to the analysis of safety-critical systems that operate over a sequence of time steps, such as aircraft collision avoidance systems and autonomous cars. In many applications such as autonomous driving, failures cannot be completely eliminated due to the complex stochastic environment in which the system operates. As a result, safety validation is not only concerned about whether a failure can occur, but also discovering which failures are most likely to occur. This article presents adaptive stress testing (AST), a framework for finding the most likely path to a failure event in simulation. We consider a general black box setting for partially observable and continuous-valued systems operating in an environment with stochastic disturbances. We formulate the problem as a Markov decision process and use reinforcement learning to optimize it. The approach is simulation-based and does not require internal knowledge of the system, making it suitable for black-box testing of large systems. We present different formulations depending on whether the state is fully observable or partially observable. In the latter case, we present a modified Monte Carlo tree search algorithm that only requires access to the pseudorandom number generator of the simulator to overcome partial observability. We also present an extension of the framework, called differential adaptive stress testing (DAST), that can find failures that occur in one system but not in another. This type of differential analysis is useful in applications such as regression testing, where we are concerned with finding areas of relative weakness compared to a baseline. We demonstrate the effectiveness of the approach on an aircraft collision avoidance application, where a prototype aircraft collision avoidance system is stress tested to find the most likely scenarios of near mid-air collision