Prateek Jindal

Extraction of events and temporal expressions from clinical narratives

This paper addresses an important task of event and timex extraction from clinical narratives in context of the i2b2 2012 challenge. State-of-the-art approaches for event extraction use a multi-class classifier for finding the event types. However, such approaches consider each event in isolation. In this paper, we present a sentence-level inference strategy which enforces consistency constraints on attributes of those events which appear close to one another. Our approach is general and can be used for other tasks as well. We also design novel features like clinical descriptors (from medical ontologies) which encode a lot of useful information about the concepts. For timex extraction, we adapt a state-of-the-art system, HeidelTime, for use in clinical narratives and also develop several rules which complement HeidelTime. We also give a robust algorithm for date extraction. For the event extraction task, we achieved an overall F1 score of 0.71 for determining span of the events along with their attributes. For the timex extraction task, we achieved an F1 score of 0.79 for determining span of the temporal expressions. We present detailed error analysis of our system and also point out some factors which can help to improve its accuracy.

Argobots: A Lightweight Low-Level Threading and Tasking Framework

In the past few decades, a number of user-level threading and tasking models have been proposed in the literature to address the shortcomings of OS-level threads, primarily with respect to cost and flexibility. Current state-of-the-art user-level threading and tasking models, however, either are too specific to applications or architectures or are not as powerful or flexible. In this paper, we present Argobots, a lightweight, low-level threading and tasking framework that is designed as a portable and performant substrate for high-level programming models or runtime systems. Argobots offers a carefully designed execution model that balances generality of functionality with providing a rich set of controls to allow specialization by end users or high-level programming models. We describe the design, implementation, and performance characterization of Argobots and present integrations with three high-level models: OpenMP, MPI, and colocated I/O services. Evaluations show that (1) Argobots, while providing richer capabilities, is competitive with existing simpler generic threading runtimes; (2) our OpenMP runtime offers more efficient interoperability capabilities than production OpenMP runtimes do; (3) when MPI interoperates with Argobots instead of Pthreads, it enjoys reduced synchronization costs and better latency-hiding capabilities; and (4) I/O services with Argobots reduce interference with colocated applications while achieving performance competitive with that of a Pthreads approach.

OpenAtom: Scalable Ab-Initio Molecular Dynamics with Diverse Capabilities

The complex interplay of tightly coupled, but disparate, computation and communication operations poses several challenges for simulating atomic scale dynamics on multi-petaflops architectures. OpenAtom addresses these challenges by exploiting overdecomposition and asynchrony in Charm++, and scales to thousands of cores for realistic scientific systems with only a few hundred atoms. At the same time, it supports several interesting ab-initio molecular dynamics simulation methods including the Car-Parrinello method, Born-Oppenheimer method, k-points, parallel tempering, and path integrals. This paper showcases the diverse functionalities as well as scalability of OpenAtom via performance case studies, with focus on the recent additions and improvements to OpenAtom. In particular, we study a metal organic framework (MOF) that consists of 424 atoms and is being explored as a candidate for a hydrogen storage material. Simulations of this system are scaled to large core counts on Cray XE6 and IBM Blue Gene/Q systems, and time per step as low as \(1.7\,s\) is demonstrated for simulating path integrals with 32-beads of MOF on 262,144 cores of Blue Gene/Q.

Detecting privacy-sensitive events in medical text

In this paper, we present a novel semi-supervised technique for finding privacy-sensitive events in clinical text. Unlike traditional semi-supervised methods, we do not require large amounts of unannotated data. Instead, our approach relies on information contained in the hierarchical structure of a large medical encyclopedia.