Amarendra K Das

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While much health data is available online, patients who are not technically astute may be unable to access it because they may not know the relevant resources, they may be reluctant to confront an unfamiliar interface, and they may not know how to compose an answer from information provided by multiple heterogeneous resources. We describe ongoing research(More)
We present work on using a domain model to guide text interpretation, in the context of a project that aims to interpret English questions as a sequence of queries to be answered from structured databases. We adapt a broad-coverage and ambiguity-enabled natural language processing (NLP) system to produce domain-specific logical forms, using knowledge of the(More)
With the explosion of data in healthcare, there is a growing need to develop intelligent methods for automatically mining and implementing analyses from these data. In clinical applications, longitudinal patient records are often stored in disparate systems or locations in a non-integreted manner, adding work to providers and researchers to effectively(More)
We describe ongoing research using natural English text queries as an intelligent interface for inferring answers from structured data in a specific domain. Users can express queries whose answers need to be deduced from data in different databases, without knowing the structures of those databases nor even the existence of the sources used. Users can pose(More)
BACKGROUND Substance use-related communication for drug use promotion and its prevention is widely prevalent on social media. Social media big data involve naturally occurring communication phenomena that are observable through social media platforms, which can be used in computational or scalable solutions to generate data-driven inferences. Despite the(More)
We present preliminary work on an intelligent interface for answering English language clinical queries. Although our approach is domain independent, we focus on the needs of clinical researchers who are identifying cohorts of patients based on HIV drug-resistance patterns. Such questions are transformed into an unambiguous logical form by natural language(More)
BACKGROUND Providing patients with recordings of their clinic visits enhances patient and family engagement, yet few organizations routinely offer recordings. Challenges exist for organizations and patients, including data safety and navigating lengthy recordings. A secure system that allows patients to easily navigate recordings may be a solution. (More)
The development of anti-biotic resistant strains of bacteria and the nearly annual emergence of new strains of influenza virus are evidence of the rapid adaptation of pathogens to environmental pressures. The ability to predict the outcome of a long-term host-pathogen interaction could significantly improve public health decisions. Starting from the premise(More)
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