Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction Mention Extraction

Social media is an useful platform to share health-related information due to its vast reach. This makes it a good candidate for public-health monitoring tasks, specifically for pharmacovigilance. We study the problem of extraction of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from twitter. Medical information extraction from social media is challenging, mainly due to short and highly information nature of text, as compared to more technical and formal medical reports. Current methods in ADR mention extraction relies on supervised learning methods, which suffers from labeled data scarcity problem. The State-of-the-art method uses deep neural networks, specifically a class of Recurrent Neural Network (RNN) which are Long-Short-Term-Memory networks (LSTMs) \cite{hochreiter1997long}. Deep neural networks, due to their large number of free parameters relies heavily on large annotated corpora for learning the end task. But in real-world, it is hard to get large labeled data, mainly due to heavy cost associated with manual annotation. Towards this end, we propose a novel semi-supervised learning based RNN model, which can leverage unlabeled data also present in abundance on social media. Through experiments we demonstrate the effectiveness of our method, achieving state-of-the-art performance in ADR mention extraction.Comment: Accepted at DTMBIO workshop, CIKM 2017. To appear in BMC Bioinformatics. Pls cite that versio

A real-world evidence study to evaluate the efficacy of software-driven digital therapeutics on major adverse cardiovascular events, vitals, adherence to medication and lifestyle changes among patients with coronary artery disease or post-coronary interventions

Background: Coronary artery disease (CAD), a leading cause of cardiovascular disease (CVD) mortality worldwide, is a major health concern in India due to the high rates of the disease. Acute coronary syndrome (ACS) is a prevalent form of CAD that requires prompt treatment. Digital therapeutics (DTx) is an emerging field that employs remote monitoring and behavioural changes to manage diseases, with promising outcomes in ACS and post-percutaneous coronary intervention (PCI) patients. This study evaluates the efficacy of a software-driven DTx intervention in enhancing outcomes for CAD patients. Methods: This pilot, single-centred, prospective and real-world evidence cohort study aims to evaluate the effectiveness of a software-driven therapeutic intervention (LYFE) in patients with ACS and/or post-PCI. The study enrolled 30 patients over a 3-month follow-up period from October to November 2022. The main outcomes measured were changes in blood pressure, heart rate, medication adherence, the incidence of major adverse cardiovascular events (MACE), all-cause readmission, and lifestyle adherence at 1 and 3 months. Results: The mean age of the patients was 53.2±12.1 years; 27 (93%) males and 2 (7%) were females. Mean BMI of the patients was 26.3±5.0. The mean difference for systolic blood pressure (SBP) and diastolic blood pressure (DBP) was 7.8±10.9 (p=0.001*), 3.7±5.7 (p=0.002*) respectively with statistically significant reduction, at 3 months. The 25 (83.3%) patients had controlled blood pressure at 3 months. 27 (90%) patients were adherent to the medication and physically active, while 3 (10%) inactive throughout the study period. No CVD death/major bleeding event was reported. Conclusions: DTx improved medication adherence and blood pressure control in CAD, ACS with post-PCI patients during the study period.

Representation of Collaborative Search Results Using Faceted Search

This paper describes the design and implementation of a web-based, faceted interface for searching and displaying web pages saved from collaborative information seeking using the Results Space framework. Results Space project is part of Interaction Design Lab at the School of Information and Library Science at the University of North Carolina at Chapel Hill. The Results Space project focuses on managing search results across multiple sessions and multiple collaborators. This paper describes the implementation of the web-interface that enables presentation of these collaborative results using faceted search. Once a user has worked on any collaborative project she needs to view and interact with the results. An ability to view the results across multiple facets like projects, collaborators and sources provides the user with a better depiction of the search efforts. This functionality can be further enhanced using different representations in which the user can view the search results. This paper discusses the process of developing a web application that provides such faceted search interface and representation of the search results using timeline and table view