C Raina MacIntyre


2019

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Does Multi-Task Learning Always Help?: An Evaluation on Health Informatics
Aditya Joshi | Sarvnaz Karimi | Ross Sparks | Cecile Paris | C Raina MacIntyre
Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association

Multi-Task Learning (MTL) has been an attractive approach to deal with limited labeled datasets or leverage related tasks, for a variety of NLP problems. We examine the benefit of MTL for three specific pairs of health informatics tasks that deal with: (a) overlapping symptoms for the same classification problem (personal health mention classification for influenza and for a set of symptoms); (b) overlapping medical concepts for related classification problems (vaccine usage and drug usage detection); and, (c) related classification problems (vaccination intent and vaccination relevance detection). We experiment with a simple neural architecture: a shared layer followed by task-specific dense layers. The novelty of this work is that it compares alternatives for shared layers for these pairs of tasks. While our observations agree with the promise of MTL as compared to single-task learning, for health informatics, we show that the benefit also comes with caveats in terms of the choice of shared layers and the relatedness between the participating tasks.

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A Comparison of Word-based and Context-based Representations for Classification Problems in Health Informatics
Aditya Joshi | Sarvnaz Karimi | Ross Sparks | Cecile Paris | C Raina MacIntyre
Proceedings of the 18th BioNLP Workshop and Shared Task

Distributed representations of text can be used as features when training a statistical classifier. These representations may be created as a composition of word vectors or as context-based sentence vectors. We compare the two kinds of representations (word versus context) for three classification problems: influenza infection classification, drug usage classification and personal health mention classification. For statistical classifiers trained for each of these problems, context-based representations based on ELMo, Universal Sentence Encoder, Neural-Net Language Model and FLAIR are better than Word2Vec, GloVe and the two adapted using the MESH ontology. There is an improvement of 2-4% in the accuracy when these context-based representations are used instead of word-based representations.

2018

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Shot Or Not: Comparison of NLP Approaches for Vaccination Behaviour Detection
Aditya Joshi | Xiang Dai | Sarvnaz Karimi | Ross Sparks | Cécile Paris | C Raina MacIntyre
Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task

Vaccination behaviour detection deals with predicting whether or not a person received/was about to receive a vaccine. We present our submission for vaccination behaviour detection shared task at the SMM4H workshop. Our findings are based on three prevalent text classification approaches: rule-based, statistical and deep learning-based. Our final submissions are: (1) an ensemble of statistical classifiers with task-specific features derived using lexicons, language processing tools and word embeddings; and, (2) a LSTM classifier with pre-trained language models.

2017

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ZikaHack 2016: A digital disease detection competition
Dillon C Adam | Jitendra Jonnagaddala | Daniel Han-Chen | Sean Batongbacal | Luan Almeida | Jing Z Zhu | Jenny J Yang | Jumail M Mundekkat | Steven Badman | Abrar Chughtai | C Raina MacIntyre
Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)

Effective response to infectious diseases outbreaks relies on the rapid and early detection of those outbreaks. Invalidated, yet timely and openly available digital information can be used for the early detection of outbreaks. Public health surveillance authorities can exploit these early warnings to plan and co-ordinate rapid surveillance and emergency response programs. In 2016, a digital disease detection competition named ZikaHack was launched. The objective of the competition was for multidisciplinary teams to design, develop and demonstrate innovative digital disease detection solutions to retrospectively detect the 2015-16 Brazilian Zika virus outbreak earlier than traditional surveillance methods. In this paper, an overview of the ZikaHack competition is provided. The challenges and lessons learned in organizing this competition are also discussed for use by other researchers interested in organizing similar competitions.