Jitendra Jonnagaddala


2017

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Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)
Jitendra Jonnagaddala | Hong-Jie Dai | Yung-Chun Chang
Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)

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Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms
Yi-Jie Huang | Chu Hsien Su | Yi-Chun Chang | Tseng-Hsin Ting | Tzu-Yuan Fu | Rou-Min Wang | Hong-Jie Dai | Yung-Chun Chang | Jitendra Jonnagaddala | Wen-Lian Hsu
Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)

The increasing popularity of social media lead users to share enormous information on the internet. This information has various application like, it can be used to develop models to understand or predict user behavior on social media platforms. For example, few online retailers have studied the shopping patterns to predict shopper’s pregnancy stage. Another interesting application is to use the social media platforms to analyze users’ health-related information. In this study, we developed a tree kernel-based model to classify tweets conveying pregnancy related information using this corpus. The developed pregnancy classification model achieved an accuracy of 0.847 and an F-score of 0.565. A new corpus from popular social media platform Twitter was developed for the purpose of this study. In future, we would like to improve this corpus by reducing noise such as retweets.

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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.

2016

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Combining Multiple Classifiers Using Global Ranking for ReachOut.com Post Triage
Chen-Kai Wang | Hong-Jie Dai | Chih-Wei Chen | Jitendra Jonnagaddala | Nai-Wen Chang
Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology

2015

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TMUNSW: Identification of Disorders and Normalization to SNOMED-CT Terminology in Unstructured Clinical Notes
Jitendra Jonnagaddala | Siaw-Teng Liaw | Pradeep Ray | Manish Kumar | Hong-Jie Dai
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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A preliminary study on automatic identification of patient smoking status in unstructured electronic health records
Jitendra Jonnagaddala | Hong-Jie Dai | Pradeep Ray | Siaw-Teng Liaw
Proceedings of BioNLP 15

2014

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TMUNSW: Disorder Concept Recognition and Normalization in Clinical Notes for SemEval-2014 Task 7
Jitendra Jonnagaddala | Manish Kumar | Hong-Jie Dai | Enny Rachmani | Chien-Yeh Hsu
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)