@inproceedings{pant-etal-2025-health,
title = "Health Sentinel: An {AI} Pipeline For Real-time Disease Outbreak Detection",
author = "Pant, Devesh and
Grandhe, Rishi Raj and
Agrawal, Jatin and
Singh Kalra, Jushaan and
Kumar, Sudhir and
Khanna, Saransh and
Samaria, Vipin and
Paul, Mukul and
Khalikar, Dr. Satish V and
Garg, Vipin and
Chauhan, Dr. Himanshu and
Verma, Dr. Pranay and
Vssg, Akhil and
Khandelwal, Neha and
Dhavala, Soma S and
Mathew, Minesh",
editor = "Atwell, Katherine and
Biester, Laura and
Borah, Angana and
Dementieva, Daryna and
Ignat, Oana and
Kotonya, Neema and
Liu, Ziyi and
Wan, Ruyuan and
Wilson, Steven and
Zhao, Jieyu",
booktitle = "Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.nlp4pi-1.3/",
doi = "10.18653/v1/2025.nlp4pi-1.3",
pages = "23--42",
ISBN = "978-1-959429-19-7",
abstract = "Early detection of disease outbreaks is crucial to ensure timely intervention by the health authorities. Due to the challenges associated with traditional indicator-based surveillance, monitoring informal sources such as online media has become increasingly popular. However, owing to the number of online articles getting published everyday, manual screening of the articles is impractical. To address this, we propose Health Sentinel. It is a multi-stage information extraction pipeline that uses a combination of ML and non-ML methods to extract events{--}structured information concerning disease outbreaks or other unusual health events{--}from online articles. The extracted events are made available to the Media Scanning and Verification Cell (MSVC) at the National Centre for Disease Control (NCDC), Delhi for analysis, interpretation and further dissemination to local agencies for timely intervention. From April 2022 till date, Health Sentinel has processed over 300 million news articles and identified over 95,000 unique health events across India of which over 3,500 events were shortlisted by the public health experts at NCDC as potential outbreaks."
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<abstract>Early detection of disease outbreaks is crucial to ensure timely intervention by the health authorities. Due to the challenges associated with traditional indicator-based surveillance, monitoring informal sources such as online media has become increasingly popular. However, owing to the number of online articles getting published everyday, manual screening of the articles is impractical. To address this, we propose Health Sentinel. It is a multi-stage information extraction pipeline that uses a combination of ML and non-ML methods to extract events–structured information concerning disease outbreaks or other unusual health events–from online articles. The extracted events are made available to the Media Scanning and Verification Cell (MSVC) at the National Centre for Disease Control (NCDC), Delhi for analysis, interpretation and further dissemination to local agencies for timely intervention. From April 2022 till date, Health Sentinel has processed over 300 million news articles and identified over 95,000 unique health events across India of which over 3,500 events were shortlisted by the public health experts at NCDC as potential outbreaks.</abstract>
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%0 Conference Proceedings
%T Health Sentinel: An AI Pipeline For Real-time Disease Outbreak Detection
%A Pant, Devesh
%A Grandhe, Rishi Raj
%A Agrawal, Jatin
%A Singh Kalra, Jushaan
%A Kumar, Sudhir
%A Khanna, Saransh
%A Samaria, Vipin
%A Paul, Mukul
%A Khalikar, Dr. Satish V.
%A Garg, Vipin
%A Chauhan, Dr. Himanshu
%A Verma, Dr. Pranay
%A Vssg, Akhil
%A Khandelwal, Neha
%A Dhavala, Soma S.
%A Mathew, Minesh
%Y Atwell, Katherine
%Y Biester, Laura
%Y Borah, Angana
%Y Dementieva, Daryna
%Y Ignat, Oana
%Y Kotonya, Neema
%Y Liu, Ziyi
%Y Wan, Ruyuan
%Y Wilson, Steven
%Y Zhao, Jieyu
%S Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 978-1-959429-19-7
%F pant-etal-2025-health
%X Early detection of disease outbreaks is crucial to ensure timely intervention by the health authorities. Due to the challenges associated with traditional indicator-based surveillance, monitoring informal sources such as online media has become increasingly popular. However, owing to the number of online articles getting published everyday, manual screening of the articles is impractical. To address this, we propose Health Sentinel. It is a multi-stage information extraction pipeline that uses a combination of ML and non-ML methods to extract events–structured information concerning disease outbreaks or other unusual health events–from online articles. The extracted events are made available to the Media Scanning and Verification Cell (MSVC) at the National Centre for Disease Control (NCDC), Delhi for analysis, interpretation and further dissemination to local agencies for timely intervention. From April 2022 till date, Health Sentinel has processed over 300 million news articles and identified over 95,000 unique health events across India of which over 3,500 events were shortlisted by the public health experts at NCDC as potential outbreaks.
%R 10.18653/v1/2025.nlp4pi-1.3
%U https://aclanthology.org/2025.nlp4pi-1.3/
%U https://doi.org/10.18653/v1/2025.nlp4pi-1.3
%P 23-42
Markdown (Informal)
[Health Sentinel: An AI Pipeline For Real-time Disease Outbreak Detection](https://aclanthology.org/2025.nlp4pi-1.3/) (Pant et al., NLP4PI 2025)
ACL
- Devesh Pant, Rishi Raj Grandhe, Jatin Agrawal, Jushaan Singh Kalra, Sudhir Kumar, Saransh Khanna, Vipin Samaria, Mukul Paul, Dr. Satish V Khalikar, Vipin Garg, Dr. Himanshu Chauhan, Dr. Pranay Verma, Akhil Vssg, Neha Khandelwal, Soma S Dhavala, and Minesh Mathew. 2025. Health Sentinel: An AI Pipeline For Real-time Disease Outbreak Detection. In Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI), pages 23–42, Vienna, Austria. Association for Computational Linguistics.