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        <title>Extracting Personal Medical Events for User Timeline Construction using Minimal Supervision</title>
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        <namePart type="given">Aakanksha</namePart>
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    <abstract>In this paper, we describe a system for automatic construction of user disease progression timelines from their posts in online support groups using minimal supervision. In recent years, several online support groups have been established which has led to a huge increase in the amount of patient-authored text available. Creating systems which can automatically extract important medical events and create disease progression timelines for users from such text can help in patient health monitoring as well as studying links between medical events and users’ participation in support groups. Prior work in this domain has used manually constructed keyword sets to detect medical events. In this work, our aim is to perform medical event detection using minimal supervision in order to develop a more general timeline construction system. Our system achieves an accuracy of 55.17%, which is 92% of the performance achieved by a supervised baseline system.</abstract>
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%0 Conference Proceedings
%T Extracting Personal Medical Events for User Timeline Construction using Minimal Supervision
%A Naik, Aakanksha
%A Bogart, Chris
%A Rose, Carolyn
%Y Cohen, Kevin Bretonnel
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S BioNLP 2017
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada,
%F naik-etal-2017-extracting
%X In this paper, we describe a system for automatic construction of user disease progression timelines from their posts in online support groups using minimal supervision. In recent years, several online support groups have been established which has led to a huge increase in the amount of patient-authored text available. Creating systems which can automatically extract important medical events and create disease progression timelines for users from such text can help in patient health monitoring as well as studying links between medical events and users’ participation in support groups. Prior work in this domain has used manually constructed keyword sets to detect medical events. In this work, our aim is to perform medical event detection using minimal supervision in order to develop a more general timeline construction system. Our system achieves an accuracy of 55.17%, which is 92% of the performance achieved by a supervised baseline system.
%R 10.18653/v1/W17-2346
%U https://aclanthology.org/W17-2346
%U https://doi.org/10.18653/v1/W17-2346
%P 356-364

Markdown (Informal)

[Extracting Personal Medical Events for User Timeline Construction using Minimal Supervision](https://aclanthology.org/W17-2346) (Naik et al., BioNLP 2017)

• Extracting Personal Medical Events for User Timeline Construction using Minimal Supervision (Naik et al., BioNLP 2017)

ACL

• Aakanksha Naik, Chris Bogart, and Carolyn Rose. 2017. Extracting Personal Medical Events for User Timeline Construction using Minimal Supervision. In BioNLP 2017, pages 356–364, Vancouver, Canada,. Association for Computational Linguistics.