@inproceedings{romberg-etal-2020-annotating,
title = "Annotating Patient Information Needs in Online Diabetes Forums",
author = "Romberg, Julia and
Dyczmons, Jan and
Borgmann, Sandra Olivia and
Sommer, Jana and
Vomhof, Markus and
Brunoni, Cecilia and
Bruck-Ramisch, Ismael and
Enders, Luis and
Icks, Andrea and
Conrad, Stefan",
editor = "Gonzalez-Hernandez, Graciela and
Klein, Ari Z. and
Flores, Ivan and
Weissenbacher, Davy and
Magge, Arjun and
O'Connor, Karen and
Sarker, Abeed and
Minard, Anne-Lyse and
Tutubalina, Elena and
Miftahutdinov, Zulfat and
Alimova, Ilseyar",
booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.smm4h-1.3",
pages = "19--26",
abstract = "Identifying patient information needs is an important issue for health care services and implementation of patient-centered care. A relevant number of people with diabetes mellitus experience a need for information during the course of the disease. Health-related online forums are a promising option for researching relevant information needs closely related to everyday life. In this paper, we present a novel data corpus comprising 4,664 contributions from an online diabetes forum in German language. Two annotation tasks were implemented. First, the contributions were categorised according to whether they contain a diabetes-specific information need or not, which might either be a non diabetes-specific information need or no information need at all, resulting in an agreement of 0.89 (Krippendorff{'}s α). Moreover, the textual content of diabetes-specific information needs was segmented and labeled using a well-founded definition of health-related information needs, which achieved a promising agreement of 0.82 (Krippendorff{'}s αu). We further report a baseline for two sub-tasks of the information extraction system planned for the long term: contribution categorization and segment classification.",
}
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<abstract>Identifying patient information needs is an important issue for health care services and implementation of patient-centered care. A relevant number of people with diabetes mellitus experience a need for information during the course of the disease. Health-related online forums are a promising option for researching relevant information needs closely related to everyday life. In this paper, we present a novel data corpus comprising 4,664 contributions from an online diabetes forum in German language. Two annotation tasks were implemented. First, the contributions were categorised according to whether they contain a diabetes-specific information need or not, which might either be a non diabetes-specific information need or no information need at all, resulting in an agreement of 0.89 (Krippendorff’s α). Moreover, the textual content of diabetes-specific information needs was segmented and labeled using a well-founded definition of health-related information needs, which achieved a promising agreement of 0.82 (Krippendorff’s αu). We further report a baseline for two sub-tasks of the information extraction system planned for the long term: contribution categorization and segment classification.</abstract>
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%0 Conference Proceedings
%T Annotating Patient Information Needs in Online Diabetes Forums
%A Romberg, Julia
%A Dyczmons, Jan
%A Borgmann, Sandra Olivia
%A Sommer, Jana
%A Vomhof, Markus
%A Brunoni, Cecilia
%A Bruck-Ramisch, Ismael
%A Enders, Luis
%A Icks, Andrea
%A Conrad, Stefan
%Y Gonzalez-Hernandez, Graciela
%Y Klein, Ari Z.
%Y Flores, Ivan
%Y Weissenbacher, Davy
%Y Magge, Arjun
%Y O’Connor, Karen
%Y Sarker, Abeed
%Y Minard, Anne-Lyse
%Y Tutubalina, Elena
%Y Miftahutdinov, Zulfat
%Y Alimova, Ilseyar
%S Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F romberg-etal-2020-annotating
%X Identifying patient information needs is an important issue for health care services and implementation of patient-centered care. A relevant number of people with diabetes mellitus experience a need for information during the course of the disease. Health-related online forums are a promising option for researching relevant information needs closely related to everyday life. In this paper, we present a novel data corpus comprising 4,664 contributions from an online diabetes forum in German language. Two annotation tasks were implemented. First, the contributions were categorised according to whether they contain a diabetes-specific information need or not, which might either be a non diabetes-specific information need or no information need at all, resulting in an agreement of 0.89 (Krippendorff’s α). Moreover, the textual content of diabetes-specific information needs was segmented and labeled using a well-founded definition of health-related information needs, which achieved a promising agreement of 0.82 (Krippendorff’s αu). We further report a baseline for two sub-tasks of the information extraction system planned for the long term: contribution categorization and segment classification.
%U https://aclanthology.org/2020.smm4h-1.3
%P 19-26
Markdown (Informal)
[Annotating Patient Information Needs in Online Diabetes Forums](https://aclanthology.org/2020.smm4h-1.3) (Romberg et al., SMM4H 2020)
ACL
- Julia Romberg, Jan Dyczmons, Sandra Olivia Borgmann, Jana Sommer, Markus Vomhof, Cecilia Brunoni, Ismael Bruck-Ramisch, Luis Enders, Andrea Icks, and Stefan Conrad. 2020. Annotating Patient Information Needs in Online Diabetes Forums. In Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task, pages 19–26, Barcelona, Spain (Online). Association for Computational Linguistics.