TY - JOUR
T1 - Humane visual AI
T2 - Telling the stories behind a medical condition
AU - So, Wonyoung
AU - Bogucka, Edyta P.
AU - Scepanovic, Sanja
AU - Joglekar, Sagar
AU - Zhou, Ke
AU - Quercia, Daniele
N1 - Publisher Copyright:
© 1995-2012 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/2
Y1 - 2021/2
N2 - A biological understanding is key for managing medical conditions, yet psychological and social aspects matter too. The main problem is that these two aspects are hard to quantify and inherently difficult to communicate. To quantify psychological aspects, this work mined around half a million Reddit posts in the sub-communities specialised in 14 medical conditions, and it did so with a new deep-learning framework. In so doing, it was able to associate mentions of medical conditions with those of emotions. To then quantify social aspects, this work designed a probabilistic approach that mines open prescription data from the National Health Service in England to compute the prevalence of drug prescriptions, and to relate such a prevalence to census data. To finally visually communicate each medical condition's biological, psychological, and social aspects through storytelling, we designed a narrative-style layered Martini Glass visualization. In a user study involving 52 participants, after interacting with our visualization, a considerable number of them changed their mind on previously held opinions: 10% gave more importance to the psychological aspects of medical conditions, and 27% were more favourable to the use of social media data in healthcare, suggesting the importance of persuasive elements in interactive visualizations.
AB - A biological understanding is key for managing medical conditions, yet psychological and social aspects matter too. The main problem is that these two aspects are hard to quantify and inherently difficult to communicate. To quantify psychological aspects, this work mined around half a million Reddit posts in the sub-communities specialised in 14 medical conditions, and it did so with a new deep-learning framework. In so doing, it was able to associate mentions of medical conditions with those of emotions. To then quantify social aspects, this work designed a probabilistic approach that mines open prescription data from the National Health Service in England to compute the prevalence of drug prescriptions, and to relate such a prevalence to census data. To finally visually communicate each medical condition's biological, psychological, and social aspects through storytelling, we designed a narrative-style layered Martini Glass visualization. In a user study involving 52 participants, after interacting with our visualization, a considerable number of them changed their mind on previously held opinions: 10% gave more importance to the psychological aspects of medical conditions, and 27% were more favourable to the use of social media data in healthcare, suggesting the importance of persuasive elements in interactive visualizations.
KW - AI
KW - complex problem communication
KW - healthcare
KW - Martini Glass structure
KW - social media data
KW - storytelling
UR - http://www.scopus.com/inward/record.url?scp=85100422295&partnerID=8YFLogxK
U2 - 10.1109/TVCG.2020.3030391
DO - 10.1109/TVCG.2020.3030391
M3 - Article
AN - SCOPUS:85100422295
SN - 1077-2626
VL - 27
SP - 678
EP - 688
JO - IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
JF - IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
IS - 2
M1 - 9222264
ER -