TY - JOUR

T1 - Unified approach to retrospective event detection for event- based epidemic intelligence

AU - Fisichella, Marco

N1 - Funding Information: The work was partially funded by the European Commission for the eXplainable Artificial Intelligence in healthcare Management (xAIM) project, agreement No INEA/CEF/ICT/A2020/2276680.

PY - 2021/12

Y1 - 2021/12

N2 - Inferring the magnitude and occurrence of real-world events from natural language text is a crucial task in various domains. Particularly in the domain of public health, the state-of-the-art document and token centric event detection approaches have not kept the pace with the growing need for more robust event detection in public health. In this paper, we propose UPHED, a unified approach, which combines both the document and token centric event detection techniques in an unsupervised manner such that events which are: rare (aperiodic); reoccurring (periodic) can be detected using a generative model for the domain of public health. We evaluate the efficiency of our approach as well as its effectiveness for two real-world case studies with respect to the quality of document clusters. Our results show that we are able to achieve a precision of 60% and a recall of 71% analyzed using manually annotated real-world data. Finally, we also make a comparative analysis of our work with the well-established rule-based system of MedISys and find that UPHED can be used in a cooperative way with MedISys to not only detect similar anomalies, but can also deliver more information about the specific outbreak of reported diseases.

AB - Inferring the magnitude and occurrence of real-world events from natural language text is a crucial task in various domains. Particularly in the domain of public health, the state-of-the-art document and token centric event detection approaches have not kept the pace with the growing need for more robust event detection in public health. In this paper, we propose UPHED, a unified approach, which combines both the document and token centric event detection techniques in an unsupervised manner such that events which are: rare (aperiodic); reoccurring (periodic) can be detected using a generative model for the domain of public health. We evaluate the efficiency of our approach as well as its effectiveness for two real-world case studies with respect to the quality of document clusters. Our results show that we are able to achieve a precision of 60% and a recall of 71% analyzed using manually annotated real-world data. Finally, we also make a comparative analysis of our work with the well-established rule-based system of MedISys and find that UPHED can be used in a cooperative way with MedISys to not only detect similar anomalies, but can also deliver more information about the specific outbreak of reported diseases.

KW - Clustering

KW - Event-based epidemic intelligence

KW - Retrospective public health event detection

UR - http://www.scopus.com/inward/record.url?scp=85116791341&partnerID=8YFLogxK

U2 - 10.1007/s00799-021-00308-9

DO - 10.1007/s00799-021-00308-9

M3 - Article

AN - SCOPUS:85116791341

VL - 22

SP - 339

EP - 364

JO - International Journal on Digital Libraries

JF - International Journal on Digital Libraries

SN - 1432-5012

IS - 4

ER -