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AI4CDM 2019 : Chronic Disease Management in the AI Era


When Jun 10, 2019 - Jun 10, 2019
Where Beijing, China
Abstract Registration Due Mar 1, 2019
Submission Deadline Mar 15, 2019
Notification Due Apr 12, 2019
Final Version Due May 3, 2019

Call For Papers

Chronic diseases, such as Diabetes, Cardiovascular diseases, hypertension have specific characteristics including increasing incidence rate, long term intervention requirement and multi-disease complications/comorbidities. Chronic Disease Management (CDM) is a series of actions designed to manage or prevent a chronic condition using a systematic approach to care and potentially employing multiple treatment modalities. Due to the internal and external complexity factors of managing chronic conditions, such as how to enable early risk identification of conditions, how to build precision medicine and decision support process, and how to support patient management at scale. With the emerging of advanced Artificial Intelligence (AI) technologies, they are becoming promising means to deal with the challenges among CDM practices. This workshop tries to discuss whether and how AI technologies can address challenges among the whole chronic disease management cycle. To concentrate, it will focus on Diabetes, and touch topics about the prevention, diagnosis, treatment, prognosis and engagement of Diabetes.

We are inviting original research submissions as well as ongoing research works, including Regular Papers (8-10 pages), Short Papers (4-6 pages), and Posters (1-2 pages). All the accepted submissions will be appeared in the conference proceedings published by IEEE and will be made available via IEEE Xplore.

• Health Data Processing, including big data technologies for processing heterogeneous health data such as medical data, image data, sensor data, behavior data, signal data and genetic data.
• Machine Learning and Deep Learning, including applications of machine learning and deep learning in longitudinal data modeling, medical text analysis, and medical image analysis for predictive modeling, disease progression modeling, and medication recommendation.
• Natural Language Processing, including but not limited to the following areas: named entity recognition, word sense disambiguation, relation extraction, syntactic parsing, semantic role labeling, topic modeling, and discourse analysis.
• Knowledge Discovery and Representation, including semantic annotation on healthcare data, semantic reasoning and inference and graph based knowledge representation.
• Human-Computer Interaction, including data/model visualization, dialog-based or communication-based learning and advanced question answering.

Workshop Website:

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