posted by organizer: yuyiqin || 828 views || tracked by 4 users: [display]

AI4CDM 2019 : Chronic Disease Management in the AI Era

FacebookTwitterLinkedInGoogle

 
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: https://yuyiqin2019.github.io/
Submission: https://www.easychair.org/conferences/?conf=ichi2019

Related Resources

COVID19_Book 2020   Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19 (Elsevier book)
ADRW 2021   «Archives during rebellions and wars». From the age of Napoleon to the cyber war era
KSEM 2021   Knowledge Science, Engineering and Management
PARMA-DITAM 2021   PARMA-DITAM: 12th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures & 10th Workshop on Design Tools and Architectures for Multi-Core
20th ICTEL October, Dubai 2021   20th ICTEL 2021 – International Conference on Teaching, Education & Learning, 23-24 October, Dubai
Fintech 2020   Sustainaility (Q2): Fintech: Recent Advancements in Modern Techniques, Methods and Real-World Solutions
17th ICTEL Rome 2021   17th ICTEL 2021 – International Conference on Teaching, Education & Learning, 06-07 September, Rome
AI, Big Data & Multimedia for COVID 2020   MTAP (Q2): Pioneering AI, Data Science and Multimedia Techniques and Findings for COVID-19
TERA - Eurasia Research 2021   19th ICTEL 2021 – International Conference on Teaching, Education & Learning, 11-12 October, Lisbon
Online Live September, London 2021   15th ICTEL 2021 – International Conference on Teaching, Education & Learning, 06-07 September, London