International Workshop on Knowledge Discovery in Healthcare Data
KDH-2020
- URL: https://sites.google.com/view/kdh-2020
- Event Date: 2020-06-09 ~ 2020-06-09
- Submission Date: 2020-03-06
- Location: Santiago, Philippines
There are many healthcare datasets consisting of both structured and unstructured information, which provide a challenge for artificial intelligence and machine learning researchers seeking to extract knowledge from data. Existing healthcare datasets include electronic medical records, large collections of complex physiological information, medical imaging data, genomics, as well as other socio-economic and behavioral data. In order to perform data-driven analysis or build causal and inferential models using these datasets, challenges such as integrating multiple data types, dealing with missing data, and handling irregularly sampled data need to be addressed. While these challenges must be considered by researchers working with healthcare data, a larger problem involves how to best ensure that the hypotheses posed and types of knowledge discoveries sought are relevant to the healthcare community. Clinical perspectives from medical professionals are required to ensure that advancements in healthcare data analysis result in positive impact to point-of-care and outcome-based systems.
This workshop builds upon the success of previous Knowledge Discovery in Healthcare Data (KDH) workshops. It welcomes contributions providing insight on the extent to which AI techniques have successfully penetrated the healthcare field, interaction among AI techniques to achieve successful learning healthcare systems, and distinctions between AI and non-AI models needed in modern healthcare environments. The focus of the workshop is on issues in data extraction and assembly, knowledge discovery, decision support for healthcare providers, and personalised self-care aids for patients. A workshop highlight will be the Blood Glucose Level Prediction (BGLP) Challenge, in which researchers will compare the efficacy of different machine learning prediction approaches on a standard set of data from patients with type 1 diabetes.
Main Topics:
- Knowledge discovery and data analytics
- Data extraction, organization and assembly
- Personalisation and decision support
- Blood glucose level prediction
A full list of topics and submission guidelines can be found at: https://sites.google.com/view/kdh-2020
This workshop builds upon the success of previous Knowledge Discovery in Healthcare Data (KDH) workshops. It welcomes contributions providing insight on the extent to which AI techniques have successfully penetrated the healthcare field, interaction among AI techniques to achieve successful learning healthcare systems, and distinctions between AI and non-AI models needed in modern healthcare environments. The focus of the workshop is on issues in data extraction and assembly, knowledge discovery, decision support for healthcare providers, and personalised self-care aids for patients. A workshop highlight will be the Blood Glucose Level Prediction (BGLP) Challenge, in which researchers will compare the efficacy of different machine learning prediction approaches on a standard set of data from patients with type 1 diabetes.
Main Topics:
- Knowledge discovery and data analytics
- Data extraction, organization and assembly
- Personalisation and decision support
- Blood glucose level prediction
A full list of topics and submission guidelines can be found at: https://sites.google.com/view/kdh-2020
Journal Promotion
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| Acta Koreana (Acta Koreana) |
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| Acta Via Serica (AVS) |
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| Computers and Concrete (CAC) |
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| Journal of Information Processing Systems (JIPS) |
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| Journal of Internet Computing and Services (KSII - JICS) |
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| Journal of Smart Tourism (Smart Tourism) |
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| Membrane and Water Treatment (MWT) |
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| Wind and Structures, An International Journal (Wind and Structures) |
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