Scope

The orientation of the section Medtech Data Analytics is toward papers that facilitate the generation of data-driven models to medical data. At present, the healthcare industry is generating a tremendous amount of data every day. Those data are a mixture of structured, semi-structured, and unstructured data. The sources of data include medical imaging, genomic sequencing, patient engage system, e-health records, mobile-phone apps, health-care social media, monitoring and wearable devices, etc. Other challenges include the vast amount of mixture data, the lack of data standardization, the concern of privacy and security issues, the low-speed and eavesdrop possibility of data transfer, the reliability of data storage, etc. These challenges have slowed the process of leveraging healthcare data and deployment of existing analytics models.

This section will highlight leveraging emerging techniques to help explore analytics in big medical data applications. Both traditional signal processing techniques and recent artificial intelligence methods are welcomed. The techniques and methods of interest include: data mining, artificial intelligence, machine learning, deep learning, knowledge discovery, predictive analysis from medical data, disease diagnostic data-driven models, healthcare workflow mining, hospital readmission and patient length of stay analytics, medical IoT and sensor data quality and reliability, disease profiling and personalized medicine, healthcare cost/service modelling, social media and cloud-computing based analytics for public health, medical expert system and decision support system, natural language processing and text mining, generating medical imaging labels, evidence-based recommender systems, clinical phenotyping, surgery planning, and real-time visualization techniques for the query and analysis of medical data.

Medtech Data Analytics aims to find new biomarkers, improve our understanding of disease mechanisms, increase the efficiency in healthcare delivery, reduce the overall cost for patient/family/hospital, and facilitate clinical decision support. To accelerate the addressing of all these challenges, this Section encourages submissions of scientific/technical findings from both academia and healthcare industry.

Submission

Copyright Statement