The use of data in modern healthcare systems are increasing rapidly with the advancement of state-of-the-art information technology. Research in healthcare systems data has received a great deal of attention in recent years from the research community to improve health outcome. Development and deployment of novel and existing data mining and machine learning techniques can have huge benefits for the healthcare systems including early detection, monitoring, prevention, prediction, diagnosis, treatment, and administration of diseases of patients. Data mining and machine learning techniques play an important role in the development of various data-driven technologies for healthcare systems to help healthcare professionals take data-driven decisions to improve health outcome.
The main goal of this research topic is to explore novel and existing data-driven techniques in healthcare relevant for development of innovative data-driven health technology (i.e., to be used for intelligent monitoring and decision support systems, predictive models for prevention, early detection, diagnosis, treatment and prognosis, digital health systems offering novel personalized data collection, treatment plans and interventions). Papers within this research topic should focus on challenges, limitations, opportunities, and potential impact for future healthcare systems from practical, experimental or theoretical perspectives. Papers should emphasize how the stakeholders (i.e., patients and their relatives, healthcare professionals, hospital administrators, etc.) benefit from the proposed technological advances.
We invite researchers and practitioners to submit their latest high-quality work on data-driven health technology including (but not limited to) the following topics:
AI and machine learning in healthcare
Transparency, trustworthiness and explainability
Digital health transformation
Security and privacy in digital health
Classification of healthcare data
Clustering of healthcare data
Text mining related to healthcare data
Big data analytics in health
Regression for prediction in healthcare data
Prediction using healthcare data
Analysis of hospital acquired complications
Social network concepts in healthcare systems
Impact of technology in healthcare systems
IoT in healthcare systems
Wrangling, exploration and cleaning of healthcare data.
Evaluation and assessment of technology
Pattern analysis using healthcare data
The use of data in modern healthcare systems are increasing rapidly with the advancement of state-of-the-art information technology. Research in healthcare systems data has received a great deal of attention in recent years from the research community to improve health outcome. Development and deployment of novel and existing data mining and machine learning techniques can have huge benefits for the healthcare systems including early detection, monitoring, prevention, prediction, diagnosis, treatment, and administration of diseases of patients. Data mining and machine learning techniques play an important role in the development of various data-driven technologies for healthcare systems to help healthcare professionals take data-driven decisions to improve health outcome.
The main goal of this research topic is to explore novel and existing data-driven techniques in healthcare relevant for development of innovative data-driven health technology (i.e., to be used for intelligent monitoring and decision support systems, predictive models for prevention, early detection, diagnosis, treatment and prognosis, digital health systems offering novel personalized data collection, treatment plans and interventions). Papers within this research topic should focus on challenges, limitations, opportunities, and potential impact for future healthcare systems from practical, experimental or theoretical perspectives. Papers should emphasize how the stakeholders (i.e., patients and their relatives, healthcare professionals, hospital administrators, etc.) benefit from the proposed technological advances.
We invite researchers and practitioners to submit their latest high-quality work on data-driven health technology including (but not limited to) the following topics:
AI and machine learning in healthcare
Transparency, trustworthiness and explainability
Digital health transformation
Security and privacy in digital health
Classification of healthcare data
Clustering of healthcare data
Text mining related to healthcare data
Big data analytics in health
Regression for prediction in healthcare data
Prediction using healthcare data
Analysis of hospital acquired complications
Social network concepts in healthcare systems
Impact of technology in healthcare systems
IoT in healthcare systems
Wrangling, exploration and cleaning of healthcare data.
Evaluation and assessment of technology
Pattern analysis using healthcare data