Calls for papers

 

International Journal of Business Intelligence and Data Mining
International Journal of Business Intelligence and Data Mining

 

Special Issue on: "Data Driven Approach for Bio-medical and Healthcare"


Guest Editors:
Dr. Deepak Gupta, National Institute of Technology, Arunachal Pradesh, India
Prof. Jian Cao, Shanghai Jiaotong University, China
Dr. Elham Bagheri, Nanyang Technological University, Singapore
Dr. Mukesh Prasad, University of Technology Sydney, Australia


Healthcare and biomedical sciences have become data-intensive fields, with a strong need for sophisticated data mining methods to extract the knowledge from the available information. For example, data analysis methods are applied on biomedical datasets, namely DNA microarray data or Next Gen sequencing data to predict treatment outcomes of paediatric Acute Lymphoblastic Leukaemia patients. Moreover, clustering methods are routinely used to investigate the interpretation of the correlated genes associated with cellular and biological function.

Biomedical data contains several challenges in data analysis, including high dimensionality, class imbalance and low numbers of samples. Although the current research in this field has shown promising results, several research issues need to be explored as follows. There is a need to explore feature selection methods to select stable sets of genes to improve predictive performance along with interpretation. There is also a need to explore big data in biomedical and healthcare research. An increasing flood of data characterises human health care and biomedical research. Healthcare data are available in different formats, including numeric, textual reports, signals and images, and the data are available from different sources. An interesting aspect is to integrate different data sources in the data analysis process which requires exploiting the existing domain knowledge from available sources. The data sources can be ontologies, annotation repositories, and domain experts’ reports.

This special issue aims to bring together the current research progress (from both academia and industry) on Machine Learning, Artificial Intelligence and Data Analysis for biomedical and healthcare applications. It will attract healthcare practitioners who have access to interesting sources of data but lack the expertise in using the data mining effectively. Special attention will be devoted to handle feature selection, class imbalance, and data fusion in biomedical and healthcare applications.

Subject Coverage
Suitable topics include, but are not limited, to the following:

  • Information fusion and knowledge transfer in biomedical and healthcare applications
  • Data Analysis of the biomedical data including genomics
  • Text mining for medical reports
  • Statistical analysis and characterization of biomedical data
  • Machine Learning Methods Applied to Medicine
  • Large Datasets and Big Data Analytics on biomedical and healthcare applications
  • Information Retrieval of Medical Images
  • Machine learning technique for single cell sequencing analysis
  • Medical imaging and genomics
  • Natural Language Processing for healthcare and biomedical

Notes for Prospective Authors

Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. (N.B. Conference papers may only be submitted if the paper has been completely re-written and if appropriate written permissions have been obtained from any copyright holders of the original paper).

All papers are refereed through a peer review process.

All papers must be submitted online. To submit a paper, please read our Submitting articles page.


Important Dates

Manuscripts due by: 30 November, 2021

Notification to authors: 15 February, 2022

Final versions due by: 15 April, 2022