Workshops | UCC 2021
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International Workshop on Machine Learning and Health Informatics (MLHI 2021)

In conjunction with 14th IEEE/ACM International Conference on Utility and Cloud Computing

December 7-10, 2021, Leicester, UK.

Scope

Machine Learning (ML) has played an important role in the advancement of health informatics (HI). ML algorithms are bottom-up approaches in which learning takes place from data in order to make decisions and predictions. These days healthcare systems deal with immense proportions of data which require intelligent techniques in order to glean insights for decision making. Healthcare data pose a set of unique challenges such as data which is incomplete, noisy, missing, dirty and unwanted that leads to sub-optimal modeling artefacts. Apart from the aforementioned challenges, most of the data originating from biomedical/bioinformatics domain is high throughput i.e. a small number of samples characterized by a high number of attributes.

ML applications have revolutionized the HI domain; a number of advanced applications can be found in various branches of healthcare. These applications assist physicians in complex diagnosis, selecting among treatment regimens, monitoring patients, evidence-based decision making, personalized medicine, drug development, to name a few. ML pertaining to diagnosis deals with identifying patterns of certain diseases within Electronic Medical Record (EMR) data. Such applications are useful for identifying anomalies in patients’ health records which can be flagged for further investigation by clinicians. Prognosis for a disease such as cancer is highly complicated process. In this regard, ML applications assist the physician in modeling the prognosis through a number of clinical variables such as gene expression profiles, histological parameters and other relevant factors. Likewise, drug discovery processes are long and complex. ML techniques can assist in decision making in all stages of the discovery process such as identification of biomarkers, digital pathology in clinical trials, target validations, and others.

In this workshop, we invite novel contributions in the area of ML to discuss the advances, challenges and future prospects of HI applications such as diagnosis, prognosis and drug development. The relevant topics include but are not limited to;

  • Machine learning in health informatics
  • COVID-19 pandemic evidence and analysis
  • Internet of Medical Things
  • Deep learning for medical imaging
  • Security and privacy in health-care
  • Blockchain in health-care
  • Medical knowledge creation and maintenance
  • Knowledge graphs for health informatics
  • Big Medical Data and analytics
  • Case studies of machine learning and health informatics

Paper Submission

The submission link for MLHI2021 is at the Easychair: https://easychair.org/conferences/?conf=mlhi2021

The MLHI workshop invites authors to submit original and unpublished work. Papers should not exceed 6 pages single-spaced double-column, using ACM format . At least one author of each accepted submission must register in full and attend the workshop to present and all workshop participants must pay the ACM conference or workshop registration fee.

Important Dates

  • Deadline for paper submission: 20 September, 2021 (Extended)
  • Notifications to authors: 20 October, 2021
  • Camera ready papers: 31 October, 2021
  • Early Registration Due: 31 October, 2021

Organisations

General Chairs:

  • Wajahat Ali Khan (University of Derby, UK, w.khan@derby.ac.uk)
  • Farid Meziane (University of Derby, UK, f.meziane@derby.ac.uk)

Program Chairs:

  • Bo Yuan (University of Derby, UK, b.yuan@derby.ac.uk )
  • Asad Masood Khattak (Zayed University, UAE, asad.khattak@zu.ac.ae )

Steering Committee:

  • Zeeshan Pervez (University of the West of Scotland, UK, zeeshan.pervez@uws.ac.uk)
  • Maqbool Hussain (Sejong University, Republic of Korea, maqbool.hussain@sejong.ac.kr )

Publicity Chair:

  • Mohammad Reza Zare (University of Leicester, UK, mrz3@leicester.ac.uk)

Technical Program Committee:

  • Haider Ali (University of Derby, UK, h.ali@derby.ac.uk)
  • Patrick Hung (University of Ontario, Canada, patrick.Hung@uoit.ca)
  • Khalid Mahmood (Oakland University, USA, mahmood@oakland.edu)
  • Adil Mehmood Khan (Innopolis University, Russia, a.khan@innopolis.ru)
  • Muhammad Fahim (Innopolis University, Russia, m.fahim@innopolis.ru)
  • Donghai Guan (Nanjing University of Aeronautics and Astronautics, China, dhguan@nuaa.edu.cn)
  • Muhammad Bilal Amin (University of Tasmania, Australia, bilal.amin@utas.edu.au)
  • Tae Ho Hur (Kyung Hee University, Republic of Korea, hth@oslab.khu.ac.kr)
  • Shujaat Hussain (National University of Computer and Emerging Sciences, Pakistan)
  • Muhammad Afzal (Sejong University, Republic of Korea, mafzal@sejong.ac.kr)
  • Taqdir Ali (Hammad Bin Khalifa University, Qatar, taqdirstar@gmail.com )
  • Muhammad Sadiq Hassan Zada (University of Derby, UK, m.hassanzada@derby.ac.uk)
  • Rabia Saleem (University of Derby, UK, r.saleem@derby.ac.uk)
  • Ovidiu Bagdasar (University of Derby, UK, o.bagdasar@derby.ac.uk)
  • Jane Labadin (Universiti Malaysia Sarawak, Malaysia, ljane@unimas.my)