Muhammad Afzal

Sejong University, Department of Software, Faculty Member

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Secured PhD degree in 2017 from Kyung Hee University South Korea with distinction by achieving Excellent Thesis Award.
An author of more than 60 publication in reputed journals and conferences. Presented research work at different venues in USA, Japan, Europe, Singapore, South Korea and other.
Reviewer of IEEE and Elsevier Journals including Computers in Biology and Medicine, IEEE Access, etc.
Associate Editor of IEEE Access.
Member IEEE, HL7, Korea Society of Internet Information (KSII)
Supervisors: Sungyoung Lee

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Bal Sanghera

Robert de Jong

Gregory Falk

The University of Sydney

The University of Queensland, Australia

Shaun Din

New York Methodist Hospital

Cary Oberije

Nicholas G Zaorsky

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Comprehensible knowledge model creation for cancer treatment decision making

by Muhammad Afzal and Muhammad Irfan

Background: A wealth of clinical data exists in clinical documents in the form of electronic heal... more Background: A wealth of clinical data exists in clinical documents in the form of electronic health records (EHRs). This data can be used for developing knowledge-based recommendation systems that can assist clinicians in clinical decision making and education. One of the big hurdles in developing such systems is the lack of automated mechanisms for knowledge acquisition to enable and educate clinicians in informed decision making. Materials and Methods: An automated knowledge acquisition methodology with a comprehensible knowledge model for cancer treatment (CKM-CT) is proposed. With the CKM-CT, clinical data are acquired automatically from documents. Quality of data is ensured by correcting errors and transforming various formats into a standard data format. Data preprocessing involves dimensionality reduction and missing value imputation. Predictive algorithm selection is performed on the basis of the ranking score of the weighted sum model. The knowledge builder prepares knowledge for knowledge-based services: clinical decisions and education support. Results: Data is acquired from 13,788 head and neck cancer (HNC) documents for 3447 patients, including 1526 patients of the oral cavity site. In the data quality task, 160 staging values are corrected. In the preprocessing task, 20 attributes and 106 records are eliminated from the dataset. The Classification and Regression Trees (CRT) algorithm is selected and provides 69.0% classification accuracy in predicting HNC treatment plans, consisting of 11 decision paths that yield 11 decision rules. Conclusion: Our proposed methodology, CKM-CT, is helpful to find hidden knowledge in clinical documents. In CKM-CT, the prediction models are developed to assist and educate clinicians for informed decision making. The proposed methodology is generalizable to apply to data of other domains such as breast cancer with a similar objective to assist clinicians in decision making and education.

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Expert Systems With Applications Personalization of wellness recommendations using contextual interpretation

by Muhammad Afzal and Muhammad Amin

A huge array of personalized healthcare and wellness systems are introduced into the portfolio of... more A huge array of personalized healthcare and wellness systems are introduced into the portfolio of digital health and quantified-self movement in recent years. These systems share common capabilities including self-tracking/monitoring and self-quantifications, based on the raw sensory data. These capabilities provide solid ground for the users to be more aware of their health; however, such measures are inefficient for changing the unhealthy habits of the users. In order to induce healthy habits in the users, a system must be capable of generating context-aware personalized recommendations. The main obstacle in this regard is the contextual interpretation of recommendations based on user's current context and contextual preferences. To resolve these issues, we propose a methodology of cross-context interpretation of recommendations (CCIR) for personalized health and wellness services. The CCIR method adds additional capabilities to the traditional reasoning methods and builds advanced form of the reasoning with the incorporation of contextual factors in the process of interpretations of the recommendations. With CCIR, the self-quantification systems can be enhanced to generate personalized recommendations in addition to tracking, quantifying, and monitoring user activities. In order to validate the proposed CCIR methodology, a set of 40 contextual scenarios and corresponding recommendations are presented for the evaluation collected from 40 different end users and 10 domain experts. Using chi-square test evaluation, the results demonstrated acceptable " goodness of fit " indices for the system developed on proposed CCIR methodology with respect to the end users' opinion. Also from the statistical observation, it is found that there exists a higher level agreement towards the system between the participants of both end users and experts.

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Comprehensible knowledge model creation for cancer treatment decision making

by Muhammad Afzal and Muhammad Irfan

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Expert Systems With Applications Personalization of wellness recommendations using contextual interpretation

by Muhammad Afzal and Muhammad Amin

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