Publications by authors named "Hafiz Syed Muhammad Bilal"

Multimodal emotion recognition has gained much traction in the field of affective computing, human-computer interaction (HCI), artificial intelligence (AI), and user experience (UX). There is growing demand to automate analysis of user emotion towards HCI, AI, and UX evaluation applications for providing affective services. Emotions are increasingly being used, obtained through the videos, audio, text or physiological signals.

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Clinical decision support systems (CDSSs) represent the latest technological transformation in healthcare for assisting clinicians in complex decision-making. Several CDSSs are proposed to deal with a range of clinical tasks such as disease diagnosis, prescription management, and medication ordering. Although a small number of CDSSs have focused on treatment selection, areas such as medication selection and dosing selection remained under-researched.

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Article Synopsis
  • Extracting clinical concepts like problems, diagnoses, and treatments from unstructured narratives facilitates advanced data-driven applications, including clinical decision support and treatment assessment.
  • The study introduces a comprehensive rule-based system that improves the automatic extraction of clinical concepts with higher accuracy compared to existing tools.
  • The new system achieves an average F1-score of 72.94%, outperforming previous models, especially in extracting problem-related concepts, which scored 80.45% on average.
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Objective: Causality mining is an active research area, which requires the application of state-of-the-art natural language processing techniques. In the healthcare domain, medical experts create clinical text to overcome the limitation of well-defined and schema driven information systems. The objective of this research work is to create a framework, which can convert clinical text into causal knowledge.

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Objective: Ubiquitous computing has supported personalized health through a vast variety of wellness and healthcare self-quantification applications over the last decade. These applications provide insights for daily life activities but unable to portray the comprehensive impact of personal habits on human health. Therefore, in order to facilitate the individuals, we have correlated the lifestyle habits in an appropriate proportion to determine the overall impact of influenced behavior on the well-being of humans.

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The user experience (UX) is an emerging field in user research and design, and the development of UX evaluation methods presents a challenge for both researchers and practitioners. Different UX evaluation methods have been developed to extract accurate UX data. Among UX evaluation methods, the mixed-method approach of triangulation has gained importance.

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Medical students should be able to actively apply clinical reasoning skills to further their interpretative, diagnostic, and treatment skills in a non-obtrusive and scalable way. Case-Based Learning (CBL) approach has been receiving attention in medical education as it is a student-centered teaching methodology that exposes students to real-world scenarios that need to be solved using their reasoning skills and existing theoretical knowledge. In this paper, we propose an interactive CBL System, called iCBLS, which supports the development of collaborative clinical reasoning skills for medical students in an online environment.

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In recent years, the focus of healthcare and wellness technologies has shown a significant shift towards personal vital signs devices. The technology has evolved from smartphone-based wellness applications to fitness bands and smartwatches. The novelty of these devices is the accumulation of activity data as their users go about their daily life routine.

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Large quantities of data have been generated from multiple sources at exponential rates in the last few years. These data are generated at high velocity as real time and streaming data in variety of formats. These characteristics give rise to challenges in its modeling, computation, and processing.

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Synopsis of recent research by authors named "Hafiz Syed Muhammad Bilal"

  • Hafiz Syed Muhammad Bilal focuses on the intersection of health technology, user experience, and artificial intelligence, particularly in the fields of emotion recognition and clinical decision support systems.
  • His recent research includes the development of a hybrid multimodal emotion recognition framework to enhance user experience evaluation and a clinical decision support system for chronic kidney disease treatment, highlighting the need for automation in healthcare decision-making.
  • Additionally, he explores advanced techniques in clinical text analysis, such as causality mining and concept extraction, to support machine learning applications in healthcare, providing insights into the effectiveness of various treatments and improving clinical workflows.