A Case-Based Reasoning Model Powered by Deep Learning for Radiology Report Recommendation

Case-Based Reasoning models are one of the most used reasoning paradigms in expert-knowledge-driven areas. One of the most prominent fields of use of these systems is the medical sector, where explainable models are required. However, these models are considerably reliant on user input and the introduction of relevant curated data. Deep learning approaches offer an analogous solution, where user input is not required. This paper proposes a hybrid Case-Based Reasoning, Deep Learning framework for medical-related applications, focusing on the generation of medical reports. The proposal combines the explainability and user-focused approach of case-based reasoning models with the deep learning techniques performance. Moreover, the framework is fully modular to fit a wide variety of tasks and data, such as real-time sensor captured data, images, or text, to name a few. An implementation of the proposed framework focusing on radiology report generation assistance is provided. This implementation is used to evaluate the proposal, showing that it can provide meaningful and accurate corrections, even when the amount of information available is minimal. Additional tests on the optimization degree of the case base are also performed, evidencing how the proposed framework can optimize this base to achieve optimal performance

Zakat management system with allocation prediction using case-based reasoning / Nurkhairizan Khairudin ... [et al.]

Zakat has become one of the vital opportunity to be given to the poor and needy. However, there are problems faced by the institution of zakat with the inefficiency and inaccurate issue, especially in the zakat allocation and distribution aspects. Moreover, the zakat allocation and distribution process is time consuming due to the variety of the criteria to be considered, especially when it involves an educational institution. Since the problem usually originates from the organization of zakat itself, it is essential to minimize the difficulties so that zakat can be distributed in a proper way to the qualified person with a suitable allocation. Therefore, the purpose of this project is to develop a web-based Zakat Management and Allocation Prediction System using Case-based Reasoning(CBR) technique. The proposed method consists of two components: (1) Web-based zakat management system which aims to properly manage all related data of the zakat applicant, and (2) Zakat allocation module using CBR to suggests the allocation amount of zakat by finding the similarities between the previous cases and the new cases. For the prediction purposes, the significant main features are identified and suitable weightage is assigned to be able the CBR engine to produce a suggestion. Experimental results using real data collected from UiTM(Perak) Tapah Campus show that our proposed model achieves a significant improvement in the efficiency of managing and allocating the amount of zakat

How to Improve Hospital Flows in the Context of the COVID Pandemic

In healthcare systems, the adoption of logistics 4.0 main technologies in the processes flows is essential to avoid unnecessary movements and manual work performed by people who could be performing tasks that require logical reasoning. In the context of the COVID pandemic, the adoption of new technologies to replace people in manual processes had become even more usual. This paper aims to demonstrate through simulation, the opportunities of improvement with lean manufacturing concepts and industry 4.0 technologies the hospital flows. After describing the problem and the need of improvements in hospital logistics, a literature review with concepts of Industry 4.0, Lean Manufacturing, and Logistics 4.0 will be presented. The hybrid approach used in the development of a decision aid tool that combines real data and methods of machine learning and problem-solving will be then, an example will be given for illustrating the concepts and methods elaborated

Medical Education for the 21st Century

Medical education has undergone a substantial transformation from the traditional models of the basic classroom, laboratory, and bedside that existed up to the late 20th century. The focus of this text is to review the spectrum of topics that are essential to the training of 21st-century healthcare providers. Modern medical education goes beyond learning physiology, pathophysiology, anatomy, pharmacology, and how they apply to patient care. Contemporary medical education models incorporate multiple dimensions, including digital information management, social media platforms, effective teamwork, emotional and coping intelligence, simulation, as well as advanced tools for teaching both hard and soft skills. Furthermore, this book also evaluates the evolving paradigm of how teachers can teach and how students can learn – and how the system evaluates success