scholarly journals A Deep Learning Approach to Case Based Reasoning to the Evaluation and Diagnosis of Cervical Carcinoma

2018 ◽  
pp. 185-197 ◽  
Author(s):  
José Neves ◽  
Henrique Vicente ◽  
Filipa Ferraz ◽  
Ana Catarina Leite ◽  
Ana Rita Rodrigues ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cervical Carcinoma ◽  
Case Based Reasoning ◽  
Learning Approach ◽  
Case Based

scholarly journals Does the Use of Case-based Learning Impact the Retention of Key Concepts in Undergraduate Biochemistry?

2017 ◽  
Vol 6 (2) ◽  
pp. 110 ◽  
Author(s):  
Genevieve Newton ◽  
Verena Kulak ◽  
Rahul Sharma
Keyword(s):  
Deep Learning ◽  
Retention Test ◽  
Knowledge Retention ◽  
Learning Approach ◽  
Final Exam ◽  
Key Concepts ◽  
Factor Study ◽  
Case Based Learning ◽  
Study Process ◽  
Case Based

Objective: Enhanced knowledge retention and a preference towards a deep learning approach are desirable pedagogical outcomes of case-based learning (CBL). The CBL literature is sparse with respect to these outcomes, and this is especially so in the area of biochemistry. The present study determined the effect of CBL vs. non CBL on knowledge retention in an undergraduate biochemistry course; it also investigated associations of learning approach, age and gender.Methods: We used the Revised Two-Factor Study Process Questionnaire, a retention test, final exam grades and other demographic information to statistically compare academic outcomes of students subjected to either CBL or non-CBL active learning techniques.Results: We showed that students exposed to CBL in a second year course performed significantly better on a retention test conducted nine months after the final exam, and that there was a positive correlation between a deep learning approach and higher retention scores. We did not find an association between gender and age with the retention of biochemistry concepts.Conclusions: Our findings suggest that use of CBL in undergraduate biochemistry education may confer benefits in terms of retention of knowledge of key concepts.

scholarly journals The 25th International Conference on Case-Based Reasoning

AI Magazine ◽  
2018 ◽  
Vol 39 (2) ◽  
pp. 79-80
Author(s):  
David W. Aha ◽  
Kerstin Bach ◽  
Odd Erik Gundersen ◽  
Jean Lieber
Keyword(s):  
Deep Learning ◽  
Poster Session ◽  
Case Based Reasoning ◽  
International Conference ◽  
Process Oriented ◽  
Case Based

ICCBR-2017, the 25th International Conference on Case-Based Reasoning, was held in Trondheim (Norway) in June 2017. The conference included 27 original contributions presented in oral sessions and in a poster session. In addition to three invited talks, the meeting also included workshops on CBR and Deep Learning, Computer Analogy, and Process-Oriented CBR, as well as a Doctoral Consortium, the Computer Cooking Contest, and the first CBR Video Competition.

scholarly journals An Ontology-Based and Case-Based Reasoning Supported Workplace Learning Approach

2017 ◽  
pp. 333-354 ◽  
Author(s):  
Sandro Emmenegger ◽  
Knut Hinkelmann ◽  
Emanuele Laurenzi ◽  
Andreas Martin ◽  
Barbara Thönssen ◽  
...  
Keyword(s):  
Workplace Learning ◽  
Case Based Reasoning ◽  
Learning Approach ◽  
Case Based

scholarly journals A fusion method based on Deep Learning and Case-Based Reasoning which improves the resulting medical image segmentations

2020 ◽  
Vol 147 ◽  
pp. 113200 ◽  
Author(s):  
Lisa Corbat ◽  
Mohammad Nauval ◽  
Julien Henriet ◽  
Jean-Christophe Lapayre
Keyword(s):  
Deep Learning ◽  
Medical Image ◽  
Case Based Reasoning ◽  
Fusion Method ◽  
Case Based

scholarly journals Case-Based Reasoning Framework for Malaria Diagnosis

2020 ◽  
Vol 12 (6) ◽  
pp. 31-48
Author(s):  
Eshetie Gizachew Addisu ◽  
◽  
Abiot Sinamo Boltena ◽  
Samson Yohannes Amare
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Malaria Diagnosis ◽  
Retrieval Algorithm ◽  
Case Based Reasoning ◽  
Learning Approach ◽  
Tigray Region ◽  
Programming Tool ◽  
Machine Learning Approach ◽  
Case Based

Malaria is life threatening disease in Ethiopia specifically in Tigray region. Having common symptoms with other diseases makes it complex and challenging to diagnose effectively. In this paper case based reasoning framework for malaria diagnosis has been designed to diminish the challenges faced by inexperienced practitioners during malaria diagnosis and to solve the problem on shortage of health professionals. The required knowledge for this study was collected through interview and document analysis from domain experts, malaria patient history cards and other related relevant documents. In the case acquisition process the manual format of cases makes the process too challenging. Decision tree is used to model the acquired knowledge. The case structure was then constructed using the selected most determinant attributes. Machine learning approach is applied to select the most relevant features. Feature-vector case representation technique is applied to represent the collected malaria cases. Jcolibri programming tool integrated with Eclipse and Nearest Neighbor retrieval algorithm are used to design the framework. To the end based on the results we can say that the machine learning approach can be used to select most relevant attributes in diseases having several common symptoms and designing case-based diagnosis frameworks could overcome the main problems observed in health centers of Tigray. As an artifact the framework is evaluated by statistical analysis, comparative evaluation, user evaluation and other evaluation techniques. Averagely 79 % precision, 89 % recall, 91.4% accuracy and 78.8% domain expert’s evaluation was the results scored.

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