Souâd Demigha
6. Discussion and conclusion
Souâd Demigha
Table 2: Clustering a shared-feature network Choose a clustering method.
Create a top node for the tree. Call it N.
Let C be the set of cases needing organization.
Put any features shared by all the cases in C into N.
Partition C using the clustering method. Create a node for each partition, attaching each as a successor to node N.
For each partition.
Create a node Ni.
If it contains more than one case, then repeat step 4, with N= Ni, C = the cases in the partition.
Else, put the features of the one case into node Ni.
For breast cancer, data items may be grouped according to logical relationships or senologist (expert-radiologist in breast cancer) affinities or preferences. Due to the complexity of medical data, it will be better in certain projects or diagnoses to adapt existing algorithms or optimize their use to obtain better results (Iavindrasana et al., 2009. The heterogeneity of the medical data such as: volume and complexity, physician’s interpretation, poor mathematical categorization and canonical form motivates medical data miners to develop new approaches to analyze data, (Iavindrasana et al., 2009).
To remediate to these deficiencies it will be advisable to create standard vocabularies, interfaces between different sources of data integrations, and design of electronic patient records. In (Jesneck et al., 2006), the authors propose a strategy “decision fusion” for the classification of imaging data from multiple modalities, multiple sources and having various types of features (Tusch et al., 2008).
5.3.3 Knowledge acquisition
We distinguish between 5 categories of data: Clinical features, Radiological features, Histological features, Image Data features and Digital image features.
5.3.4 Knowledge representation
Object-oriented based retrieval represent one way to represent cases is in the form of objects where each of the attributes could be of a simple type, like integer or string, or of type object. This forms a hierarchy of the object structure within which cases in the same classes of the hierarchy can be compared. The issue with this type of structure is when the target case and the case in the case base are not objects of the same class. Using this type of retrieval, not all the cases are compared to the target case, so it is faster than K-NN. Also this method is tolerant to missing attributes. If values are missing for the target case, the higher part of the hierarchy is searched, resulting in more retrieved cases.
We have organized data using CBR. We have structured the fifth categories of features as cases (CBR). We have modeled these cases with the object modeling, (Bergmann, 1998).
5.3.5 Illustration
Table 3 is an example of a scenario proposed for a training session for junior radiologists. This scenario will place the junior-radiologist in a situation where they will perform a learning session. It will require them to learn targeted knowledge and skills, (Demigha, 2015a).
Table 3: A scenario example
The junior radiologist is provided with a case library of videos of experts telling their stories, strategies, and perspectives that might help them with their task.
When they achieve their goal, they ask a question of the case library, and an appropriate video is retrieved and shown.
A story proposes a topic to radiologists (juniors) they should learn more about or a skill they need to learn.
A story tells how that expert dealt with some difficult issue the student is addressing.
Souâd Demigha
system. We used the Case-Based Reasoning approach which still a very innovative approach and applied fluently in the domain of artificial intelligence and knowledge-based systems. We have tested this “knowledge base” on some clinical and radiological cases. Results are very encouraging.
By using the cloud, researchers can access the resources needed for executing large-scale clinical trials involving multiple institutions. The emerging technologies of cloud computing have already attracted several researchers, clinical administrators, and software developers to move medical image archives such as PACS onto the cloud, in order to improve manageability, accessibility, and storage availability.
Cloud computing provides a cost efficient platform for many applications, but due to the number of available options and fast growth of this industry, the selection of a suitable service or a set of services for an application becomes a challenge for cloud customers. Case-based reasoning is a viable option to assist cloud customers in finding the best service for their applications based on previous experiences of other customers and experts.
The system is under development and needs some revision and performance tests. The future steps will concern the development of the CBR tool and its implementation. The implementation will be considered as evidence concepts put forward in this work. It displays a scenario of use of the overall tool which helps understanding the concrete role that such a system can play in a real medical environment.
References
Amazon Web Services, (2013). “What is Cloud Computing?”.
Arina, T. (2011). “Cloud learning: Learning Environments in the Cloud Era”, Retrieved from http://www.slideshare.net/infe/cloud-learning-learning-environments-in-the-cloud-era.
Bergmann, R. and Stahl, A. (1998). “Similarity Measures for Object-Oriented Case Representations”, Proc. 4th European Workshop, EWCBR, Advances in Case-Based Reasoning Lecture, Vol. 1488, pp. 25-36.
Bhakti, M. and Nugroho, H. (2012). “Taking up autonomous SOA framework into cloud computing”, Cloud Computing and Social Networking (ICCCSN), pp. 1–4.
Bichindaritz, I. (2008). “Memory Structures and Organization in Case-Based Reasoning”, Case-Based Reasoning on Images and Signals, P. Perener, ed. Springer, pp. 175–194.
Demigha, S. (2018). “Big Data, Knowledge Management (KM) and Case-Based Reasoning (CBR)”, European Conference on Knowledge Management, Vol.1, pp. 164-173.
Demigha, S. (2015a). “A generic elearning tool for radiologists and hospital practitioners with CBR”, Proc. ECEL, 14thEuropean Conference on e-Learning, pp. 809-818.
Demighab, S. (2015). “Data Mining for Breast Cancer Screening,” In the 10th IEEE International Conference on Computer Science & Education, IEEE ICCSE, pp 59–63, Fitzwilliam College, Cambridge University, UK.
Demighac, S. (2015). “Mining Knowledge of the Patient Record”, the 12th International Conference on Intellectual Capital, Knowledge Management and Organisational Learning, ICICKM, Bangkok University Bangkok, pp. 71-79, Thailand.
Duane, C. (2010). “Cloud computing History 101”. Available from:
http://www.constructioncloudcomputing.com/2010/08/14/cloud-computing history/.
Dubey, N., Vishwakarma, S. (2016). “Cloud Computing in Healthcare”, International Journal of Current Trends in Engineering & Research (IJCTER) e-ISSN 2455–1392 Vol. 2, No. Issue 5, pp. 211 – 216. Geissbuhler A, Kulikowski C, editors. IMIA Yearbook of Medical Informatics.
Haghighat, M., Zonouz, S., and Abdel-Mottaleb, M. (2015). “Trustworthy Cloud-based and Cross-Enterprise Biometric Identification”, Expert Systems with Applications, Vol. 42, No. 21, pp. 7905–7916,
Hu, D.H. et al. (2010). “BetterLife 2.0: Large-Scale Social Intelligence Reasoning on Cloud”, Cloud Computing Technology and Science CloudComIEEE Second International Conference, pp. 529–536.
Iavindrasan, J.,Cohen, G., Depeursinge, A., Müller, H., Meyer, R., and Geissbuhler, A. (2009). “Clinical Data Mining: a Review”, IMIA Yearbook of Medical Informatics.
Jesneck, J.L., Nolte, L.W., Baker, J.A., Floyd, C.E and Lo. J.Y. (2006). “Optimized approach to decision fusion of eterogeneous data for breast cancer diagnosis”, Med Phys; Vol.33, pp.2945-54.
Kagadis, G.C., Kloukinas, C, Moore, K., Philbin, J. Panagiotis Papadimitroulas, P. et al. (2013). “Cloud computing in medical imaging”, Med Phys, Vol. 40, No. 7.
Kharat, K., T., Safvi, A. Thind,S.S and Singh, A. (2012). “Cloud Computing for radiologists”, Indian JRadiol Imagin, Vol 22, No.
3, pp. 150–154.
Kolodner, J. (1993). “The Case-Based Reasoning”, Morgan Kaufmann.
Krissi, D. (2008). “Distinguishing Cloud computing from utility computing”, Available from:
http://www.ebizq.net/blogs/saasweek/2008/03/distinguishing_cloud_computing/
Maurer, M. et al. (2013). “Adaptive resource configuration for Cloud infrastructure management”, Future Generation Computer Systems, Vol. 29, pp. 472–487.
Maurer, M. et al. (2010). “Simulating autonomic SLA enactment in clouds using case based reasoning”, Towards a Service- Based Internet, Vol. 648, pp. 25–36.
Souâd Demigha
Minor, M. and Schulte-zurhausen, E. (2014). “Towards Process-Oriented Cloud Management with Case-Based Reasoning”, 22nd International Conference on Case-Based Reasoning (ICCBR), pp. 305–314. Patel, R.P. (2012). Schulte-zurhausen, E. and Minor, M. (2014). “Task Placement in a Cloud with Case-based Reasoning”; 4th International Conference on Cloud Computing and Service Science, pp. 323–328.
Soltani, S. (2016). “IAAS Cloud Service Selection using Case-Based Reasoning”, PhD thesis, School of Computing in conformity with the requirements for the degree of Doctor of Philosophy Queen’s University Kingston, Ontario, Canada.
Tsatsoulis, C. and Williams, A.B. (2000). “Case-Based Reasoning”, Knowledge-Based Systems, pp. 807– 837.
Tusch, G., Bretl, C.E, Connor, M., Das, A . (2008). “SPOT Towards Temporal Data Mining in Medicine and Bioinformatics”, in AMIA Annu Symp Proc, pp. 1157.