Abstract
According to the world health organization, traumatic brain injury (TBI) is in fact the main cause of death and disability worldwide. In Oran Hospital in Algeria and according to pediatric doctors in the critical care service, the majority of TBI cases recorded has been caused by road accidents. It is, therefore, crucial that procedures for early diagnosis, treatment orientation and physical gestures for the child be provided in a timely and efficient manner. In medicine, case-based reasoning (CBR) approach has become a successful model using previous specific patient cases when diagnosing and treating new ones. An important step in the CBR process is the retrieval phase, which deals with looking for similar and useful cases. However, its efficiency is subject to degradation since the quality of cases retrieved and search time may increase as the search space in the case base increases. To overcome this restriction, we proposed in this study a CBR system. A filter step is proposed to reduce the case base before starting the process of searching for similar cases. This filtering will remove from the search space all cases having lesions different from those of the new case. Then a multilabel text categorization tool is used to identify the lesions of the new case. Therefore, this identification will allow selecting from previous cases those with the same lesions as the new case. These cases constitute the important semantic and useful cases in the CBR search space.
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References
Hyder Adnan A et al (2007) The impact of traumatic brain injuries: a global perspective pp 341–353
Reed J, Byard K, Fine H (eds) (2015) Neuropsychological rehabilitation of childhood brain injury: a practical guide. Springer
Rosenlund C, Schou RF (2020) Trauma protocol (ABCDE). In: Management of severe traumatic brain injury. Springer, Cham, pp 95–99
Benfriha H, Atmani B, Khemliche B, Aoul NT, Douah A (2019) A multi-labels text categorization framework for cerebral lesion’s identification. In: Alfaries A, Mengash H, Yasar A, Shakshuki E (eds) Advances in data science, cyber security and IT applications, ICC 2019. Communications in computer and information science, vol 1098. Springer, Cham
Benfriha H, Atmani B, Barigou F, Khemliche B, Douah A, Addou ZZ, Aoul NT (2020) A new approach for case acquisition in CBR based on multi-label text categorization: a case study in child’s traumatic brain injuries. Int J Comput Digital Syst (In press)
Jia WC et al (2016) Integrating a semantic-based retrieval agent into case-based reasoning systems: a case study of an online bookstore. Comput Indus J 78:29–42
Saadi F, Atmani B, Henni F (2019) Integration of datamining techniques into the CBR cycle to predict the result of immunotherapy treatment. In: 2019 international conference on computer and information sciences (ICCIS), Sakaka, Saudi Arabia, pp 1–5. https://doi.org/10.1109/ICCISci.2019.8716415
Mansoul A, Atmani B (2016) Clustering to enhance case-based reasoning. In: Modelling and implementation of complex systems. Springer, Cham, pp 137–151
Saadi F, Atmani B, Henni F (2020) Integration of fuzzy clustering into the case base reasoning for the prediction of response to immunotherapy treatment. In: Djeddi C, Jamil A, Siddiqi I (eds) Pattern recognition and artificial intelligence. MedPRAI 2019. Communications in computer and information science, vol 1144. Springer, Cham
Oyelade ON, Ezugwu AE (2020) COVID19: a natural language processing and ontology oriented temporal case-based framework for early detection and diagnosis of novel coronavirus. Preprints 2020, 2020050171. https://doi.org/10.20944/preprints202005
Barigou BN, Barigou F, Benchehida C, Atmani B, Belalem G (2018) The design of a cloud-based clinical decision support system prototype: management of drugs intoxications in childhood. Int J Healthcare Inform Syst Inform (IJHISI) 13(4):28–48
Nachet B, Adla A (2018) Intelligent ontology CBR system for fault diagnosis and repair. Int J Comput Digital Syst 7(02):85–93
El-Sappagh S, Elmogy MM (2020) Medical case based reasoning frameworks: current developments and future directions. In: Management Association, I. (Ed.), Virtual and mobile healthcare: breakthroughs in research and practice. IGI Global, pp 516–552. https://doi.org/10.4018/978-1-5225-9863-3.ch025.
Mendes D, Lopes MJ, Romão A, Rodrigues IP (2019) Healthcare computer reasoning addressing chronically Ill societies using IoT: deep learning AI to the rescue of home-based healthcare. In: Management Association, I. (Ed.), Chronic illness and long-term care: breakthroughs in research and practice. IGI Global, pp 720–736. https://doi.org/10.4018/978-1-5225-7122-3.ch036
Bentaiba-Lagrid MB, Bouzar-Benlabiod L, Rubin SH, Bouabana-Tebibel T, Hanini MR (2020) A case-based reasoning system for supervised classification problems in the medical field, expert systems with applications. https://doi.org/10.1016/j.eswa.2020.113335
Corbat L, Nauval M, Henriet J, Lapayre J-C (2020) A Fusion method based on deep learning and case-based reasoning which improves the resulting medical image segmentations. Expert Syst Appl 113200. https://doi.org/10.1016/j.eswa.2020.113200
Marie F, Corbat L, Chaussy Y, Delavelle T, Henriet J, Lapayre J-C (2019) Segmentation of deformed kidneys and nephroblastoma using case-based reasoning and convolutional neural network. Expert Syst Appl 127:282–294. https://doi.org/10.1016/j.eswa.2019.03.010
Metcalf K, Leake D (2018) Embedded word representations for rich indexing: a case study for medical records. In International conference on case-based reasoning. Springer, Cham, pp 264–280
Amin K, Kapetanakis S, Althoff KD, Dengel A, Petridis M (2018) Answering with cases: a CBR approach to deep learning. In International conference on case-based reasoning. Springer, Cham, pp 15–27
Zou Y, Kiviniemi A, Jones SW (2017) Retrieving similar cases for construction project risk management using natural language processing techniques. Autom Constr 80:66–76
Shen LY, Yan H, Fan HQ, Wu Y, Zhang Y (2017) An integrated system of text mining technique and case-based reasoning (TM-CBR) for supporting green building design. Build Environ 124:388–401
Salem YB, Idoudi R, Ettabaa KS, Hamrouni K, Solaiman B (2017) Ontology based possibilistic reasoning for breast cancer aided diagnosis. In: European, Mediterranean, and Middle Eastern conference on information systems. Springer, Cham, pp 353–366
Nasiri S, Zenkert J, Fathi M (2015) A medical case-based reasoning approach using image classification and text information for recommendation. In: Rojas I, Joya G, Catala A (eds) Advances in computational intelligence. IWANN 2015. Lecture notes in computer science, vol 9095. Springer, Cham
Herrera F, Charte F, Rivera AJ, Del Jesus MJ (2016) Multilabel classification: problem analysis, metrics and techniques. Springer, Heidelberg
Benfriha H, Barigou F, Atmani B (2016) A text categorization framework based on concept lattice and cellular automata. Int J Data Sci (IJDS) 1(3):227–246
Bello-Tomás JJ, González-Calero PA, DĂaz-Agudo B (2004) JColibri: an object-oriented framework for building CBR systems. In: Funk P, González Calero PA (eds) ECCBR 2004. LNCS (LNAI), vol 3155, pp 32–46. Springer, Heidelberg. https://doi.org/10.1007/978-3-540-28631-8_4
Acknowledgements
Authors would like to express their gratitude to Doctor Nesserine Benfriha who has significantly and decisively contributed to the labeling of the MRI scan collection.
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Benfriha, H. et al. (2022). Improving CBR Retrieval Process Through Multilabel Text Categorization for Health Care of Childhood Traumatic Brain Injuries in Road Accident. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 217. Springer, Singapore. https://doi.org/10.1007/978-981-16-2102-4_65
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