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IJSTR >> Volume 8 - Issue 11, November 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Application Of Ai And Soft Computing In Healthcare: A Review And Speculation

[Full Text]



Sumit Das, Manas K. Sanyal



Artificial Intelligence (AI), Knowledge Base (KB), Bayesian network (BN), Artificial Neural Network (ANN), Machine Learning (ML), and Fuzzy Logic (FL).



The crisis of healthcare resources in terms of man and machine in our society is the crucial issues. The calamity well observed at outbreak especially in rural areas where sufficient resources for healthcare management is very difficult to manage. The rural people are not getting proper treatment due to the lack of doctors and they most of the instance committed death due to improper diagnosis by the chock doctors. The question is how to minimize this calamity? The answer to this query is to grow technological consciousness in the glove that is the motivation of these reviews article. It has been observed that in healthcare system could not go ahead a single step without soft computing (SC) and it highly related to Artificial Intelligence in this AI-era. The aim of this article is to highlight and resolve these issues by the reviewing the recent development of artificial intelligence (AI). The paper finds link of AI and soft SC techniques in the field of medical diagnosis and healthcare management. This article reviews the methodology and application of each sub-component of AI-SC in the field of medical healthcare system. It encompasses most of the AI-SC recent development techniques to acquire the knowledge about this domain under a single umbrella. The goal of this review is to explore the application of AI-SC that could enhance diagnosis process of the critical diseases in terms of minimal cost as well as maximal crisis management



[1] F. Jiang et al., “Artificial intelligence in healthcare: past, present and future,” Stroke and Vascular Neurology, vol. 2, no. 4, pp. 230–243, Dec. 2017.W.-K. Chen, Linear Networks and Systems. Belmont, Calif.: Wadsworth, pp. 123-
[2] F. Jiang et al., “Artificial intelligence in healthcare: past, present and future,” Stroke and Vascular Neurology, vol. 2, no. 4, pp. 230–243, Dec. 2017.
[3] Ning Wu and E. A. Silva, “Artificial Intelligence Solutions for Urban Land Dynamics: A Review,” Journal of Planning Literature, vol. 24, no. 3, pp. 246–265, Feb. 2010.
[4] M. Jovanovic, S. Radovanovic, M. Vukicevic, S. Van Poucke, and B. Delibasic, “Building interpretable predictive models for pediatric hospital readmission using Tree-Lasso logistic regression,” Artificial Intelligence in Medicine, vol. 72, pp. 12–21, Sep. 2016.
[5] H. Moen et al., “Comparison of automatic summarisation methods for clinical free text notes,” Artificial Intelligence in Medicine, vol. 67, pp. 25–37, Feb. 2016.
[6] J. Awwalu, A. G. Garba, A. Ghazvini, and R. Atuah, “Artificial Intelligence in Personalized Medicine Application of AI Algorithms in Solving Personalized Medicine Problems,” International Journal of Computer Theory and Engineering, vol. 7, no. 6, pp. 439–443, Dec. 2015.
[7] S. Kumar and G. Kaur, “Detection of heart diseases using fuzzy logic,” Int. J. Eng. Trends Technol.(IJETT), vol. 4, no. 6, pp. 2694–2699, 2013.
[8] E. J. Horvitz, J. S. Breese, and M. Henrion, “Decision theory in expert systems and artificial intelligence,” International journal of approximate reasoning, vol. 2, no. 3, pp. 247–302, 1988.
[9] “Smita Sushil Sikchi, Sushil Sakshi and M.S Ali, ‘Artificial Intelligence In Medical Diagnosis’, International Journal of Applied Engineering Research, ISSN 0973-4562 Vol.7 No.11 (2012)
[10] G. S. Ginsburg and J. J. McCarthy, “Personalized medicine: revolutionizing drug discovery and patient care,” TRENDS in Biotechnology, vol. 19, no. 12, pp. 491–496, 2001.
[11] J. Wang, Y. Hu, F. Xiao, X. Deng, and Y. Deng, “A novel method to use fuzzy soft sets in decision making based on ambiguity measure and Dempster–Shafer theory of evidence: An application in medical diagnosis,” Artificial Intelligence in Medicine, vol. 69, pp. 1–11, May 2016.
[12] A. C. Constantinou, N. Fenton, W. Marsh, and L. Radlinski, “From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support,” Artificial Intelligence in Medicine, vol. 67, pp. 75–93, Feb. 2016.
[13] S. R. Santhosh et al., “Development and evaluation of SYBR Green I-based one-step real-time RT-PCR assay for detection and quantitation of Japanese encephalitis virus,” J. Virol. Methods, vol. 143, no. 1, pp. 73–80, Jul. 2007.
[14] P. Haddawy et al., “Spatiotemporal Bayesian networks for malaria prediction,” Artificial Intelligence in Medicine, vol. 84, pp. 127–138, Jan. 2018.
[15] A. C. Constantinou, B. Yet, N. Fenton, M. Neil, and W. Marsh, “Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences,” Artificial Intelligence in Medicine, vol. 66, pp. 41–52, Jan. 2016.
[16] J. Liu, S. Zhao, and X. Zhang, “An ensemble method for extracting adverse drug events from social media,” Artificial Intelligence in Medicine, vol. 70, pp. 62–76, Jun. 2016.
[17] M.-P. Hosseini, D. Pompili, K. Elisevich, and H. Soltanian-Zadeh, “Random ensemble learning for EEG classification,” Artificial Intelligence in Medicine, vol. 84, pp. 146–158, Jan. 2018.
[18] R. A. Soltan, M. Z. Rashad, and B. El-Desouky, “Diagnosis of Some Diseases in Medicine via computerized Experts System,” International Journal of Computer Science and Information Technology, vol. 5, no. 5, pp. 79–90, Oct. 2013.
[19] S. Das, M. Sanyal, D. Datta, and A. Biswas, “AISLDr: Artificial Intelligent Self-learning Doctor,” in Intelligent Engineering Informatics, vol. 695, V. Bhateja, C. A. Coello Coello, S. C. Satapathy, and P. K. Pattnaik, Eds. Singapore: Springer Singapore, 2018, pp. 79–90.
[20] P. S. Sajja and D. M. Shah, Knowledge based Diagnosis of Abdomen Pain using Fuzzy Prolog Rules. .
[21] M. A. Kadhim, M. A. Alam, and H. Kaur, “Design and Implementation of Fuzzy Expert System for Back pain Diagnosis,” p. 7, 2011.
[22] H. van Ditmarsch, T. French, F. R. Velázquez-Quesada, and Y. N. Wáng, “Implicit, explicit and speculative knowledge,” Artificial Intelligence, vol. 256, pp. 35–67, Mar. 2018.
[23] P. Jarvis and P. Jarvis, “Human Learning: implicit and explicit,” Educação & Realidade, vol. 40, no. 3, pp. 809–823, Sep. 2015.
[24] [24] A. Costa, J. A. Rincon, C. Carrascosa, P. Novais, and V. Julian, “Activities suggestion based on emotions in AAL environments,” Artificial Intelligence in Medicine, vol. 86, pp. 9–19, Mar. 2018.
[25] J. A. Rincon, A. Martin, A. Costa, P. Novais, V. Julian, and C. Carrascosa, “EmIR: An Emotional Intelligent Robot Assistant,” p. 7.
[26] S. V. Albrecht and P. Stone, “Autonomous agents modelling other agents: A comprehensive survey and open problems,” Artificial Intelligence, vol. 258, pp. 66–95, May 2018.
[27] “Diagnosis (artificial intelligence),” Wikipedia. 24-Jul-2019.
[28] A. Goldstein, Y. Shahar, E. Orenbuch, and M. J. Cohen, “Evaluation of an automated knowledge-based textual summarization system for longitudinal clinical data, in the intensive care domain,” Artificial Intelligence in Medicine, vol. 82, pp. 20–33, Oct. 2017.
[29] J. Liang, C.-H. Tsou, and A. Poddar, “A Novel System for Extractive Clinical Note Summarization using EHR Data,” in Proceedings of the 2nd Clinical Natural Language Processing Workshop, Minneapolis, Minnesota, USA, 2019, pp. 46–54.
[30] [30] L. Jiang and C. C. Yang, “User recommendation in healthcare social media by assessing user similarity in heterogeneous network,” Artificial Intelligence in Medicine, vol. 81, pp. 63–77, Sep. 2017.
[31] L. Wang, B. E. Bray, J. Shi, G. Del Fiol, and P. J. Haug, “A method for the development of disease-specific reference standards vocabularies from textual biomedical literature resources,” Artificial Intelligence in Medicine, vol. 68, pp. 47–57, Mar. 2016.
[32] L. Anselma, L. Piovesan, and P. Terenziani, “Temporal detection and analysis of guideline interactions,” Artificial Intelligence in Medicine, vol. 76, pp. 40–62, Feb. 2017.
[33] J.-B. Lamy, “Owlready: Ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies,” Artificial Intelligence in Medicine, vol. 80, pp. 11–28, Jul. 2017.
[34] Y. Shen et al., “An ontology-driven clinical decision support system (IDDAP) for infectious disease diagnosis and antibiotic prescription,” Artificial Intelligence in Medicine, vol. 86, pp. 20–32, Mar. 2018.
[35] V. Vemulapalli et al., “Non-obvious correlations to disease management unraveled by Bayesian artificial intelligence analyses of CMS data,” Artificial Intelligence in Medicine, vol. 74, pp. 1–8, Nov. 2016.
[36] I. Perez et al., “Out of hours workload management: Bayesian inference for decision support in secondary care,” Artificial Intelligence in Medicine, vol. 73, pp. 34–44, Oct. 2016.
[37] P. Petousis, S. X. Han, D. Aberle, and A. A. T. Bui, “Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network,” Artificial Intelligence in Medicine, vol. 72, pp. 42–55, Sep. 2016.
[38] M. Moradi and N. Ghadiri, “Different approaches for identifying important concepts in probabilistic biomedical text summarization,” Artificial Intelligence in Medicine, vol. 84, pp. 101–116, Jan. 2018.
[39] S. Das, S. Biswas, A. Paul, and A. Dey, “AI Doctor: An Intelligent Approach for Medical Diagnosis,” in Industry Interactive Innovations in Science, Engineering and Technology, vol. 11, S. Bhattacharyya, S. Sen, M. Dutta, P. Biswas, and H. Chattopadhyay, Eds. Singapore: Springer Singapore, 2018, pp. 173–183.
[40] V. Bertaud-Gounot, R. Duvauferrier, and A. Burgun, “Ontology and medical diagnosis,” Informatics for Health and Social Care, vol. 37, no. 2, pp. 51–61, Mar. 2012.
[41] “Ontology and medical diagnosis - Google Search.” [Online]. Available:https://www.google.com/search?rlz=1C1CHBD_enIN727IN728&biw=1366&bih=657&tbm=isch&sa=1&ei=ORRgXZaCHc35rQGs4YTgAw&q=Ontology+and+medical+diagnosis&oq=Ontology+and+medical+diagnosis&gs_l=img.12...1016977.1016977..1019286...0.0.. [Accessed: 23-Aug-2019].
[42] J. S. de Bruin, K.-P. Adlassnig, A. Blacky, and W. Koller, “Detecting borderline infection in an automated monitoring system for healthcare-associated infection using fuzzy logic,” Artificial Intelligence in Medicine, vol. 69, pp. 33–41, May 2016.
[43] J. A. Mendez et al., “Improving the anesthetic process by a fuzzy rule based medical decision system,” Artificial Intelligence in Medicine, vol. 84, pp. 159–170, Jan. 2018.
[44] F. Amato, A. López, E. M. Peña-Méndez, P. Vaňhara, A. Hampl, and J. Havel, “Artificial neural networks in medical diagnosis,” Journal of Applied Biomedicine, vol. 11, no. 2, pp. 47–58, 2013.
[45] R. Nayak, L. C. Jain, and B. K. H. Ting, “Artificial Neural Networks in Biomedical Engineering: A Review,” in Computational Mechanics–New Frontiers for the New Millennium, Elsevier, 2001, pp. 887–892.
[46] F. Zhou, L. Jin, and J. Dong, “Premature ventricular contraction detection combining deep neural networks and rules inference,” Artificial Intelligence in Medicine, vol. 79, pp. 42–51, Jun. 2017.
[47] C.-J. Tseng, C.-J. Lu, C.-C. Chang, G.-D. Chen, and C. Cheewakriangkrai, “Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence,” Artificial Intelligence in Medicine, vol. 78, pp. 47–54, May 2017.
[48] M. Tahir and M. Hayat, “Machine learning based identification of protein–protein interactions using derived features of physiochemical properties and evolutionary profiles,” Artificial Intelligence in Medicine, vol. 78, pp. 61–71, May 2017.
[49] N. Khajehali and S. Alizadeh, “Extract critical factors affecting the length of hospital stay of pneumonia patient by data mining (case study: an Iranian hospital),” Artificial Intelligence in Medicine, vol. 83, pp. 2–13, Nov. 2017.
[50] Y. Kazemi and S. A. Mirroshandel, “A novel method for predicting kidney stone type using ensemble learning,” Artificial Intelligence in Medicine, vol. 84, pp. 117–126, Jan. 2018.
[51] A. Jamshidi, J.-P. Pelletier, and J. Martel-Pelletier, “Machine-learning-based patient-specific prediction models for knee osteoarthritis,” Nat Rev Rheumatol, vol. 15, no. 1, pp. 49–60, Jan. 2019.
[52] M. Rotmensch, Y. Halpern, A. Tlimat, S. Horng, and D. Sontag, “Learning a Health Knowledge Graph from Electronic Medical Records,” Scientific Reports, vol. 7, no. 1, Dec. 2017.
[53] M. A. Shwe et al., “Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. I. The probabilistic model and inference algorithms,” Methods Inf Med, vol. 30, no. 4, pp. 241–255, Oct. 1991.
[54] V. Schetinin, L. Jakaite, and W. Krzanowski, “Bayesian averaging over Decision Tree models for trauma severity scoring,” Artificial Intelligence in Medicine, vol. 84, pp. 139–145, Jan. 2018.
[55] “(PDF) The Applications of Genetic Algorithms in Medicine,” ResearchGate. [Online]. Available: https://www.researchgate.net/publication/283498449_The_Applications_of_Genetic_Algorithms_in_Medicine. [Accessed: 25-Aug-2019].
[56] H. Li, D. Yuan, X. Ma, D. Cui, and L. Cao, “Genetic algorithm for the optimization of features and neural networks in ECG signals classification,” Scientific Reports, vol. 7, p. 41011, Jan. 2017.
[57] “(PDF) Recommender Systems for Health Informatics: State-of-the-Art and Future Perspectives,” ResearchGate. [Online]. Available:https://www.researchgate.net/publication/309583367_Recommender_Systems_for_Health_Informatics_State-of-the-Art_and_Future_Perspectives. [Accessed: 25-Aug-2019].
[58] “(PDF) Personalization of Logical Models With Multi-Omics Data Allows Clinical Stratification of Patients,” ResearchGate. [Online]. Available: https://www.researchgate.net/publication/330583852_Personalization_of_Logical_Models_With_Multi-Omics_Data_Allows_Clinical_Stratification_of_Patients. [Accessed: 25-Aug-2019].
[59] C. Zhao, J. Jiang, and Y. Guan, “EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning,” arXiv:1709.06908 [cs], Sep. 2017.
[60] M. Bakator and D. Radosav, “Deep Learning and Medical Diagnosis: A Review of Literature,” Multimodal Technologies and Interaction, vol. 2, no. 3, p. 47, Aug. 2018.
[61] D. Derrington, “Artificial Intelligence for Health and Health Care,” p. 69.
[62] A. Miller, “The intrinsically linked future for human and Artificial Intelligence interaction,” Journal of Big Data, vol. 6, no. 1, Dec. 2019.
[63] “(PDF) Artificial Intelligence in Surgery,” ResearchGate. [Online]. Available: https://www.researchgate.net/publication/329927695_Artificial_Intelligence_in_Surgery. [Accessed: 25-Aug-2019].
[64] S. Lee and M. Choi, “Ultra-rare Disease and Genomics-Driven Precision Medicine,” Genomics & Informatics, vol. 14, no. 2, p. 42, 2016.
[65] A. Saxena and T. C. Services, “Taking Personalized Medicine Mainstream: Unlocking the Potential of Patient Data with Business 4.0TM,” p. 8.
[66] K. Chui, W. Alhalabi, S. Pang, P. Pablos, R. Liu, and M. Zhao, “Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications,” Sustainability, vol. 9, no. 12, p. 2309, Dec. 2017.
[67] M. K. Hassan, A. I. El Desouky, S. M. Elghamrawy, and A. M. Sarhan, “Big Data Challenges and Opportunities in Healthcare Informatics and Smart Hospitals,” in Security in Smart Cities: Models, Applications, and Challenges, A. E. Hassanien, M. Elhoseny, S. H. Ahmed, and A. K. Singh, Eds. Cham: Springer International Publishing, 2019, pp. 3–26.
[68] Das S., Sanyal M.K., Datta D. (2018) Advanced Diagnosis of Deadly Diseases Using Regression and Neural Network. In: Mandal J., Sinha D. (eds) Social Transformation – Digital Way. CSI 2018. Communications in Computer and Information Science, vol 836. Springer, Singapore
[69] Das S., Sanyal M.K., Datta D. (2020) Artificial Intelligent Reliable Doctor (AIRDr.): Prospect of Disease Prediction Using Reliability. In: Mandal J., Sinha D. (eds) Intelligent Computing Paradigm: Recent Trends. Studies in Computational Intelligence, vol 784. Springer, Singapore
[70] Das, S., Sanyal, M.K., Datta, D."Intelligent Approaches for the Diagnosis of Low Back Pain",Proceedings - 2019 Amity International Conference on Artificial Intelligence, IEEE, AICAI 2019