Explainable Artificial Intelligence-Based Diseases Diagnosis From Unstructured Clinical Data and Decision Making Using Blockchain Technologies.

Sumathi M.; S.P. Raja

doi:10.9781/ijimai.2025.02.003

Authors

Sumathi M. School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu.
S.P. Raja School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu.

DOI:

https://doi.org/10.9781/ijimai.2025.02.003

Keywords:

Ant Colony Optimization, Blockchain, Clinical Care, CNNn-LSTM, Disease Diagnosis, Explainable AI, Unstructured Data

Abstract

In the digital era, health information is stored in digital form for easy maintenance, analysis and transfer. The proficiency of manual illness diagnosis and drug prediction in the medical field depends on the expertise availability, and experience of the specialists. In emergency and abnormal situation, the patient’s life completely depends on expert’s availability. Therefore, a different approach is needed to get around the difficulties in managing emergency cases. Artificial intelligence helps to take decisions in an accurate manner but does not provide the details of the decisions. The ability to treat emergency patients entirely depends on the particular hospitals. The clinical data includes numerical results, text prescriptions, scanned images, etc. Therefore, managing unstructured data with care is necessary for making clinical decisions. An explainable artificial intelligence-based disease diagnosis and blockchain-based decision-making system are presented in this work to address these challenges and improve patient care. A natural language processing system analyzes the unstructured data to identify different types of data and explainable AI diagnosis disease with justification and reason for the prediction. An ant colony optimization-based recommender system examines the predicted decision and identifies the specific drug for the disease. The disease decision and drug information are kept in a permissioned blockchain for confirmation. Decisions are validated by more than 50% of the experts present in the permissioned blockchain network, which consists of experts from various regions. As a result, the quickest and most accurate decisions possible are taken to handle emergency situations.

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References

Gazali, S., S. Kaur, and I. Singh, “Artificial intelligence based clinical data management systems: A review,” Informatics in Medicine Unlocked, vol. 9, pp. 219–229, 2017.

A. Martinez-Millana, A. Saez-Saez, R. Tornero-Costa, and N. Azzopardi-Muscat, “Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews,” International Journal of Medical Informatics, vol. 168, pp. 1–12, 2022.

S. M. Sumathi, N. Vijayaraj, S. P. Raja, and M. Rajakamal, “Internet ofthings based confidential healthcare data storage, access control and monitoring using blockchain technique,” Computing and Informatics, vol. 41, no. 5, pp. 1–31, 2022.

S. M. Sumathi, S. Sangeetha, and A. Thomas, “Generic cost optimization and secured sensitive attribute storage model for template-based text document on cloud,” Computer Communications, vol. 150, pp. 569–580, 2020.

V. Bellini, P. Pelosi, M. Valente, A. V. Gaddi, and M. Baciarello, “Using artificial intelligence technique to support clinical decisions in perioperative medicine,” Perioperative Care and Operating Room Management, vol. 28, pp. 1–8, 2022.

J. R. Parikh, C. A. Genetti, A. Aykanat, C. A. Brownstein, and K. Schmitz-Abe, “A data-driven architecture using natural language processing to improve phenol-typing efficiency and accelerate genetic diagnoses of rare disorders,” HGG Advances, vol. 2, no. 1, pp. 1–10, 2021.

J. Lve, N. Viani, J. Kam, L. Yin, S. Verma, S. Puntis, R. N. Cardinal, A. Roberts, R. Stewart, and S. Velupillai, “Generation and evaluation of artificial mental health records for natural language processing,” Digital Medicine, vol. 3, no. 1, pp. 1–10, 2020.

E. H. Houssein, R. E. Mohamed, and A. A. Ali, “Machine learningtechniques for biomedical natural language processing: A comprehensive review,” IEEE Access, vol. 9, pp. 1–26, 2021.

C. Comito, D. Falcone, and A. Forestiero, “AI-driven clinical decision support: Enhancing disease diagnosis exploiting patient similarity,” IEEE Access, vol. 10, pp. 1–12, 2022.

S. Sim and M. Cho, “Convergence model of AI and IoT for virus disease control system,” Personal and Ubiquitous Computing, vol. 25, pp. 1–11, 2021.

H. Lu, S. Uddin, F. Hajati, M. A. Moni, and M. Khushi, “A patient network-based machine learning model for disease prediction: The case of type-2 diabetes mellitus,” Applied Intelligence, vol. 51, pp. 4667–4680, 2021.

F. Amato, L. Coppolino, G. Cozzolino, G. Mazzeo, F. Moscato, and R. Nardone, “Enhancing random forest classification with NLP in DAMEH: A system for data management in e-health domain,” Neurocomputing, vol. 457, pp. 79–91, 2021.

E. L. Romm and I. F. Tsigelny, “Artificial intelligence in drug treatment,” Annual Review of Pharmacology and Toxicology, vol. 60, pp. 353–369, 2020.

E. Khodabandehloo, D. Riboni, and A. Alimohammdi, “HealthXAI: Collaborative and explainable AI for supporting early diagnosis of cognitive decline,” Future Generation Computer Systems, vol. 125, pp. 168–189, 2021.

T. Ploug and S. Holm, “The four dimensions of contestable AI diagnostics: A patient-centric approach to explainable AI,” Artificial Intelligence in Medicine, vol. 107, pp. 1–5, 2020.

N. L. Fitriyani, M. Syafrudin, G. Alfian, and J. Rhee, “HDPM: An effective heart disease prediction model for a clinical decision support system,” IEEE Access, vol. 8, pp. 115710–115726, 2020.

R. F. Mansour, A. E. Amraoui, I. Nouaouri, V. G. Diaz, D. Gupta, and S. Kumar, “Artificial intelligence and internet of things enabled disease diagnosis model for smart healthcare systems,” IEEE Access, vol. 9, pp. 163512–163521, 2021.

V. Singh and D. Jain, “A hybrid parallel classification model for the diagnosis of chronic kidney disease,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 7, no. 4, pp. 8–15, 2021.

S. Dinakaran and P. Anitha, “An efficient drug compound analysis using spectral deep feature classification based compound analysis model for drug recommendation,” Neuroscience Informatics, vol. 2, no. 3, pp. 1–9, 2022.

F. Ali, S. El-Sappagh, S. M. R. Islam, and A. Ali, “An intelligent healthcare monitoring framework using wearable sensors and social networking data,” Future Generation Computer Systems, vol. 114, pp. 23–43, 2021.

F. Ali, S. M. R. Islam, D. Kwak, P. Khan, and N. Ullah, “Type-2 fuzzy ontology-aided recommendation systems for IoT-based healthcare,” Computer Communications, vol. 119, pp. 138–155, 2018.

L. F. Grandamorales, P. Valdiviezo-Diaz, R. Reategui, and L. Barba-Guaman, “Drug recommendation system for diabetes using a collaborative filtering and clustering approach: Development and performance evaluation,” Journal of Medical Internet Research, vol. 24, 2022.

Q. Ye, C.-Y. Hsieh, Z. Yang, Y. Kang, J. Chen, D. Cao, S. He, and T. Hou, “A unified drug-target interaction prediction framework based on knowledge graph and recommendation system,” Nature Communications, vol. 12, no. 1, pp. 1–12, 2021.

G. Zhang, X. Chen, L. Zhang, B. Feng, X. Guo, J. Liang, and Y. Zhang, “STAIBT: Blockchain and CP-ABE empowered secure and trusted agricultural IoT blockchain terminal,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 7, no. 5, pp. 56–65, doi: 10.9781/ijimai.2021.12.002.

T. Hovorushchenko, A. Moskalenko, and V. Osyadlyi, “Methods of medical data management based on blockchain technologies,” Journal of Reliable Intelligent Environments, vol. 9, no. 1, pp. 5–16, 2022.

S. Singh, S. K. Sharma, P. Mehrotra, P. Bhatt, and M. Kaurav, “Blockchain technology for efficient data management in healthcare system: Opportunity, challenges and future perspectives,” Materials Today: Proceedings, vol. 62, pp. 5042–5046, 2022.

R. Cerchione, P. Centobelli, E. Riccio, S. Abbate, and E. Oropallo, “Blockchains coming to hospital to digitalize healthcare services: Designing a distributed electronic health record ecosystem,” Technovation, vol. 117, pp. 1–16, 2022.

G. Lin, H. Wang, J. Wan, L. Zhang, and J. Huang, “A blockchain-based fine-grained data sharing scheme for e-healthcare system,” Journal of Systems Architecture, vol. 127, 2022.

K. Johnson, “Implementation of ICD-10: Experiences and lessons learned from a Canadian hospital,” in IFHRO Congress, AHIMA Convention, Oct. 2004. [Online]. Available: www.ahima.org.

D. Riano, F. Real, J. A. López-Vallverdu, F. Campana, S. Ercolani, P. Mecocci, R. Annicchiarico, and C. Caltagirone, “An ontology-based personalization of health-care knowledge to support clinical decisions for chronically ill patients,” Journal of Biomedical Informatics, vol. 45, no. 3, pp. 429–446, 2012.

C. Li, J. Zhang, X. Yang, and Y. Luo, “Lightweight blockchain consensus mechanism and storage optimization for resource-constrained IoT devices,” Information Processing and Management, vol. 58, no. 6, pp. 1–24, 2021.