Artificial Intelligence Doctor Assistant using Bayesian Networks

Authors

  • S. Selvaraj Research Scholar
  • S. Rajaprakash Aarupadai Veedu Institute of Technology, Vinyka Mission’s Research Foundation, Tamilnadu , India
  • R. Mariappan Mailam Engineering College, Mailam, Tamilnadu , India

Keywords:

AI doctor Assistant, conditional probability, Bayes network

Abstract

This study proposes the development and implementation of an Artificial Intelligence (AI) Doctor Assistant utilizing Bayesian Networks for enhanced medical decision-making. Bayesian Networks, known for their ability to model complex relationships among variables and handle uncertainty, are employed to create a dynamic and adaptable system for medical diagnosis and treatment recommendation.  The AI Doctor Assistant integrates patient data, medical history, and diagnostic information to construct a probabilistic graphical model using Bayesian Networks. This model captures the interdependencies among various medical factors and symptoms, allowing for accurate and personalized assessments. The system employs machine learning techniques to continuously update and refine the Bayesian Network based on new patient data and emerging medical knowledge. By analyzing large datasets, the AI Doctor Assistant enhances its predictive capabilities, enabling more precise and timely diagnosis.

Downloads

Download data is not yet available.

References

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.

Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358.

Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317-1318.

Char, D. S., Shah, N. H., Magnus, D., Implementing Machine Learning in Health Care—Addressing Ethical Challenges. New England Journal of Medicine, 378(11), 981-983.

Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2017). Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236-1246.

Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.

Chen, J. H., Asch, S. M., Machine Learning and Prediction in Medicine - Beyond the Peak of Inflated Expectations. The New England Journal of Medicine, 376(26), 2507-2509.

Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402-2410.

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology, 2(4), 230-243.

Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., ... & Liu, P. J. (2018). Scalable and accurate deep learning with electronic health records. npj Digital Medicine, 1(1), 1-10.

Ravi, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G. Z. (2017). Deep learning for health informatics. IEEE Journal of Biomedical and Health Informatics, 21(1), 4-21.

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.

van der Schaar, M., & Ioannidis, J. P. (2018). The future of AI in healthcare: the challenges of implementation. PLoS Medicine, 15(11), e1002702.

Choi, E., Bahadori, M. T., & Sun, J. (2016). Doctor AI: Predicting clinical events via recurrent neural networks. Journal of Healthcare Informatics Research, 2(4), 272-293.

Liao, F., Liang, M., Li, Z., Hu, X., Song, S., & Wen, D. (2019). Explainable and interpretable artificial intelligence for accurate diagnosis of gastric cancer: A hybrid model with feature selection and visualization. IEEE Access, 7, 44063-44074.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

Tang, A., Tam, R., Cadrin-Chênevert, A., Guest, W., Chong, J., Barfett, J., ... & Martel, A. L. (2018). Canadian Association of Radiologists white paper on artificial intelligence in radiology. Canadian Association of Radiologists Journal, 69(2), 120-135.

Bates, D. W., & Gawande, A. A. (2017). Improving safety with information technology. New England Journal of Medicine, 377(15), 1406-1407.

Downloads

Published

24.03.2024

How to Cite

Selvaraj, S. ., Rajaprakash, S. ., & Mariappan, R. . (2024). Artificial Intelligence Doctor Assistant using Bayesian Networks. International Journal of Intelligent Systems and Applications in Engineering, 12(18s), 516–522. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4999

Issue

Vol. 12 No. 18s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.