A CNN-DRIVEN APPROACH TO SMART DIAGNOSIS AND REPORT HANDLING IN HOSPITAL MANAGEMENT SYSTEMS
DOI:
https://doi.org/10.29121/granthaalayah.v12.i2.2024.6108Keywords:
Cnn-Driven, Diagnosis, Report Handling, Hospital Management System (Hms), Computerized Platform, Clinical WorkflowsAbstract [English]
The Hospital Management System (HMS) is a comprehensive, computerized platform developed to streamline the administrative and clinical workflows of hospitals. It is capable of managing inpatient and outpatient records, treatment details, billing, pharmacy and lab data, and overall hospital administration, including ward allocation and departmental coordination. Despite the digitization of hospital records, one of the prevailing issues is the inaccessibility of medical reports for patients once they leave the hospital premises. This project proposes a smart, CNN-integrated HMS that not only stores reports securely in a centralized database but also enables remote and real-time access to reports from any location.
In addition to conventional management features, the system incorporates Convolutional Neural Networks (CNNs) to automate and intelligently classify diagnostic images such as X-rays, CT scans, and pathology slides. This feature helps doctors make faster, more accurate decisions and provides patients with annotated, AI-reviewed reports. The CNN also facilitates the categorization of disease patterns based on image data, improving the accuracy of diagnosis and enabling the system to suggest treatment protocols based on historical data and image similarity.
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References
Kumar, A., & Shinde, R. (2017). Hospital Management System. International Journal of Computer Applications.
Gupta, N., & Saxena, A. (2019). Improving Hospital Efficiency Through IT Systems. Health Informatics Journal.
Sharma, S., & Mehta, V. (2021). Secured Access in Healthcare Systems. Journal of Medical Systems.
Desai, M., & Patel, K. (2020). End-to-End Automation in Multispecialty Hospitals. International Journal of Healthcare Information Systems.
Ramesh, P., & Iyer, K. (2018). Activity-Based Costing in Hospital Management. Journal of Health Economics and Management.
Verma, D., & Singh, R. (2022). Challenges in Traditional Healthcare Recordkeeping. Journal of Health and Technology.
Thomas, L., & Jacob, R. (2020). Delay in Medical Data Processing: A Study. Health IT Journal.
Banerjee, S., & Rao, S. (2019). Manual vs. Digital Recordkeeping in Hospitals. International Journal of Healthcare Engineering.
Kumar, R., & Sharma, A. (2021). Evolution of Hospital Management Systems: A Survey. Health Informatics Journal.
Lee, J., Kim, H., & Park, Y. (2020). Cloud-based Smart Healthcare: Integrating Cloud Computing and IoT. IEEE Access.
Litjens, G., Kooi, T., Bejnordi, B. E., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis.
https://doi.org/10.1016/j.media.2017.07.005 DOI: https://doi.org/10.1016/j.media.2017.07.005
Rajpurkar, P., Irvin, J., Zhu, K., et al. (2018). Deep Learning for Chest Radiograph Diagnosis: A Retrospective Comparison of the CheXNeXt Algorithm to Practicing Radiologists. PLoS Medicine. https://doi.org/10.1371/journal.pmed.1002686 DOI: https://doi.org/10.1371/journal.pmed.1002686
Kiranyaz, S., Ince, T., & Gabbouj, M. (2019). 1D Convolutional Neural Networks and Applications: A Survey. Neurocomputing.
https://doi.org/10.1109/ICASSP.2019.8682194 DOI: https://doi.org/10.1109/ICASSP.2019.8682194
Zhang, Z., Yang, G., & Wang, Y. (2020). Predicting Patient Readmission using Deep Learning Models. BMC Medical Informatics and Decision Making.
Islam, M. M., Karim, M. R., & Kwak, D. (2019). A Comprehensive Study on Deep Learning-Based Document Processing in Healthcare. Journal of Biomedical Informatics.
Wang, X., Liu, Y., & Song, H. (2021). Hybrid Deep Learning Models for Efficient Medical Diagnosis. Expert Systems with Applications.
Chen, Y., Ding, S., & Xu, Z. (2018). Challenges and Opportunities in Data Sharing and Privacy in Healthcare. Health Affairs.
Li, T., Sahu, A. K., & Talwalkar, A. (2020). Federated Learning: Privacy-Preserving AI in Healthcare. Nature Digital Medicine.
Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems (NeurIPS).
Ahmad, T., Rauf, H. T., & Lali, M. I. U. (2021). IoT and AI Integration in Smart Healthcare: Recent Trends and Future Directions. Journal of Network and Computer Applications.
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