JMIR Publications

ABSTRACT

Background:

Cancer is one of the major health challenges worldwide, requiring continuous patient monitoring, timely medical intervention, and personalized treatment strategies. Cancer patients often undergo complex treatment procedures, including chemotherapy, radiation therapy, surgery, immunotherapy, and targeted therapy. During these treatments, patients may experience various symptoms, side effects, and changes in their health conditions that require regular assessment. However, conventional healthcare monitoring primarily depends on periodic hospital visits, which may limit the timely identification of health complications and continuous assessment of treatment progress. To address these challenges, this project proposes a Web-Based Intelligent Oncology Patient Monitoring System that integrates Artificial Intelligence (AI) and Machine Learning (ML) technologies to improve cancer patient care and monitoring. The proposed system provides a centralized web-based platform for managing patient information, monitoring symptoms, tracking treatment progress, recording medication adherence, and maintaining clinical records. It enables patients to update their health status and report symptoms through an accessible interface, while authorized healthcare professionals can review patient information and monitor changes in their clinical condition. The integration of AI and ML techniques is intended to analyze available patient data, identify potential health risks, and support the early detection of complications. Based on the information provided, the system can generate alerts for healthcare professionals when reported symptoms or predicted risk levels indicate the need for further clinical assessment. Furthermore, the system aims to support personalized healthcare by considering individual patient characteristics, medical history, reported symptoms, and treatment-related information. By organizing relevant clinical data in a unified platform, it can assist healthcare professionals in evaluating treatment progress and making informed decisions. The system also emphasizes data security, patient confidentiality, and controlled access to medical information to ensure the responsible management of sensitive healthcare records. The web-based architecture enables convenient access to patient information from different locations, subject to appropriate authentication and authorization mechanisms. The primary objective of this project is to enhance the efficiency of oncology patient monitoring, encourage timely communication between patients and healthcare professionals, and support data-driven clinical decision-making. By combining digital healthcare management with intelligent analytical techniques, the proposed system seeks to reduce the limitations of conventional monitoring approaches and contribute to more responsive, accessible, and personalized cancer care. The platform is intended to function as a decision-support tool for healthcare professionals rather than a replacement for medical diagnosis or clinical judgment. Overall, this project presents an intelligent and integrated approach to oncology patient monitoring, with the potential to improve continuity of care, facilitate early risk identification, and support the delivery of patient-centered cancer treatment.

Objective:

Contemporary oncological surveillance methodologies may demonstrate the subsequent deficiencies: 1. Reliance upon scheduled clinical facility appointments. 2. Dispersed and fragmented patient documentation. 3. Labour-intensive observations and record-keeping procedures. 4. Insufficient capacity for instantaneous symptomatology documentation. 5. Postponed recognition of emerging clinical risk factors. 6. Inadequate provision of customized clinical recommendations. 7. Constraints in systematic examination of extended temporal patient datasets. 8. Suboptimal coordination between patients and medical practitioners.

Methods:

The proposed methodology encompasses the following sequential phases: Data Collection → Data Preprocessing → Feature Engineering → Model Development → Model Evaluation → Web Integration → Monitoring → Alert Generation → Personalized Care Support

Results:

For a publishable research paper, this section should contain actual experimental results rather than fabricated values. A suitable results table can be structured as follows: Fig 2; Oncology Workforce Productivity Dashboard The optimal-performing model may subsequently be deployed within the web-based application following comprehensive validation procedures. The system assessment should evaluate whether intelligent surveillance mechanisms can enhance the detection of potentially problematic patient presentations relative to traditional monitoring methodologies. Nevertheless, assertions regarding clinical efficacy should not be advanced without prospective empirical validation

Conclusions:

This investigation presents a Web-Based Intelligent System for Oncology Patient Monitoring and Personalized Cancer Care that synthesizes web application development, healthcare data management, artificial intelligence and machine learning component can evaluate selected patient characteristics and monitoring information to facilitate risk categorization and evidence-based clinical decision support. The proposed system demonstrates considerable potential to enhance the continuity, accessibility, and individualization of oncology care delivery. However, effective clinical deployment necessitates comprehensive datasets, through model validation, algorithmic transparency, data privacy safeguards, system interoperability, usability assessment, and prospective randomized clinical investigations. In summary, this research illustrates how the integration of Web Development, Artificial Intelligence, Machine learning and Healthcare Analytics establishes a robust foundation for advanced oncology patient monitoring systems of the future.