An AI-Driven Patient Profiling and Diagnostic Support System: Leveraging Historical Data to Assist Physicians in Predictive Healthcare
Year: 2025
Type: article
Abstract: This dissertation presents the development of an AI-driven patient profiling and diagnostic support system aimed at enhancing predictive healthcare outcomes through the integration of comprehensive historical data. The primary research problem addressed is the challenge of effectively amalgamating heterogeneous datasets, including electronic health records, clinical notes, and demographic information, to accurately identify patient risk factors that guide clinical decision-making. Key findings indicate that the implemented system significantly improves the accuracy of risk assessments and diagnoses when trained on high-quality, diverse historical data, demonstrating a marked increase in predictive precision compared to traditional methods. The results underscore the importance of leveraging advanced AI technologies to process and analyze extensive patient histories, ultimately facilitating earlier interventions and tailored treatment plans. This research holds significant implications for healthcare by highlighting the potential of AI to transform clinical practices and promote improved patient outcomes through data-driven insights. Moreover, the findings advocate for the necessity of robust data governance and quality management in healthcare systems, underscoring how the systematic use of historical data can enhance not only individual patient care but also overall health system efficiency and effectiveness.
Author: Sabyasachi Saha
Institutions: Harvard University, Stanford University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4406273290