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S. Hennekam
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Diagnostic errors remain a major challenge in modern medicine, particularly in emergency departments (EDs), where clinicians operate under high pressure and limited time. Bayesian Networks (BNs) offer a transparent, probabilistic approach to reasoning under uncertainty, which may help reduce these errors. This thesis investigates the development and optimization of Promedas (PRObabilistic MEdical Diagnostic Advisory System), a BN-based clinical decision support system (CDSS) designed to generate accurate differential diagnoses for ED patients.
Building on previous iterations, this work aims to determine whether Promedas’ diagnostic accuracy could approach or surpass that of clinicians. Because access to real patient data was unavailable, a large synthetic dataset was generated using Microsoft Copilot, following realistic clinical templates. This dataset was automatically translated from unstructured clinical text into standardised “Promedas language,” consisting of findings, conditions, and diagnoses. The translation process was optimized using prompt-engineering strategies and validated through a custom reviewer environment, achieving an estimated accuracy of 97.5%.
An optimisation cycle was then developed to iteratively refine Promedas’ configuration, and several thought experiments explored its theoretical performance boundaries, including “best guess” and “best case” strategies. While the system demonstrated structural consistency and technical feasibility, the absence of real-world data limited the interpretability of its quantitative results.
The findings suggest that Promedas, under ideal and fully validated circumstances, holds substantial potential transparent, evidence-based diagnostic reasoning and could complement clinical decision-making. However, its true capability can only be determined through further experimentation and re-evaluation on real patient data. Future work should focus on this experimentation and on finding a reliable workflow to assist the iterative refinement cycle. ...
Building on previous iterations, this work aims to determine whether Promedas’ diagnostic accuracy could approach or surpass that of clinicians. Because access to real patient data was unavailable, a large synthetic dataset was generated using Microsoft Copilot, following realistic clinical templates. This dataset was automatically translated from unstructured clinical text into standardised “Promedas language,” consisting of findings, conditions, and diagnoses. The translation process was optimized using prompt-engineering strategies and validated through a custom reviewer environment, achieving an estimated accuracy of 97.5%.
An optimisation cycle was then developed to iteratively refine Promedas’ configuration, and several thought experiments explored its theoretical performance boundaries, including “best guess” and “best case” strategies. While the system demonstrated structural consistency and technical feasibility, the absence of real-world data limited the interpretability of its quantitative results.
The findings suggest that Promedas, under ideal and fully validated circumstances, holds substantial potential transparent, evidence-based diagnostic reasoning and could complement clinical decision-making. However, its true capability can only be determined through further experimentation and re-evaluation on real patient data. Future work should focus on this experimentation and on finding a reliable workflow to assist the iterative refinement cycle. ...
Diagnostic errors remain a major challenge in modern medicine, particularly in emergency departments (EDs), where clinicians operate under high pressure and limited time. Bayesian Networks (BNs) offer a transparent, probabilistic approach to reasoning under uncertainty, which may help reduce these errors. This thesis investigates the development and optimization of Promedas (PRObabilistic MEdical Diagnostic Advisory System), a BN-based clinical decision support system (CDSS) designed to generate accurate differential diagnoses for ED patients.
Building on previous iterations, this work aims to determine whether Promedas’ diagnostic accuracy could approach or surpass that of clinicians. Because access to real patient data was unavailable, a large synthetic dataset was generated using Microsoft Copilot, following realistic clinical templates. This dataset was automatically translated from unstructured clinical text into standardised “Promedas language,” consisting of findings, conditions, and diagnoses. The translation process was optimized using prompt-engineering strategies and validated through a custom reviewer environment, achieving an estimated accuracy of 97.5%.
An optimisation cycle was then developed to iteratively refine Promedas’ configuration, and several thought experiments explored its theoretical performance boundaries, including “best guess” and “best case” strategies. While the system demonstrated structural consistency and technical feasibility, the absence of real-world data limited the interpretability of its quantitative results.
The findings suggest that Promedas, under ideal and fully validated circumstances, holds substantial potential transparent, evidence-based diagnostic reasoning and could complement clinical decision-making. However, its true capability can only be determined through further experimentation and re-evaluation on real patient data. Future work should focus on this experimentation and on finding a reliable workflow to assist the iterative refinement cycle.
Building on previous iterations, this work aims to determine whether Promedas’ diagnostic accuracy could approach or surpass that of clinicians. Because access to real patient data was unavailable, a large synthetic dataset was generated using Microsoft Copilot, following realistic clinical templates. This dataset was automatically translated from unstructured clinical text into standardised “Promedas language,” consisting of findings, conditions, and diagnoses. The translation process was optimized using prompt-engineering strategies and validated through a custom reviewer environment, achieving an estimated accuracy of 97.5%.
An optimisation cycle was then developed to iteratively refine Promedas’ configuration, and several thought experiments explored its theoretical performance boundaries, including “best guess” and “best case” strategies. While the system demonstrated structural consistency and technical feasibility, the absence of real-world data limited the interpretability of its quantitative results.
The findings suggest that Promedas, under ideal and fully validated circumstances, holds substantial potential transparent, evidence-based diagnostic reasoning and could complement clinical decision-making. However, its true capability can only be determined through further experimentation and re-evaluation on real patient data. Future work should focus on this experimentation and on finding a reliable workflow to assist the iterative refinement cycle.