# AI-Powered Stroke Diagnosis System: Methodological Framework and Implementation
## 4 Methodology Development
### Integration Strategies  
Our methodological framework is distinctly characterized by the seamless and sophisticated integration of multi-modal data sources, a critical element that is essential for the effective development of a robust and highly efficient stroke diagnosis system that can significantly enhance clinical outcomes. The organized clinical data, including various patient health metrics and imaging data from advanced medical technologies, are carefully processed through separate but complementary pipelines before being integrated into numerous innovative modeling methods aimed at enhancing the diagnostic accuracy.  
In the case of the structured data, our comprehensive pipeline initiates with the systematic extraction and merging of large-scale CSV files sourced from the eICU database, which is an extensive repository of critical care patient information. This intricate process is executed with remarkable precision to adeptly handle high-dimensional data while concurrently ensuring memory efficiency and computational feasibility. Key preprocessing steps integral to this pipeline encompass the one-hot encoding of categorical variables, the normalization of continuous features employing methodologies such as MinMaxScaler, and the imputation of missing values to maintain data integrity. Furthermore, binary stroke labels are generated based on established diagnostic codes and enriched clinical narratives, which collectively reflect the rigor of the established clinical criteria used within the medical community.  
Concurrently, the image data undergo their own specialized preprocessing stream that is tailored to enhance the quality and usability of the data for subsequent analysis. In this phase, images are standardized through a rigorous process of resizing to uniform dimensions, such as 224 \(\times\) 224 pixels, and normalized according to standard statistical measures to ensure consistency across the dataset. Additionally, various augmentation techniques are employed, involving random transformations that serve to enhance the robustness of the data against variations. These preprocessing steps are of paramount importance as they effectively prepare the images for in-depth analysis utilizing the MobileNet convolutional neural network. Beyond the individual processing of structured and imaging data, we also explore advanced early fusion strategies, wherein feature representations extracted from both the structured data and the images are concatenated in a manner that maximizes the synergy between the two modalities. This sophisticated integration is achieved by processing numeric features through a multi-layer perceptron while simultaneously extracting visual features from the CNN; the resulting feature vectors are subsequently merged and funneled into a final classification layer. Such an integrative approach capitalizes on the complementary strengths inherent in both data modalities, thereby significantly enhancing the overall predictive capability of the diagnostic system.  
A particularly critical aspect of our integration strategy revolves around the meticulous calibration of model outputs, which is an essential consideration given the inherent imbalance observed within the dataset--specifically, the fact that stroke cases represent a minority within the broader patient population. It becomes vitally important to optimize decision thresholds to achieve an acceptable equilibrium between precision and recall, thereby enhancing the model's diagnostic accuracy. To this end, sensitivity analyses and calibration curves are systematically employed in order to fine-tune these decision thresholds, ensuring that the predicted probabilities generated by the model accurately reflect the true clinical risks associated with stroke occurrences. This calibration process is, therefore, indispensable for tailoring the model's performance to align with the pressing clinical imperative of minimizing false negatives, which can have profound implications for patient outcomes and overall healthcare efficacy.