## 4 Discussion
### Application of the Predictive Model
* **Specific Outcomes:** They show a significant positive impact on COVID-19 (0.0322289) and on Quality and Resource with smaller values, but a very negative impact on  
Figure 3: Confusion matrix for logistic regression. Figure 2: Predictive models performances. Security (\(-0.0431382\)). This could reflect the importance of specific outcomes in COVID-19 research and their lesser relevance for security studies. * **Clinical Challenges:** Clinical challenges have a small positive impact on COVID-19 (0.00912065) and Quality (0.00155511), suggesting that clinical issues are relevant but not dominant in these fields. It is interesting to note the slight negative impact on Resources (\(-0.00374929\)), perhaps indicating that clinical challenges are less central in resource management. * **Data Challenges:** These challenges show a negative impact on COVID-19 (\(-0.0119134\)) but a positive, albeit smaller, impact on Quality (0.00559921). This may indicate that while data-related challenges are perceived as problematic in COVID-19 research, they may provide opportunities for improvement in quality. * **General:** A significant variation in the impact of general challenges, with a strong positive impact on Resource (0.0458024) and a very negative impact on Technnovation (\(-0.107902\)), highlights how general challenges are seen as crucial in resource management but hinder technological innovation. * **Methodology--General Approach:** This has a strong negative impact on COVID-19 (\(-0.0524088\)) and Quality (\(-0.0229678\)) but a positive impact on Technnovation (0.0377616), suggesting that a nonspecific methodological approach is less useful for direct studies but advantageous for technological innovation. * **Discipline--General Studies in Health and Technology:** These studies have an extremely positive impact on Resources (0.330612) but a negative impact on Technnovation (\(-0.128479\)), highlighting the importance of general research in resource management and its limiting impact on technological innovation. * **Model Predictions:** The model predictions show the highest confidence in the Resource category (0.74) compared to others, suggesting that the analyzed features are particularly indicative of studies focused on resource management. Figure 4: Receiver Operating Characteristic (ROC) Curves for logistic regression across five target classes. The analysis of SHAP values thus provides valuable insights into how different features influence the classification of studies in the target categories, highlighting the intersection of methodology, discipline, challenges, and outcomes in AI research in healthcare management. This detailed understanding helps delineate specific areas of strength and potential improvements in the predictive model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multirow{2}{*}{**Features**} & \multicolumn{5}{c}{**Target Class**} \\ \cline{3-7}  & & **COVID-19** & **Quality** & **Resource** & **Security** & **Techninovation** \\ \hline \multirow{5}{*}{Results} & Progress in data management & 0 & 0 & 0 & 0 & 0 \\ \cline{2-7}  & Clinical and medical insights & 0.00558636 & 0.00117473 & 0.00446783 & 0.00819819 & \(-\)0.0194271 \\ \cline{2-7}  & Technological advancements & 7.21863 & 0.00129731 & \(-\)0.00260619 & \(-\)0.00944916 & 0.0106858 \\ \cline{2-7}  & Specific outcomes & 0.0322289 & 0.00750072 & 0.0193839 & \(-\)0.0431382 & \(-\)0.0159753 \\ \cline{2-7}  & Advancements in security & 0 & 0 & 0 & 0 & 0 \\ \hline \multirow{5}{*}{Challenges} & Clinical & 0.00912065 & 0.00155511 & \(-\)0.00374929 & \(-\)0.00103583 & \(-\)0.00589064 \\ \cline{2-7}  & Data & \(-\)0.0119134 & 0.00559921 & 0.00402851 & 0.00955029 & \(-\)0.00726464 \\ \cline{2-7}  & Financial & \(-\)0.00584617 & \(-\)7.04298 \(\times\) 10\({}^{-65}\) & 0.000463316 & 0.00218184 & 0.00327144 \\ \cline{2-7}  & General & 0.00863243 & 0.0205111 & 0.0458024 & 0.0329556 & \(-\)0.107902 \\ \cline{2-7}  & Regulation & 0 & 0 & 0 & 0 & 0 \\ \cline{2-7}  & Technology & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}