# Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
## 4 Experiments and Results
### Length of Stay Classification
The SVM model outperformed both, achieving the highest accuracy of 72.50%, the highest precision of 79.27%, a recall of 73.33%, an F-score of 76.19%, and an AUROC of 76.20%. These results demonstrate the strong predictive performance of diagnosis features, with the SVM model again showing superior performance. Combining diagnoses and summaries yielded the best results among all of our proposed models. The MLP model achieved an accuracy of 72.50%, a precision of 74.07%, a recall of 83.33%, an F-score of 78.43%, and an AUROC of 79.70%. The RF model had a lower accuracy at 66.00% and a precision of 64.13%, the highest recall of 98.33% among all models, an F-score of 77.63%, and an AUROC of 81.70%. The SVM model performed the best overall, achieving the highest accuracy of 77.50%, the highest precision of 78.19%, a recall of 86.66%, the highest F-score of 82.21%, and the highest AUROC of 82.40%. These results indicate that combining diagnoses and summaries provides the most comprehensive and accurate predictive performance, with the SVM model being the most effective. In summary, our proposed methods show varying degrees of success, with models using diagnoses and summaries combined outperforming models that use other feature sets. The SVM model consistently performed the best across different feature sets, especially when both diagnoses and summaries were used; the best hyperparameters found for this model were \"C = 1\" for the regularization parameter, \"gamma = 0.01\" for the the kernel coefficient, and a \"radial basis function (RBF)\" as the kernel. This model, being our best-performing classifier, has set a new benchmark for this task and showed significant improvements and robustness over the benchmark methods (_Papers With Code_, Length of Stay Prediction on MIMIC-III, 2022, [[https://paperswithcode.com/sota/length-of-stay-prediction-on-mimic-iii](https://paperswithcode.com/sota/length-of-stay-prediction-on-mimic-iii)]([https://paperswithcode.com/sota/length-of-stay-prediction-on-mimic-iii](https://paperswithcode.com/sota/length-of-stay-prediction-on-mimic-iii)) (accessed 27 June 2024)), especially in terms of precision and recall. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Method** & **Accuracy** & **Precision** & **Recall** & **F-Score** & **AUROC** \\ \hline \multirow{2}{*}{**Benchmark Methods**} & [PERSON] et al. [35] & 69.50 & - & - & 59.50 & 73.60 \\  & [PERSON] et al. [36] & 71.44 & - & - & - & 77.78 \\ \hline \multirow{2}{*}{**Clinical and Demographic features only**} & MLP & 63.00 & 65.13 & 82.50 & 72.79 & 58.6 \\  & RF & 63.00 & 65.33 & 81.67 & 72.59 & 62.50 \\  & SVM & 64.50 & 67.13 & 80.00 & 73.00 & 63.3 \\ \hline \multirow{2}{*}{**Summaries only**} & MLP & 62.50 & 66.42 & 75.83 & 70.81 & 67.50 \\  & RF & 58.50 & 60.11 & 91.67 & 72.61 & 63.80 \\  & SVM & 66.00 & 68.57 & 80.00 & 73.84 & 69.30 \\ \hline \multirow{2}{*}{**Diagnoses only**} & MLP & 70.00 & 74.59 & 75.83 & 75.21 & 76.80 \\  & RF & 69.50 & 69.28 & 88.33 & 77.66 & 70.20 \\  & SVM & 72.50 & 79.27 & 73.33 & 76.19 & 76.20 \\ \hline \multirow{2}{*}{**Diagnoses and summaries**} & MLP & 72.50 & 74.07 & 83.33 & 78.43 & 79.70 \\  & RF & 66.00 & 64.13 & 98.33 & 77.63 & 81.70 \\ \cline{1-1}  & SVM & 77.50 & 78.19 & 86.66 & 82.21 & 82.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of proposed systems and benchmark methods in term of accuracy, precision, recall, F1, and AUROC scores in percentages (%).