# Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
## 4 Experiments and Results
### Length of Stay Classification  
In this study, we focused on classifying ICU length of stay (LOS) into two classes: \"short\" if the LOS is 3 days or less and \"long\" otherwise. We then compared our proposed systems with existing methods [35; 36] for the same classification task. [PERSON] et al. [35] achieved an accuracy of 69.50%, an F-score of 59.50%, and an AUROC of 73.60%. In comparison, [PERSON] et al. [36] achieved a higher accuracy of 71.44% and the highest AUROC among the benchmark methods at 77.78%. These results indicate that, while both benchmark methods are strong, [PERSON] et al. 's model is particularly robust in terms of overall predictive performance, as indicated by the AUROC metric. The features we used included problem list summaries, which were the main diagnoses relevant to the reasons for hospitalization that were present but not explicit in the _Plan_ section of the EHR daily progress notes; these were extracted manually for the challenge [19]. We used diagnoses we extracted as well from the other sections of the EHR daily progress notes and other clinical demographic features from the MIMIC-III database. These features were mapped across other tables such as _Patients_, _ICUStays_, and _Admissions_ using the \"HADM_ID\" column, which stands for each hospital admission of a patient. Included features were LOS, our target, insurance information, gender, ethnicity, religion, admission type, first and last care unit before ICU, and discharge location. The features were encoded and fed into three classifiers: SVMClassifier, RandomForestClassifier (RF), and MLPClassifier (MLP) from the scikit-learn library [49]. Diagnoses and problems were encoded using QuickUMLS to extract medical concepts from the dataset. The classifiers were trained using five-fold cross-validation for hyperparameter tuning and were tested on the challenge's testing dataset. Four experiments were run using different combinations of features. The results are shown in Table 3.  
When using only clinical and demographic features, our proposed models showed varying degrees of performance. The MLP model achieved an accuracy of 63.00%, a precision of 65.13%, a recall of 82.50%, an F-score of 72.79%, and an AUROC of 58.60%. The RF model had a similar accuracy of 63.00% and a slightly higher precision of 65.33% and a recall of 81.67%, resulting in an F-score of 72.59% and an AUROC of 62.50%. The SVM model outperformed the other models in this category, achieving the highest accuracy of 64.50%, the highest precision of 67.13%, a recall of 80.00%, an F-score of 73.00%, and the highest AUROC of 63.30%. These results indicate that while SVM shows the best performance among models using only clinical and demographic features, it still falls short compared to the benchmark methods in terms of AUROC. When using only summaries as features, the performance of the models varied significantly. The MLP model achieved an accuracy of 62.50% and a precision of 66.42%, a recall of 75.83%, an F-score of 70.81%, and an AUROC of 67.50%. The RF model had a lower accuracy at 58.50% but achieved the highest recall of 91.67% among all models using summaries, resulting in an F-score of 72.61% and an AUROC of 63.80%. The SVM model again performed the best in this category, achieving the highest accuracy of 66.00%, the highest precision of 68.57%, a recall of 80.00%, the highest F-score of 73.84%, and the highest AUROC of 69.30%. These results suggest that summaries alone can be highly predictive, with the SVM model particularly excelling using this feature set. The models using only diagnoses as features performed notably well. The MLP model achieved an accuracy of 70.00% and a precision of 74.59%, a recall of 75.83%, an F-score of 75.21%, and the highest AUROC in this category of 76.80%. The RF model had a similar accuracy of 69.50% and a precision of 69.28%, the highest recall of 88.33% among all models using diagnoses, an F-score of 77.66%, and an AUROC of 70.20%.