# A Hybrid Intelligent Approach to Predict Discharge Diagnosis in Pediatric Surgical Patients
## 4 Conclusions  
In this paper, a computer-aided diagnosis system was proposed to be employed as a tool for accurate and timely diagnosis in a third-level hospital. Based on decision trees, the system considers the historical records of input pediatric patients to predict an estimated discharge diagnosis and possible treatments. The proposed CAD system predicts three categories of patient discharge: (1) deceased, (2) unhealthy, and (3) healthy. The CAD learns from the previous diagnoses, the treatment applied, and the patient's discharge condition in order to assist the pediatric surgeon's decision-making so that the best treatment can be offered. If the patient is discharged with pathology, this condition is due to the fact that the patient requires management or surgical treatment in stages due to the complexity of the pathology or the need for its correction at a later age. For example, there is the case of high anorectal malformations, in which at the neonatal stage, a procedure is performed that allows the child to have a bowel movement through an artificial orifice, and then, at a later age, the definitive correction is performed. Let us suppose that the patient dies later, before completing the entire treatment. In that case, the factors that led to the patient's poor evolution would have to be recorded, and the system would have to be fed back. Therefore, the system must have continuous feedback to improve medical decision support.  
The main advantages that are foreseen with using a prediction system to support the making decision process include the following. First, a graphical representation of the possible paths from admission to output diagnosis can provide the means for better decisions. Second, the resulting decision tree can be employed to inform possible paths and risks to the patients' parents, according to previous system experience. Third, statistics on previous cases may provide experience-based evidence in the case of legal conflicts. Finally, an adaptive system may learn from new cases to make more accurate decisions as the knowledge is improved with experience.  
Future work could further improve the results by using XGBoost with ensemble learning (Boosting and Bagging) methods, which consists of selecting the samples that obtained the least error during the learning of sequentially constructed trees. This distributed learning environment can solve problems beyond billions of examples, making it much more versatile when it comes to treatments as delicate as pediatric, and hyperparameters for ensemble learning can be optimized considering a trade-off between _accuracy_ and stability [47]. Additionally, given the continuous entry of novel cases, the system might be adapted to incorporate new information on the new sample records. Ensemble learning methodologies have been proven to provide good performance after incremental learning and fusion adaptation. Finally, although the use of the ICD10 standard provides a helpful framework, within the next few years, the system should consider the recent ICD11 codification to include a more accurate diagnosis [48]. Backward compatibility may be resolved by adding a module to translate medical records from ICD10 to ICD11.  
Conceptualization, [PERSON]-G. and D.D.; methodology, J.S.-G., W.C., M.D.-l.-T., and H.A.-G.; software, [PERSON]-G. and M.D.-l.-T.; validation, W.C., H.A.-G., D.D., and J.E.T.-C.; formal analysis, M.D.-l.-T.; investigation, D.D.; resources, J.E.T.-C.; data curation, [PERSON]-G.; writing--original draft preparation, [PERSON]-G., M.D.-l.-T., and H.A.-G.; writing--review and editing, [PERSON]-G., M.D.-l.-T., W.C., J.E.T.-C., and H.A.-G.; visualization, J.S.-G. and H.A.-G.; supervision, W.C.; project administration, J.S.-G.; funding acquisition, J.E.T.-C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.