# A Hybrid Intelligent Approach to Predict Discharge Diagnosis in Pediatric Surgical Patients
## 1 Introduction  
Computer-aided diagnosis systems have been proposed to solve medicine and biology problems since the late 1950s [1]. In a variety of health institutions, current clinical practices include the use of computer-based tools daily. However, such systems still present challenges either in the clinical, regulatory, and algorithmic aspects [2]. Regarding the algorithmic aspects, recent trends exhibit that studies are focusing on the use of artificial intelligence and machine learning techniques to diagnose diseases based on patients' historical records [1]. Medical treatments are complex management problems in pediatric patients due to limited historical information and the following two important factors. First, supplying antibiotics or drugs without an adequate diagnosis can lengthen treatment times and aggravate the condition of the patient. Secondly, surgical procedures in this type of patient are hazardous due to the small size of their organs, generating problems such as anastomosis [3]. From this perspective, as noted by [PERSON] et al. [4], a correct and timely diagnostic may help practitioners to reduce the risk involved in these procedures. According to [PERSON] et al. [5], more than fifty percent of pediatric cases attended in hospitals correspond to readmission conditions, and many of these occurred in emergencies with surgical procedures in a critical time. Whereas some conditions may require a single intervention, in some cases, follow-up treatments are necessary due to unresolved pathologies in previous treatments. These situations may result in patients with very delicate conditions or death, which may provoke further sanctions to doctors and hospitals [5; 6]. Additional complications may be caused by the lack of specialized medical equipment, supplies, and trained professionals for specialized surgical procedures, either in adults or children [5; 7]. For a more accurate and prompt diagnosis, the clinical histories of patients are used to find treatment and symptom patterns over the most complex diseases. In this regard, [PERSON] and [PERSON] [8] mentioned that 60 years of studies in neonatal physiology led to the creation of specialized pediatric anesthesia, critical neonatal care, and the identification of a significant part of the pathologies of the child. In that direction, the use of the _international statistical classification of diseases and related health problems_ (ICD10) drives the diagnosis of complex cases, including infants; however, the diagnosis requires a subjective analysis that is highly influenced by the expertise of the physician. Nowadays, computer scientists and medical researchers have investigated the potential of intelligent systems for computer-aided diagnosis and the planning of surgical procedures. Data from the clinical histories enable the modeling of the relationship between patient conditions and discharge diagnosis when patients are submitted to surgical procedures, using machine learning techniques [9]. An increasing tendency to use data science in medicine has been noted to provide additional information in complex cases or improve the diagnosis by reducing subjectivity [10]. Thus, [PERSON] [11] suggested that in 2050, medicine will evolve into _precision medicine_, involving customized therapies that fit the biology of the patient and their metabolism, as well as genetically estimated drug dosing. From that perspective, pediatric procedures are not excluded, and computational models are fundamental for disease prediction and intervention, reducing time and effort for specific treatments [6; 12]. The adoption of computer-aided diagnosis systems to improve the diagnosis of breast cancer [13] and lesions in organs [14] are examples of the numerous applications that are becoming more usual. Figure 1 shows the four steps, followed by a generic structure of a computer-aided diagnosis system, as suggested by [PERSON] [15] and [PERSON] and Triantaphyllou [2].