# name
Use of Machine Learning and SNOMED CT  Encoded Health Problems to Predict Hospital  Discharge Diagnoses

# metadata.dc.description[].value
Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2024-2025. Tutor: Barrios Montenegro, J. C. ; Director: Santiago Frid

# metadata.dc.description.abstract[].value
The accurate classification of discharge diagnoses is a critical step in clinical decision-making, as 
it has direct effect on patient care, hospital management, and administrative tasks. Traditionally, 
diagnostic coding has been a manual and time-consuming process, typically done after a patient
is discharged, which could lead to delays for subsequent processes such as billing, reporting, and 
care optimization. Recently, the Hospital Clínic de Barcelona has integrated a structured list of 
health problems coded in SNOMED CT into the Electronic Health Record (EHR) from the beginning 
of the patient’s hospitalization. This development has enabled the reuse of structured clinical data 
throughout the care process and has opened the door for predictive tools using Machine Learning 
(ML).
The goal of this research is to determine whether there’s a significant relationship between reported 
health problems and the final ICD-10 discharge diagnoses. To explore this, data obtained from the 
Hospital Clínic de Barcelona was analysed, incorporating information from various clinical sources, 
such as demographics, laboratory results, prescriptions, and admissions records. Feature 
engineering was also carried out and methods based on decision trees, along with ANOVA tests,
were used to identify the most relevant input variables. Subsequently, several supervised ML 
models, including Decision Trees (DTs), Random Forest (RF), and XGBoost were trained and 
evaluated.
The best performing model, a Decision Tree classifier, achieved an accuracy of 69.8%, with a recall 
and F1-score of 0.68, and an AUC of 0.83. While no single variable served as a dominant predictor, 
the results show that health problems coded in SNOMED CT, combined with other clinical and

# metadata.dc.source[].value
Treballs Finals de Grau (TFG) - Enginyeria Biomèdica

# metadata.dc.title[].value
Use of Machine Learning and SNOMED CT  Encoded Health Problems to Predict Hospital  Discharge Diagnoses