Prioritizing patients for stomach cancer screening programs: a machine learning approach
Fecha
2022-10-03Autor(es)
Poveda Amaya, Maria CarolinaDirector(es)
Patiño Guevara, Diego AlejandroMurillo Moreno, Raul Hernando
Barrera Ferro, Oscar David
Publicador
Pontificia Universidad Javeriana
Facultad
Facultad de Ingeniería
Programa
Maestría en Ingeniería Industrial
Título obtenido
Magíster en Ingeniería Industrial
Tipo
Tesis/Trabajo de grado - Monografía - Maestría
COAR
Tesis de maestríaCompartir este registro
Citación
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Título en inglés
Prioritizing patients for stomach cancer screening programs: a machine learning approachResumen
Stomach cancer ranks fifth in incidence and is the fourth cause of death by cancer in the world. Since usually this disease is asymptomatic or the symptoms are shared with other diseases, it is diagnosed when the probabilities of recovery are low or null. In this context, performing endoscopy screenings and biopsy follow-ups during early stages could allow the detection of stomach cancer when the patient has a higher probability of recovery. Hence, a proper prioritizing of patients can make feasible the implementation of endoscopy screening programs. This work presents a Decision Support System (DSS) to support the prioritization of patients for endoscopy screening programs. For this purpose, we use the information available in the national healthcare system of Colombia (Sistema General de Seguridad Social en Salud, SGSSS). Our contribution to literature is twofold. First, we identify variables that explain the probability of being diagnosed with stomach cancer, including clinical pathways modeled from a Process Mining approach. Second, we assess the effectiveness of two machine learning approaches for classifying patients and their performance in terms of coverage. Our results show a feasible way to design prevention programs for patient prioritization in a cost-effective approach.
Abstract
Stomach cancer ranks fifth in incidence and is the fourth cause of death by cancer in the world. Since usually this disease is asymptomatic or the symptoms are shared with other diseases, it is diagnosed when the probabilities of recovery are low or null. In this context, performing endoscopy screenings and biopsy follow-ups during early stages could allow the detection of stomach cancer when the patient has a higher probability of recovery. Hence, a proper prioritizing of patients can make feasible the implementation of endoscopy screening programs. This work presents a Decision Support System (DSS) to support the prioritization of patients for endoscopy screening programs. For this purpose, we use the information available in the national healthcare system of Colombia (Sistema General de Seguridad Social en Salud, SGSSS). Our contribution to literature is twofold. First, we identify variables that explain the probability of being diagnosed with stomach cancer, including clinical pathways modeled from a Process Mining approach. Second, we assess the effectiveness of two machine learning approaches for classifying patients and their performance in terms of coverage. Our results show a feasible way to design prevention programs for patient prioritization in a cost-effective approach.
Palabras clave
Aprendizaje automáticoMinería de procesos
Ruta asistencial
Detección temprana
Prevención
Cáncer de estómago
Keywords
Machine LearningProcess mining
Clinical pathways
Early detection
Prevention
Stomach cancer
Cobertura espacial
ColombiaTemas
Maestría en ingeniería industrial - Tesis y disertaciones académicasAprendizaje de máquinas
Aprendizaje automático (Inteligencia artificial)
Neoplasias gástricas
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