Predição de internações por condições sensíveis à atenção básica

• Autor(a) principal: Silva, Zilmar Sousa
• Data de Publicação: 2020
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFG
• dARK ID: ark:/38995/001300000b1t1
• Texto Completo: http://repositorio.bc.ufg.br/tede/handle/tede/10769
• topic: Internações; Condições sensíveis a atenção básica; Atenção primária de saúde; Predição; Mineração de dados; Data mining; Hospitalizations; Ambulatory care sensitive conditions; Primary health care; Prediction; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO

One of the main problems, with strategic and financial consequences for the public health system and private health insurance providers, is the occurrence of hospitalizations for Ambulatory Care Sensitive Conditions (ACSC), %Conditions Sensitive to Primary Care (ICSAB), that is, hospitalizations that could be avoided if certain actions were performed in outpatient care. Health systems have significant data regarding patients seen in their network, coming from a range of information systems for primary outpatient and hospital care. We can then use this data and see if it is possible to find patterns that could previously indicate a risk of hospitalization for the patient. The main purpose of this work is to use data mining techniques, in particular machine learning algorithms, to generate models for predicting ACSC in six pathological subgroups that fall into this category: Urinary Tract Infection, Heart Failure, Unspecified Bronchitis, Chronic Obstructive Pulmonary Disease, Diabetes Mellitus and Essential Hypertension.The data for this project are from patient care in health units in the municipality of Mineiros, GO, Brazil. Among the models generated, those that achieved the best results were Decision Tree and SVM (Support Vector Machine) which resulted in accuracy values ranging from 81 % (chronic obstructive pulmonary disease) to 92 % (essential hypertension) ), and AUC ROC ranging from 87 % (urinary tract infection) to 97 % (essential hypertension). The results achieved indicate that the use of machine learning models are promising for the prediction of ACSC and, combining with new studies using temporal windows for forecasting, they can contribute effectively to the reduction of hospitalizations, and thus, bring benefits to the patient who will not need to go through the negative experience of hospital treatment.