A Neural Network Approach to High Cost Patients Detection

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Barbosa, Franklin Messias ; Ishii, Renato Porfirio ; Gervasi, O ; Murgante, B ; Misra, S ; Garau, C ; Blecic, I ; Taniar, D ; Apduhan, BO ; Rocha, AMAC ; Tarantino, E ; Torre, CM

• Tipo de documento: Artigo Científico
• Fonte: COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2021, PT III; v. 12951, p. 14-pg., 2021-01-01.

Resumo

The growing aging of the world's population along with several environmental, social and economic factors, end up posing major challenges for public health. One challenge is the detection and treatment of high cost patients, i.e., a small percentage of patients associated with majority of expenditures in healthcare. The early detection of patients who may become high cost in the future can be used to better target interventions focusing on preventing their transition or, in the case of those who are already in such condition, to allow appropriate approaches, rather than generic ones. In both cases, the detection of such patients can be beneficial, reducing avoidable costs and improving patients' condition. In order to make such detection, this work has focused on using deep learning techniques, specifically, Neural Networks, along with a dataset composed of survey answers applied by the United States government, called Medical Expenditure Panel Survey (MEPS) and attributes gathered from the literature. For the purposes of this work, 11 years of the MEPS dataset were considered, including the years from 2006 to 2016. The models created have shown results ranging between 83% to 90% on metrics such as accuracy, precision, recall, specificity and f1-score. This work also aimed to make the creation and testing of such networks easier, by providing the tools developed during its evolution on GitHub. (AU)

• Processo FAPESP: 14/50937-1 - INCT 2014: da Internet do Futuro
• Beneficiário: Fabio Kon
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 15/24485-9 - Internet do futuro aplicada a cidades inteligentes
• Beneficiário: Fabio Kon
• Modalidade de apoio: Auxílio à Pesquisa - Temático