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System for predicting foot complications in diabetic patients using matching learning

Grant number: 22/01694-5
Support type:Research Grants - Innovative Research in Small Business - PIPE
Duration: May 01, 2022 - April 30, 2024
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal researcher:Leissi Margarita Castañeda Leon
Grantee:Leissi Margarita Castañeda Leon
Company:BIOO Artificial Intelligence em Tecnologia da Informação Ltda
CNAE: Desenvolvimento e licenciamento de programas de computador customizáveis
Tratamento de dados, provedores de serviços de aplicação e serviços de hospedagem na internet
City: São Paulo
Pesquisadores principais:
Bruno Sergio Ferreira Massa
Assoc. researchers: Fernando Antonios Maman
Associated research grant:20/09430-1 - System for predicting foot complications in diabetic patients using machine learning, AP.PIPE
Associated scholarship(s):22/05261-6 - System for predicting foot complications in diabetic patients using matching learning, BP.TT
22/05257-9 - System for predicting foot complications in diabetic patients using matching learning, BP.TT
22/05239-0 - System for predicting foot complications in diabetic patients using matching learning, BP.PIPE

Abstract

In the world, there are already 500M people with diabetes and 30% of them will suffer from a physiological disaster called "diabetic foot", which generates $350B dollars in costs per year. The mortality rate can be compared to more aggressive cancers. Foot complications are present in about 19% to 34% of diabetic patients. It is estimated that 9 to 26 million people worldwide are diagnosed with diabetic foot. Mortality in this group of patients is 5 times higher than in diabetic patients without foot complications. Therefore, identifying patients who are at risk of developing foot complications and taking early action is essential for the good evolution of the cases. The prediction of complications in patients with chronic diseases has been the subject of studies involving the application of concepts of artificial intelligence and machine learning. The results are very promising with an accuracy of 85.3% in predicting readmission of patients within 30 days after discharge and 89% in predicting the development of diabetes, for example. Thus, this project deals with research for the creation of algorithms that allow predicting the emergence of complications such as foot lesions in diabetic patients, exploring machine learning based on structured (exams) and unstructured (free text) retrospective data from electronic medical records (clinical consultation tracking software used by doctors). In particular, the development of the PIPE Phase I project allowed the creation of algorithms of a prototype that demonstrated the feasibility of the approach. The research will be developed at BIOO and will allow the development of a validated product with data from Hospital das Clínicas of USP, having been approved by the Ethics and Scientific Committee of the Instituto de Ortopedia (protocol number 1408). In parallel, a partnership with InovaHC was contracted to apply the algorithm and incubate the company in this ecosystem. Thus, the objective of the project is to develop a system for predicting foot complications in diabetic patients, working in clinics, hospitals and public and private health systems. In addition to the public health benefits, the future system has great economic potential. For example, treating patients with diabetic foot represents an expense of $8.78 billion annually in the United States alone. This is the market niche that the company intends to operate. Thus, diabetic foot complications are responsible for a great impact on the individual and society. Technologies such as the proposal aim to improve the identification of patients at risk, enabling more assertive and individualized actions with better efficiency, helping to prioritize care and reducing the current total cost and improving the application of health resources. As part of the development of PIPE 1, BIOO participated in PIPE Empreendedor and InovaHC's In.cube mentoring programs. This work made it possible to identify and remodel BIOO's business opportunities around 3 main products: 1- library plus services for training and organizing datasets; 2- Epidemiological Data Analytics module; 3- Diabetic foot prediction module. (AU)

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