Computer-aided system with machine learning components for generating medical recommendations for type 1 diabetes patients
Artykuł w czasopiśmie
MNiSW
70
Lista 2024
| Status: | |
| Autorzy: | Nowicki Tomasz |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2025 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 4 |
| Wolumen/Tom: | 21 |
| Strony: | 59 - 75 |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Otwarte czasopismo |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 1 czerwca 2025 |
| Abstrakty: | angielski |
| The paper presents an original method for processing medical data from a type 1 diabetes patient, with the aim of generating therapeutic recommendations to improve the quality of patient care. The article summarizes the results of the first phase of research in this area, which focused on identifying mathematical models and selecting algorithmic methods for further verification in clinical settings. The problem under study is characterized by high complexity, the need to tailor the method to the available data, and, in the completed stage of the research, the inability to perform experiments beyond computer simulations. The proposed approach introduces several novel solutions, including the development of a computer model of a person with diabetes, an original time-series similarity criterion for blood glucose concentration, and the innovative application of a genetic algorithm. The use of the genetic algorithm proved to be effective. The method was developed for patients using an insulin pump and a continuous glucose monitoring system. In the research section, data from five real patients were analyzed using the developed method, and the results indicated that it may be effective in supporting real-world therapy. |
