A new numerical method for processing longitudinal data: clinical applications
Background: Processing longitudinal data is a computational issue that arises in many applications, such as in aircraft design, medicine, optimal control and weather forecasting. Given some longitudinal data, i.e. scattered measurements, the aim consists in approximating the parameters involved in t...
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| Format: | Article |
| Language: | English |
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Milano University Press
2018-06-01
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| Series: | Epidemiology, Biostatistics and Public Health |
| Online Access: | https://ebph.it/article/view/12881 |
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| author | Ilaria Stura Emma Perracchione Giuseppe Migliaretti Franco Cavallo |
| author_facet | Ilaria Stura Emma Perracchione Giuseppe Migliaretti Franco Cavallo |
| author_sort | Ilaria Stura |
| collection | DOAJ |
| description | Background: Processing longitudinal data is a computational issue that arises in many applications, such as in aircraft design, medicine, optimal control and weather forecasting. Given some longitudinal data, i.e. scattered measurements, the aim consists in approximating the parameters involved in the dynamics of the considered process. For this problem, a large variety of well-known methods have already been developed.
Results: Here, we propose an alternative approach to be used as effective and accurate tool for the parameters fitting and prediction of individual trajectories from sparse longitudinal data. In particular, our mixed model, that uses Radial Basis Functions (RBFs) combined with Stochastic Optimization Algorithms (SOMs), is here presented and tested on clinical data. Further, we also carry out comparisons with other methods that are widely used in this framework.
Conclusions: The main advantages of the proposed method are the flexibility with respect to the datasets, meaning that it is effective also for truly irregularly distributed data, and its ability to extract reliable information on the evolution of the dynamics. |
| format | Article |
| id | doaj-art-fe85db212be548f7a68fb1aa35639452 |
| institution | Kabale University |
| issn | 2282-0930 |
| language | English |
| publishDate | 2018-06-01 |
| publisher | Milano University Press |
| record_format | Article |
| series | Epidemiology, Biostatistics and Public Health |
| spelling | doaj-art-fe85db212be548f7a68fb1aa356394522025-08-20T03:25:46ZengMilano University PressEpidemiology, Biostatistics and Public Health2282-09302018-06-0115210.2427/1288111156A new numerical method for processing longitudinal data: clinical applicationsIlaria Stura0Emma Perracchione1Giuseppe Migliaretti2Franco Cavallo3Dipartimento di Scienze della Sanità Pubblica e Pediatriche, University of Torino, ItalyDipartimento di Matematica Tullio Levi-Civita, Università di Padova, ItaliaDipartimento di Scienze della Sanità Pubblica e Pediatriche, Università di Torino, ItaliaDipartimento di Scienze della Sanità Pubblica e Pediatriche, Università di Torino, ItaliaBackground: Processing longitudinal data is a computational issue that arises in many applications, such as in aircraft design, medicine, optimal control and weather forecasting. Given some longitudinal data, i.e. scattered measurements, the aim consists in approximating the parameters involved in the dynamics of the considered process. For this problem, a large variety of well-known methods have already been developed. Results: Here, we propose an alternative approach to be used as effective and accurate tool for the parameters fitting and prediction of individual trajectories from sparse longitudinal data. In particular, our mixed model, that uses Radial Basis Functions (RBFs) combined with Stochastic Optimization Algorithms (SOMs), is here presented and tested on clinical data. Further, we also carry out comparisons with other methods that are widely used in this framework. Conclusions: The main advantages of the proposed method are the flexibility with respect to the datasets, meaning that it is effective also for truly irregularly distributed data, and its ability to extract reliable information on the evolution of the dynamics.https://ebph.it/article/view/12881 |
| spellingShingle | Ilaria Stura Emma Perracchione Giuseppe Migliaretti Franco Cavallo A new numerical method for processing longitudinal data: clinical applications Epidemiology, Biostatistics and Public Health |
| title | A new numerical method for processing longitudinal data: clinical applications |
| title_full | A new numerical method for processing longitudinal data: clinical applications |
| title_fullStr | A new numerical method for processing longitudinal data: clinical applications |
| title_full_unstemmed | A new numerical method for processing longitudinal data: clinical applications |
| title_short | A new numerical method for processing longitudinal data: clinical applications |
| title_sort | new numerical method for processing longitudinal data clinical applications |
| url | https://ebph.it/article/view/12881 |
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