1,721,297 research outputs found
A Reliable Machine Learning Approach applied to Single-Cell Classification in Acute Myeloid Leukemia
No full textInterpreting longitudinal data through temporal abstractions: An application to diabetic patients monitoring
No full textIn this paper we present a new approach for the intelligent analysis of longitudinal data coming from diabetic patients home monitoring. This approach consists in exploiting temporal abstractions to preprocess the raw data and to obtain a new time series of abstract episodes, whose features are then interpreted through statistical and probabilistic techniques. We finally show the application of this methodology on the data of two diabetic patients monitored for six months
The subcutaneous route to insulin-dependent diabetes therapy
No full textDiscusses closed-loop and partially closed-loop control strategies for insulin delivery and measuring glucose concentration. The authors review the subcutaneous closed- and partially closed-loop strategies that have been proposed and tested in recent years for insulin-dependent diabetes therapy. Focus is on control, modeling, and information technology aspects, and future directions of research are also outlined. This survey complements earlier reviews concerning control approaches and application of computers in diabetes car
Mathematical modeling of erythropoietin therapy in uremic anemia. Does it improve cost-effectiveness?
No full textThis paper describes the improvements in r-HuEPO therapy of uremic patients that may be obtained by using a mathematical model of patient response together with a delivery control strategy derived from the theory of industrial control. A mathematical model of r-HuEPO action is presented, and its applicability to dialytic patients is shown. Moreover, a new statistical technique for identifying the parameters of the mathematical model analyzing a patient population is summarized, and a control strategy for r-HuEPO delivery in uremic patients based on a Fuzzy Set Controller is introduced. Some results obtained from simulation, are presented
Temporal abstractions for interpreting diabetic patients monitoring data
No full textIn this article we present a new approach for the intelligent analysis of longitudinal data coming from chronic patients home monitoring. This approach exploits temporal abstractions to pre-process the raw data and to obtain a new time series of abstract episodes, whose features are then interpreted through statistical and probabilistic techniques. We describe in detail an application of the presented technique to the analysis of diabetic patients' data, showing some results obtained on a real case monitored for six months. © 1998 Elsevier Science B.V. All rights reserved
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