Particle Swarm Optimization Based Adaptable Predictor of Glycemia Values

Macaš, Martin; Lhotska, Lenka; Stechova, Katerina; Piťhová, Pavlína; Saiti, Kyriaki

doi:10.1109/cybconf.2017.7985799

Cited by 5 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The aim of the project was interdisciplinary research focused on decision support in diabetic patient treatment. The core is the design of a software prototype (SW)-an application for cell phones-that offers to a diabetic patient treated by an insulin pump an advanced advice on insulin dose based on previous individual experience (self-learning algorithm) [57][58][59]. Inputs for the SW are (1) complex information on food content (including glycaemia index and reflecting the previous patient's glycaemia reaction to the similar type of food); (2) the amount of active insulin (information available from the insulin pump); and (3) additional information on physical activity and stress (psychic, illness, etc.).…”

Section: Intelligent Methods For Evaluation Of Long-term Eeg Recordin...mentioning

confidence: 99%

Artificial Intelligence and Women Researchers in the Czech Republic

Lhotská

Štěpánková

2022

Applied Sciences

View full text Add to dashboard Cite

Artificial intelligence as a research area has been continuously growing for several decades. Many applications were developed in various domains. Medicine and health care have attracted more intensive attention thanks to rapid technological development that has accelerated generation of large volumes of data requiring intelligent analysis and evaluation. This article illustrates, through examples of women researchers and selected AI projects in medicine, the wide spectrum of applications developed during the last fifteen years in the Czech Republic, and in particular at the Czech Technical University in Prague. Women researchers played an important and irreplaceable role since the advent of AI research in the Czech Republic. By their example, they motivated many young female students to join the community and start their research career in the AI area. They frequently participated in research projects led by the senior women researchers. The presented overview of projects illustrates the diversity of the medical area and the potential of AI methods that can be used for solving data- and knowledge-intensive problems. We briefly touch on the AI study programs. In conclusion, we point out the future challenges in AI and its applications in medicine and health care.

show abstract

Section: Intelligent Methods For Evaluation Of Long-term Eeg Recordin...mentioning

confidence: 99%

Artificial Intelligence and Women Researchers in the Czech Republic

Lhotská

Štěpánková

2022

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…To this model, we can include a moving-average (MA) component to build what we call an ARMA [62], or ARIMA [63] process. We can incorporate past CHO intakes and insulin boluses into an AR model as exogenous inputs (ARX model) [47], [64]- [67]. The MA and exogenous components can both be integrated together into an ARMAX process [62], [67] or ARIMAX process [68].…”

Section: B Glucose Predictive Modelsmentioning

confidence: 99%

“…We can incorporate past CHO intakes and insulin boluses into an AR model as exogenous inputs (ARX model) [47], [64]- [67]. The MA and exogenous components can both be integrated together into an ARMAX process [62], [67] or ARIMAX process [68]. The ARIMAX includes an Integration (or derivative) component which may be needed when the time-series that is being predicted is non-stationary.…”

Section: B Glucose Predictive Modelsmentioning

confidence: 99%

See 1 more Smart Citation

GLYFE: review and benchmark of personalized glucose predictive models in type 1 diabetes

Bois

Yacoubi

Ammi

2021

Med Biol Eng Comput

View full text Add to dashboard Cite

Due to the sensitive nature of diabetes-related data, preventing them from being shared between studies, progress in the field of glucose prediction is hard to assess. To address this issue, we present GLYFE (GLYcemia Forecasting Evaluation), a benchmark of machine-learning-based glucose-predictive models.To ensure the reproducibility of the results and the usability of the benchmark in the future, we provide extensive details about the data flow. Two datasets are used, the first comprising 10 insilico adults from the UVA/Padova Type 1 Diabetes Metabolic Simulator (T1DMS) and the second being made of 6 real type-1 diabetic patients coming from the OhioT1DM dataset. The predictive models are personalized to the patient and evaluated on 3 different prediction horizons (30, 60, and 120 minutes) with metrics assessing their accuracy and clinical acceptability.The results of nine different models coming from the glucoseprediction literature are presented. First, they show that standard autoregressive linear models are outclassed by kernel-based nonlinear ones and neural networks. In particular, the support vector regression model stands out, being at the same time one of the most accurate and clinically acceptable model. Finally, the relative performances of the models are the same for both datasets. This shows that, even though data simulated by T1DMS are not fully representative of real-world data, they can be used to assess the forecasting ability of the glucose-predictive models.Those results serve as a basis of comparison for future studies. In a field where data are hard to obtain, and where the comparison of results from different studies is often irrelevant, GLYFE gives the opportunity of gathering researchers around a standardized common environment.

show abstract