Computer methods and programs in biomedicine
-
Comput Methods Programs Biomed · Sep 2019
Minimizing postprandial hypoglycemia in Type 1 diabetes patients using multiple insulin injections and capillary blood glucose self-monitoring with machine learning techniques.
Diabetic patients treated with intensive insulin therapies require a tight glycemic control and may benefit from advanced tools to predict blood glucose (BG) concentration levels and hypo/hyperglycemia events. Prediction systems using machine learning techniques have mainly focused on applications for sensor augmented pump (SAP) therapy. In contrast, insulin bolus calculators that rely on BG prediction for multiple daily insulin (MDI) injections for patients under self-monitoring blood glucose (SMBG) are scarce because of insufficient data sources and limited prediction capability of forecasting models. ⋯ The results indicated a decrease of 37% in the median number of postprandial hypoglycemias median decrease of 44% for hypoglycemias of 70 mg/dL and 54 mg/dL, respectively. This dramatic reduction makes this method a good candidate to be integrated into any Decision Support System for diabetes management.
-
Comput Methods Programs Biomed · Sep 2019
Automatic Multi-Level In-Exhale Segmentation and Enhanced Generalized S-Transform for wheezing detection.
Wheezing is a common symptom of patients caused by asthma and chronic obstructive pulmonary diseases. Wheezing detection identifies wheezing lung sounds and helps physicians in diagnosis, monitoring, and treatment of pulmonary diseases. Different from the traditional way to detect wheezing sounds using digital image process methods, automatic wheezing detection uses computerized tools or algorithms to objectively and accurately assess and evaluate lung sounds. We propose an innovative machine learning-based approach for wheezing detection. The phases of the respiratory sounds are separated automatically and the wheezing features are extracted accordingly to improve the classification accuracy. ⋯ The comparison results indicate the very good performance of the proposed methods for long-term wheezing monitoring and telemedicine.
-
Comput Methods Programs Biomed · Aug 2019
Influence of passive elements on prediction of intradiscal pressure and muscle activation in lumbar musculoskeletal models.
The objective of this study was to investigate the effect of incorporating various passive elements, which could represent combined or individual effects of intervertebral disc, facet articulation and ligaments, on the prediction of lumbar muscle activation and L4-L5 intradiscal pressure. ⋯ Caution must be taken while modeling facet articulation as elements with rotational stiffness, as they may lead to overestimation of intradiscal pressure in trunk axial rotation. The inclusion of ligaments as spring-like elements may improve the simulation of flexion-relaxation phenomenon in trunk flexion. Future models considering detailed properties of passive elements are needed to allow more access to understanding the mechanics of the lumbar spine.
-
Comput Methods Programs Biomed · Jul 2019
Corrigendum to "Haemodynamic impacts of myocardial bridge length: A congenital heart disease" [Comput. Methods Progr. Biomed., 175 (2019) 25-33].
In this corrigendum, the authors would like to acknowledge the cardiac catheterization laboratory staff at Tehran Heart Center for their assistance in performing the studies under the ethics application TH38-02-2017-20.
-
Comput Methods Programs Biomed · May 2019
Semiparametric competing risks regression under interval censoring using the R package intccr.
Competing risk data are frequently interval-censored in real-world applications, that is, the exact event time is not precisely observed but is only known to lie between two time points such as clinic visits. This type of data requires special handling because the actual event times are unknown. To deal with this problem we have developed an easy-to-use open-source statistical software. ⋯ The R package intccr provides a convenient and flexible software for the analysis of the cumulative incidence function based on interval-censored competing risks data.