Computer methods and programs in biomedicine
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Comput Methods Programs Biomed · Sep 2019
An intelligent warning model for early prediction of cardiac arrest in sepsis patients.
Sepsis-associated cardiac arrest is a common issue with the low survival rate. Early prediction of cardiac arrest can provide the time required for intervening and preventing its onset in order to reduce mortality. Several studies have been conducted to predict cardiac arrest using machine learning. However, no previous research has used machine learning for predicting cardiac arrest in adult sepsis patients. Moreover, the potential of some techniques, including ensemble algorithms, has not yet been addressed in improving the prediction outcomes. It is required to find methods for generating high-performance predictions with sufficient time lapse before the arrest. In this regard, various variables and parameters should also been examined. ⋯ We illustrated that machine learning techniques, especially ensemble algorithms have high potentials to be used in prognostic systems for sepsis patients. The proposed model, in comparison with the exiting warning systems including APACHE II and MEWS, significantly improved the evaluation criteria. According to the results, the time series dynamics of vital signs are of great importance in the prediction of cardiac arrest incidence in sepsis patients.
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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.
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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.
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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.