Computer methods and programs in biomedicine
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Comput Methods Programs Biomed · Jan 2020
Two-step deep neural network for segmentation of deep white matter hyperintensities in migraineurs.
Patients with migraine show an increased presence of white matter hyperintensities (WMHs), especially deep WMHs. Segmentation of small, deep WMHs is a critical issue in managing migraine care. Here, we aim to develop a novel approach to segmenting deep WMHs using deep neural networks based on the U-Net. ⋯ We developed a novel segmentation framework tailored for deep WMHs using U-Net. Our algorithm is open-access to promote future research in quantifying deep WMHs and might contribute to the effective management of WMHs in migraineurs.
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Comput Methods Programs Biomed · Dec 2019
Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records.
Diabetes is responsible for considerable morbidity, healthcare utilisation and mortality in both developed and developing countries. Currently, methods of treating diabetes are inadequate and costly so prevention becomes an important step in reducing the burden of diabetes and its complications. Electronic health records (EHRs) for each individual or a population have become important tools in understanding developing trends of diseases. Using EHRs to predict the onset of diabetes could improve the quality and efficiency of medical care. In this paper, we apply a wide and deep learning model that combines the strength of a generalised linear model with various features and a deep feed-forward neural network to improve the prediction of the onset of type 2 diabetes mellitus (T2DM). ⋯ Our algorithm has further optimised the prediction of diabetes onset using a novel state-of-the-art machine learning algorithm: the wide and deep learning neural network architecture.
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Comput Methods Programs Biomed · Dec 2019
Developing an Indonesia's health literacy short-form survey questionnaire (HLS-EU-SQ10-IDN) using the feature selection and genetic algorithm.
Measuring health literacy becomes more important because its association with health status and healthcare outcomes. Studies have developed at least 133 measurement tools for health literacy. HLS-EU-Q47 is a questionnaire consisting of 12 sub-dimensions and 47 questions developed by the Europe Health Literacy Consortium. Many countries in Europe and Asia have used HLS-EU-Q47 as a tool for measuring health literacy in the general public. Indonesia has conducted general health literacy survey using HLS-EU-Q47 but finding the difficulties because of the time-consuming interview. A shorter version of HLS-EU-Q47 is needed to apply in health literacy researches in Indonesia. This paper reports the results of feature reduction to develop a short Indonesian version HLS-EU questionnaire and measures the accuracy of the model compared with other short form like HLS-EU-SQ16 or HLS-SF12. ⋯ A data mining technique using feature selection with combination of genetic algorithm and k-NN algorithm was applied to develop a short version questionnaire and proved to have better accuracy, as compared with the short version developed by traditional statistical technique.
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Comput Methods Programs Biomed · Oct 2019
Simulating blood pressure and end tidal CO2 in a CPR training manikin.
The American Heart Association supports titrating the mechanics of cardiopulmonary resuscitation (CPR) to blood pressure and end tidal carbon dioxide (ETCO2) thresholds during in-hospital cardiac arrest. However, current CPR manikin training systems do not prepare clinicians to use these metrics to gauge their performance, and currently provide only feedback on hand placement, depth, rate, release, and interruptions of chest compressions. We addressed this training hardware deficiency through development of a novel CPR training manikin that displays simulated blood pressure and ETCO2 waveforms in real time on a simulated clinical monitor visible to the learner, reflecting the mechanics of chest compressions provided to the manikin. Such a manikin could improve clinicians' CPR technique while also training them to titrate CPR quality to physiologic blood pressure and ETCO2 targets as performance indicators. ⋯ A CPR manikin that simulates blood pressure and ETCO2 was successfully developed with acceptable relevance, performance and feasibility as a CPR quality training tool.
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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.