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Health Information Science and Systems

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https://www.readbyqxmd.com/read/29163933/a-novel-glomerular-basement-membrane-segmentation-using-neutrsophic-set-and-shearlet-transform-on-microscopic-images
#1
Yanhui Guo, Amira S Ashour, Baiqing Sun
Purpose: Glomerular basement membrane segmentation is an ultimate step in several image processing applications for kidney diseases and abnormalities in microscopic images. However, extracting the glomerular basement membrane (GBM) regions accurately is considered challenging because of the large variants in the microscopic images. The contribution of this work is to propose a computer-aided detection system to provide accurate GBM segmentation. Methods: A novel GBM segmentation algorithm is developed based on neutrsophic set and shearlet transform...
December 2017: Health Information Science and Systems
https://www.readbyqxmd.com/read/29147563/a-novel-microaneurysms-detection-approach-based-on-convolutional-neural-networks-with-reinforcement-sample-learning-algorithm
#2
Umit Budak, Abdulkadir Şengür, Yanhui Guo, Yaman Akbulut
Microaneurysms (MAs) are known as early signs of diabetic-retinopathy which are called red lesions in color fundus images. Detection of MAs in fundus images needs highly skilled physicians or eye angiography. Eye angiography is an invasive and expensive procedure. Therefore, an automatic detection system to identify the MAs locations in fundus images is in demand. In this paper, we proposed a system to detect the MAs in colored fundus images. The proposed method composed of three stages. In the first stage, a series of pre-processing steps are used to make the input images more convenient for MAs detection...
December 2017: Health Information Science and Systems
https://www.readbyqxmd.com/read/29147562/cognitive-computing-and-escience-in-health-and-life-science-research-artificial-intelligence-and-obesity-intervention-programs
#3
REVIEW
Thomas Marshall, Tiffiany Champagne-Langabeer, Darla Castelli, Deanna Hoelscher
Objective: To present research models based on artificial intelligence and discuss the concept of cognitive computing and eScience as disruptive factors in health and life science research methodologies. Methods: The paper identifies big data as a catalyst to innovation and the development of artificial intelligence, presents a framework for computer-supported human problem solving and describes a transformation of research support models. This framework includes traditional computer support; federated cognition using machine learning and cognitive agents to augment human intelligence; and a semi-autonomous/autonomous cognitive model, based on deep machine learning, which supports eScience...
December 2017: Health Information Science and Systems
https://www.readbyqxmd.com/read/29142742/supporting-breast-cancer-decisions-using-formalized-guidelines-and-experts-decision-patterns-initial-prototype-and-evaluation
#4
Dennis Andrzejewski, Rüdiger Breitschwerdt, Michael Fellmann, Eberhard Beck
Transparent decisions and its documentation of breast cancer patients' therapy are getting more important especially since modern therapeutic approaches favor personalized forms of treatment. The medical decisions for a treatment are very complex, because there are rules and different options for each patient. To support the decision process, we analyzed the current decision rules and implemented them in a prototype of a rule-based expert system. Thus, this system shall support the quality assurance regarding transparent documentation of individualized therapeutic decisions...
December 2017: Health Information Science and Systems
https://www.readbyqxmd.com/read/29142741/statistical-sleep-pattern-modelling-for-sleep-quality-assessment-based-on-sound-events
#5
Hongle Wu, Takafumi Kato, Masayuki Numao, Ken-Ichi Fukui
A good sleep is important for a healthy life. Recently, several consumer sleep devices have emerged on the market claiming that they can provide personal sleep monitoring; however, many of them require additional hardware or there is a lack of scientific evidence regarding their reliability. In this paper we proposed a novel method to assess the sleep quality through sound events recorded in the bedroom. We used subjective sleep quality as training label, combined several machine learning approaches including kernelized self organizing map, hierarchical clustering and hidden Markov model, obtained the models to indicate the sleep pattern of specific quality level...
December 2017: Health Information Science and Systems
https://www.readbyqxmd.com/read/29142740/combined-empirical-mode-decomposition-and-texture-features-for-skin-lesion-classification-using-quadratic-support-vector-machine
#6
Maram A Wahba, Amira S Ashour, Sameh A Napoleon, Mustafa M Abd Elnaby, Yanhui Guo
Purpose: Basal cell carcinoma is one of the most common malignant skin lesions. Automated lesion identification and classification using image processing techniques is highly required to reduce the diagnosis errors. Methods: In this study, a novel technique is applied to classify skin lesion images into two classes, namely the malignant Basal cell carcinoma and the benign nevus. A hybrid combination of bi-dimensional empirical mode decomposition and gray-level difference method features is proposed after hair removal...
December 2017: Health Information Science and Systems
https://www.readbyqxmd.com/read/29142739/classification-of-amyotrophic-lateral-sclerosis-disease-based-on-convolutional-neural-network-and-reinforcement-sample-learning-algorithm
#7
Abdulkadir Sengur, Yaman Akbulut, Yanhui Guo, Varun Bajaj
Electromyogram (EMG) signals contain useful information of the neuromuscular diseases like amyotrophic lateral sclerosis (ALS). ALS is a well-known brain disease, which can progressively degenerate the motor neurons. In this paper, we propose a deep learning based method for efficient classification of ALS and normal EMG signals. Spectrogram, continuous wavelet transform (CWT), and smoothed pseudo Wigner-Ville distribution (SPWVD) have been employed for time-frequency (T-F) representation of EMG signals. A convolutional neural network is employed to classify these features...
December 2017: Health Information Science and Systems
https://www.readbyqxmd.com/read/29109858/using-neutrosophic-graph-cut-segmentation-algorithm-for-qualified-rendering-image-selection-in-thyroid-elastography-video
#8
Yanhui Guo, Shuang-Quan Jiang, Baiqing Sun, Siuly Siuly, Abdulkadir Şengür, Jia-Wei Tian
Recently, elastography has become very popular in clinical investigation for thyroid cancer detection and diagnosis. In elastogram, the stress results of the thyroid are displayed using pseudo colors. Due to variation of the rendering results in different frames, it is difficult for radiologists to manually select the qualified frame image quickly and efficiently. The purpose of this study is to find the qualified rendering result in the thyroid elastogram. This paper employs an efficient thyroid ultrasound image segmentation algorithm based on neutrosophic graph cut to find the qualified rendering images...
December 2017: Health Information Science and Systems
https://www.readbyqxmd.com/read/29109857/an-optimum-allocation-sampling-based-feature-extraction-scheme-for-distinguishing-seizure-and-seizure-free-eeg-signals
#9
Sachin Taran, Varun Bajaj, Siuly Siuly
Epileptic seizure is the common neurological disorder, which is generally identified by electroencephalogram (EEG) signals. In this paper, a new feature extraction methodology based on optimum allocation sampling (OAS) and Teager energy operator (TEO) is proposed for detection of seizure EEG signals. The OAS scheme selects the finite length homogeneous sequence from non-homogeneous recorded EEG signal. The trend of selected sequence by OAS is still non-linear, which is analyzed by non-linear operator TEO. The TEO convert non-linear but homogenous EEG sequence into amplitude-frequency modulated (AM-FM) components...
December 2017: Health Information Science and Systems
https://www.readbyqxmd.com/read/29081974/improving-patient-engagement-in-self-measured-blood-pressure-monitoring-using-a-mobile-health-technology
#10
Alan L Kaplan, Erica R Cohen, Eyal Zimlichman
OBJECTIVE: To understand usage patterns and clinical efficacy of Hello Heart, an mHealth technology application designed to facilitate patient engagement in managing hypertension. METHODS: In this single-arm observational study, all subjects with ≥2 blood pressure (BP) recordings were included. The cohort was divided into subgroups by weeks passed since download that patients were still recording measurements. Changes in BP were compared between subgroups. RESULTS: Of 5115 eligible subjects, 3803 (74%) recorded BP for ≥2 weeks...
December 2017: Health Information Science and Systems
https://www.readbyqxmd.com/read/29067166/the-potential-adoption-benefits-and-challenges-of-loinc-codes-in-a-laboratory-department-a-case-study
#11
Chukwuemeka Uchegbu, Xia Jing
BACKGROUND: Logical Observation Identifiers Names and Codes (LOINC) are a standard for identifying and reporting laboratory investigations that were developed and are maintained by the Regenstrief Institute. LOINC codes have been adopted globally by hospitals, government agencies, laboratories, and research institutions. There are still many healthcare organizations, however, that have not adopted LOINC codes, including rural hospitals in low- and middle- income countries. Hence, organizations in these areas do not receive the benefits that accrue with the adoption of LOINC codes...
December 2017: Health Information Science and Systems
https://www.readbyqxmd.com/read/29062476/building-diversified-multiple-trees-for-classification-in-high-dimensional-noisy-biomedical-data
#12
Jiuyong Li, Lin Liu, Jixue Liu, Ryan Green
PURPOSE: It is common that a trained classification model is applied to the operating data that is deviated from the training data because of noise. This paper will test an ensemble method, Diversified Multiple Tree (DMT), on its capability for classifying instances in a new laboratory using the classifier built on the instances of another laboratory. METHODS: DMT is tested on three real world biomedical data sets from different laboratories in comparison with four benchmark ensemble methods, AdaBoost, Bagging, Random Forests, and Random Trees...
December 2017: Health Information Science and Systems
https://www.readbyqxmd.com/read/29038733/mining-comorbidity-patterns-using-retrospective-analysis-of-big-collection-of-outpatient-records
#13
Svetla Boytcheva, Galia Angelova, Zhivko Angelov, Dimitar Tcharaktchiev
BACKGROUND: Studying comorbidities of disorders is important for detection and prevention. For discovering frequent patterns of diseases we can use retrospective analysis of population data, by filtering events with common properties and similar significance. Most frequent pattern mining methods do not consider contextual information about extracted patterns. Further data mining developments might enable more efficient applications in specific tasks like comorbidities identification. METHODS: We propose a cascade data mining approach for frequent pattern mining enriched with context information, including a new algorithm MIxCO for maximal frequent patterns mining...
December 2017: Health Information Science and Systems
https://www.readbyqxmd.com/read/29038732/progressive-sampling-based-bayesian-optimization-for-efficient-and-automatic-machine-learning-model-selection
#14
Xueqiang Zeng, Gang Luo
PURPOSE: Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed...
December 2017: Health Information Science and Systems
https://www.readbyqxmd.com/read/28413630/patient-healthcare-trajectory-an-essential-monitoring-tool-a-systematic-review
#15
REVIEW
Jessica Pinaire, Jérôme Azé, Sandra Bringay, Paul Landais
BACKGROUND: Patient healthcare trajectory is a recent emergent topic in the literature, encompassing broad concepts. However, the rationale for studying patients' trajectories, and how this trajectory concept is defined remains a public health challenge. Our research was focused on patients' trajectories based on disease management and care, while also considering medico-economic aspects of the associated management. We illustrated this concept with an example: a myocardial infarction (MI) occurring in a patient's hospital trajectory of care...
December 2017: Health Information Science and Systems
https://www.readbyqxmd.com/read/27280018/predict-ml-a-tool-for-automating-machine-learning-model-building-with-big-clinical-data
#16
Gang Luo
BACKGROUND: Predictive modeling is fundamental to transforming large clinical data sets, or "big clinical data," into actionable knowledge for various healthcare applications. Machine learning is a major predictive modeling approach, but two barriers make its use in healthcare challenging. First, a machine learning tool user must choose an algorithm and assign one or more model parameters called hyper-parameters before model training. The algorithm and hyper-parameter values used typically impact model accuracy by over 40 %, but their selection requires many labor-intensive manual iterations that can be difficult even for computer scientists...
2016: Health Information Science and Systems
https://www.readbyqxmd.com/read/27217953/ethiopic-maternal-care-data-mining-discovering-the-factors-that-affect-postnatal-care-visit-in-ethiopia
#17
Geletaw Sahle
BACKGROUND: Improving maternal health and reducing maternal mortality rate are key concerns. One of the eight millennium development goals adopted at the millennium summit, was to improve maternal health in Ethiopia. This leads towards discovering the factors that hinder postnatal care visit in Ethiopia. METHODS: In this research, knowledge discovery from data (KDD) was applied to identify the factors that hinder postnatal care visits in Ethiopia. Decision tree (using J48 algorithm) and rule induction (using JRip algorithm) techniques were applied on 6558 records of Ethiopian demographic and health survey data...
2016: Health Information Science and Systems
https://www.readbyqxmd.com/read/27099714/a-systematic-exploration-of-the-micro-blog-feature-space-for-teens-stress-detection
#18
Liang Zhao, Qi Li, Yuanyuan Xue, Jia Jia, Ling Feng
BACKGROUND: In the modern stressful society, growing teenagers experience severe stress from different aspects from school to friends, from self-cognition to inter-personal relationship, which negatively influences their smooth and healthy development. Being timely and accurately aware of teenagers psychological stress and providing effective measures to help immature teenagers to cope with stress are highly valuable to both teenagers and human society. Previous work demonstrates the feasibility to sense teenagers' stress from their tweeting contents and context on the open social media platform-micro-blog...
2016: Health Information Science and Systems
https://www.readbyqxmd.com/read/26958341/automatically-explaining-machine-learning-prediction-results-a-demonstration-on-type-2-diabetes-risk-prediction
#19
Gang Luo
BACKGROUND: Predictive modeling is a key component of solutions to many healthcare problems. Among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. Most machine learning models give no explanation for their prediction results, whereas interpretability is essential for a predictive model to be adopted in typical healthcare settings. METHODS: This paper presents the first complete method for automatically explaining results for any machine learning predictive model without degrading accuracy...
2016: Health Information Science and Systems
https://www.readbyqxmd.com/read/26889379/erratum-to-usability-study-of-a-simplified-electroencephalograph-as-a-health-care-system
#20
Shinichi Motomura, Muneaki Ohshima, Ning Zhong
[This corrects the article DOI: 10.1186/s13755-015-0012-z.].
2016: Health Information Science and Systems
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