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Health Information Science and Systems

P Parthasarathy, S Vivekanandan
Uric acid biosensors for arthritis disease has been developed for the specific selection of uricase enzyme film thickness coated over the TiO2 -CeO2 nano-composite matrix is modelled mathematically. This model is purely based on R-diffusion conditions with irreversible first-order catalytic reactions. By arithmetical method, the impact of the thickness of enzyme layer on the current response of the biosensor was explored. This article displays a structure for choice of the enzyme layer thickness, guaranteeing the adequately stable sensitivity of a biosensor in a required extent of the maximal enzymatic rate...
December 2018: Health Information Science and Systems
Siyu Zhou, Atsushi Ogihara, Shoji Nishimura, Qun Jin
Purpose: Rapid developments in information technology have enabled wearable devices to be applied in the health field. In elderly adults, wearable devices aid in data collection and exerts a positive effect on their social capital. This study evaluated the changes in these two parameters among elderly adults using wearable devices, and analyzed the effect of these devices on their daily lives. Methods: We selected 18 elderly people using wearable devices, between February and May 2017...
December 2018: Health Information Science and Systems
Humayun Kiser, Tasmina Nasrin
Maternal and child mortality are the key indicators of health and development of the country. Maternal and child health are interconnected to prenatal care. Consulting a doctor at the prenatal stage will not only ensure mother's and her unborn babies' safety, but also has a great influence to reduce the maternal and infant mortality. In this paper, an attempt has been made to analyze the status of prenatal care provided by the qualified doctor among pregnant mothers in Bangladesh. Data and required information of 8793 reproductive women were collected from the Bangladesh Demographic and Health Survey (BDHS) 2014...
December 2018: Health Information Science and Systems
Siuly Siuly, Runhe Huang, Mahmoud Daneshmand
No abstract text is available yet for this article.
December 2018: Health Information Science and Systems
A I Shahin, Yanhui Guo, K M Amin, Amr A Sharawi
Background: White blood cells (WBCs) play a crucial role in the diagnosis of many diseases according to their numbers or morphology. The recent digital pathology equipments investigate and analyze the blood smear images automatically. The previous automated segmentation algorithms worked on healthy and non-healthy WBCs separately. Also, such algorithms had employed certain color components which leak adaptively with different datasets. Methods: In this paper, a novel segmentation algorithm for WBCs in the blood smear images is proposed using multi-scale similarity measure based on the neutrosophic domain...
December 2018: Health Information Science and Systems
Feng-Cong Li, Yi-Nan Zhao, Peng-Cheng Gong, Li Feng, Xiang-Kui Wan, Yan Li
We propose a beamforming algorithm based on waveform diversity for hyperthermia treatment of breast cancer using an ultrasonic array. The introduced array has a structure with a network connecting the feeding nodes and the array elements, and the objective of the algorithm is to train the weight matrix of the network to minimize the difference between the generated beam pattern and the ideal one. The training procedure of the algorithm, which is inspired by the idea of machine learning, comprises three parts: forward calculation, comparison, and backward calculation...
December 2017: Health Information Science and Systems
Mohamed S Barakat, Matthew Field, Aditya Ghose, David Stirling, Lois Holloway, Shalini Vinod, Andre Dekker, David Thwaites
According to the estimations of the World Health Organization and the International Agency for Research in Cancer, lung cancer is the most common cause of death from cancer worldwide. The last few years have witnessed a rise in the attention given to the use of clinical decision support systems in medicine generally and in cancer in particular. These can predict patients' likelihood of survival based on analysis of and learning from previously treated patients. The datasets that are mined for developing clinical decision support functionality are often incomplete, which adversely impacts the quality of the models developed and the decision support offered...
December 2017: Health Information Science and Systems
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
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
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
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
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
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
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
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
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
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
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
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
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
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