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https://www.readbyqxmd.com/read/28269690/smartsock-a-wearable-platform-for-context-aware-assessment-of-ankle-edema
#1
Ramin Fallahzadeh, Mahdi Pedram, Hassan Ghasemzadeh
Ankle edema an important symptom for monitoring patients with chronic systematic diseases. It is an important indicator of onset or exacerbation of a variety of diseases that disturb cardiovascular, renal, or hepatic system such as heart, liver, and kidney failure, diabetes, etc. The current approaches toward edema assessment are conducted during clinical visits. In-clinic assessments, in addition to being burdensome and expensive, are sometimes not reliable and neglect important contextual factors such as patient's physical activity level and body posture...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269002/classification-of-large-scale-fundus-image-data-sets-a-cloud-computing-framework
#2
Sohini Roychowdhury
Large medical image data sets with high dimensionality require substantial amount of computation time for data creation and data processing. This paper presents a novel generalized method that finds optimal image-based feature sets that reduce computational time complexity while maximizing overall classification accuracy for detection of diabetic retinopathy (DR). First, region-based and pixel-based features are extracted from fundus images for classification of DR lesions and vessel-like structures. Next, feature ranking strategies are used to distinguish the optimal classification feature sets...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268569/automatic-detection-of-neovascularization-on-optic-disk-region-with-feature-extraction-and-support-vector-machine
#3
Shuang Yu, Di Xiao, Yogesan Kanagasingam
Neovascularization (NV) is a definitive indicator for the onset of Proliferative Diabetic Retinopathy (PDR). The new vessels are fragile and prone to bleed, leading to high risk of sudden vision loss. Automatic detection of NV is an important task in automatic Diabetic Retinopathy (DR) screening as a consequence of the unmet requirement between the growing number of DR patients and limited number of ophthalmologists. This paper focuses on the computer aided detection of neovascularization in the optic disk region...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227951/smartsock-a-wearable-platform-for-context-aware-assessment-of-ankle-edema
#4
Ramin Fallahzadeh, Mahdi Pedram, Hassan Ghasemzadeh, Ramin Fallahzadeh, Mahdi Pedram, Hassan Ghasemzadeh
Ankle edema an important symptom for monitoring patients with chronic systematic diseases. It is an important indicator of onset or exacerbation of a variety of diseases that disturb cardiovascular, renal, or hepatic system such as heart, liver, and kidney failure, diabetes, etc. The current approaches toward edema assessment are conducted during clinical visits. In-clinic assessments, in addition to being burdensome and expensive, are sometimes not reliable and neglect important contextual factors such as patient's physical activity level and body posture...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227212/classification-of-large-scale-fundus-image-data-sets-a-cloud-computing-framework
#5
Sohini Roychowdhury, Sohini Roychowdhury, Sohini Roychowdhury
Large medical image data sets with high dimensionality require substantial amount of computation time for data creation and data processing. This paper presents a novel generalized method that finds optimal image-based feature sets that reduce computational time complexity while maximizing overall classification accuracy for detection of diabetic retinopathy (DR). First, region-based and pixel-based features are extracted from fundus images for classification of DR lesions and vessel-like structures. Next, feature ranking strategies are used to distinguish the optimal classification feature sets...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226747/automatic-detection-of-neovascularization-on-optic-disk-region-with-feature-extraction-and-support-vector-machine
#6
Shuang Yu, Di Xiao, Yogesan Kanagasingam, Shuang Yu, Di Xiao, Yogesan Kanagasingam, Di Xiao, Yogesan Kanagasingam, Shuang Yu
Neovascularization (NV) is a definitive indicator for the onset of Proliferative Diabetic Retinopathy (PDR). The new vessels are fragile and prone to bleed, leading to high risk of sudden vision loss. Automatic detection of NV is an important task in automatic Diabetic Retinopathy (DR) screening as a consequence of the unmet requirement between the growing number of DR patients and limited number of ophthalmologists. This paper focuses on the computer aided detection of neovascularization in the optic disk region...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28155089/predicting-dpp-iv-inhibitors-with-machine-learning-approaches
#7
Jie Cai, Chanjuan Li, Zhihong Liu, Jiewen Du, Jiming Ye, Qiong Gu, Jun Xu
Dipeptidyl peptidase IV (DPP-IV) is a promising Type 2 diabetes mellitus (T2DM) drug target. DPP-IV inhibitors prolong the action of glucagon-like peptide-1 (GLP-1) and gastric inhibitory peptide (GIP), improve glucose homeostasis without weight gain, edema, and hypoglycemia. However, the marketed DPP-IV inhibitors have adverse effects such as nasopharyngitis, headache, nausea, hypersensitivity, skin reactions and pancreatitis. Therefore, it is still expected for novel DPP-IV inhibitors with minimal adverse effects...
February 2, 2017: Journal of Computer-aided Molecular Design
https://www.readbyqxmd.com/read/28138367/machine-learning-and-data-mining-methods-in-diabetes-research
#8
REVIEW
Ioannis Kavakiotis, Olga Tsave, Athanasios Salifoglou, Nicos Maglaveras, Ioannis Vlahavas, Ioanna Chouvarda
The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide...
2017: Computational and Structural Biotechnology Journal
https://www.readbyqxmd.com/read/28137222/machine-learning-and-molecular-dynamics-based-insights-into-mode-of-actions-of-insulin-degrading-enzyme-modulators
#9
Salma Jamal, Sukriti Goyal, Asheesh Shanker, Abhinav Grover
BACKGROUND: Alzheimer's disease (AD) is one of the most common lethal neurodegenerative disorders having impact on the lives of millions of people worldwide. The disease lacks effective treatment options and the unavailability of the drugs to cure the disease necessitates the development of effectual anti-Alzheimer drugs. Several mechanisms have been reported underlying the association of the two disorders, diabetes and dementia, one among which is the insulin-degrading enzyme (IDE) which is known to degrade insulin as well beta-amyloid peptides...
January 30, 2017: Combinatorial Chemistry & High Throughput Screening
https://www.readbyqxmd.com/read/28113284/type-2-diabetes-screening-test-by-means-of-a-pulse-oximeter
#10
Enrique Moreno, Maria Jose Lujan, Maria Anyo Lujan, Montse Torrres Rusinol, Paqui Juarez Fernandez, Pilar Nunez Manrique, Cristina Aragon Trivino, Magda Miquel, Marife Rodriguez, M Jose Gonzalez Burguillos
In this paper, we propose a method for screening for the presence of type 2 diabetes by means of the signal obtained from a pulse oximeter. The screening system consists of two parts; the first analyses the signal obtained from the pulse oximeter, and the second consists of a machine-learning module. The system consists of a front end that extracts a set of features form the pulse oximeter signal. These features are based on physiological considerations. The set of features were the input of a machine-learning algorithm that determined the class of the input sample, i...
April 20, 2016: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28113188/type-2-diabetes-screening-test-by-means-of-a-pulse-oximeter
#11
Enrique Monte Moreno, Maria Jose Anyo Lujan, Montse Torrres Rusinol, Paqui Juarez Fernandez, Pilar Nunez Manrique, Cristina Aragon Trivino, Magda Pedrosa Miquel, Marife Alvarez Rodriguez, M Jose Gonzalez Burguillos
In this paper, we propose a method for screening for the presence of type 2 diabetes by means of the signal obtained from a pulse oximeter. The screening system consists of two parts: the first analyzes the signal obtained from the pulse oximeter, and the second consists of a machine-learning module. The system consists of a front end that extracts a set of features form the pulse oximeter signal. These features are based on physiological considerations. The set of features were the input of a machine-learning algorithm that determined the class of the input sample, i...
February 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28092510/unobtrusive-and-wearable-systems-for-automatic-dietary-monitoring
#12
Temiloluwa Prioleau, Elliot Moore, Maysam Ghovanloo
The threat of obesity, diabetes, anorexia and bulimia in our society today has motivated extensive research on dietary monitoring. Standard self-report methods such as 24-hour recall and food frequency questionnaires are expensive, burdensome and unrealiable to handle the growing health crisis. Long-term activity monitoring in daily living is a promising approach to provide individuals with quantitative feedback that can encourage healthier habits. Although several studies have attempted automating dietary monitoring using wearable, hand-held, smartobject, and environmental systems, it remains an open research problem...
January 16, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28068461/a-new-prediction-model-for-evaluating-treatment-resistant-depression
#13
Alexander Kautzky, Pia Baldinger-Melich, Georg S Kranz, Thomas Vanicek, Daniel Souery, Stuart Montgomery, Julien Mendlewicz, Joseph Zohar, Alessandro Serretti, Rupert Lanzenberger, Siegfried Kasper
OBJECTIVE: Despite a broad arsenal of antidepressants, about a third of patients suffering from major depressive disorder (MDD) do not respond sufficiently to adequate treatment. Using the data pool of the Group for the Study of Resistant Depression and machine learning, we intended to draw new insights featuring 48 clinical, sociodemographic, and psychosocial predictors for treatment outcome. METHOD: Patients were enrolled starting from January 2000 and diagnosed according to DSM-IV...
February 2017: Journal of Clinical Psychiatry
https://www.readbyqxmd.com/read/28060227/preoperative-opioid-use-is-associated-with-early-revision-after-total-knee-arthroplasty-a-study-of-male-patients-treated-in-the-veterans-affairs-system
#14
Alon Ben-Ari, Howard Chansky, Irene Rozet
BACKGROUND: Opioid use is endemic in the U.S. and is associated with morbidity and mortality. The impact of long-term opioid use on joint-replacement outcomes remains unknown. We tested the hypothesis that use of opioids is associated with adverse outcomes after total knee arthroplasty (TKA). METHODS: We performed a retrospective analysis of patients who had had TKA within the U.S. Veterans Affairs (VA) system over a 6-year period and had been followed for 1 year postoperatively...
January 4, 2017: Journal of Bone and Joint Surgery. American Volume
https://www.readbyqxmd.com/read/28034409/visualizing-the-knowledge-structure-and-evolution-of-big-data-research-in-healthcare-informatics
#15
REVIEW
Dongxiao Gu, Jingjing Li, Xingguo Li, Changyong Liang
BACKGROUND: In recent years, the literature associated with healthcare big data has grown rapidly, but few studies have used bibliometrics and a visualization approach to conduct deep mining and reveal a panorama of the healthcare big data field. METHODS: To explore the foundational knowledge and research hotspots of big data research in the field of healthcare informatics, this study conducted a series of bibliometric analyses on the related literature, including papers' production trends in the field and the trend of each paper's co-author number, the distribution of core institutions and countries, the core literature distribution, the related information of prolific authors and innovation paths in the field, a keyword co-occurrence analysis, and research hotspots and trends for the future...
February 2017: International Journal of Medical Informatics
https://www.readbyqxmd.com/read/28018716/machine-learning-based-detection-of-age-related-macular-degeneration-amd-and-diabetic-macular-edema-dme-from-optical-coherence-tomography-oct-images
#16
Yu Wang, Yaonan Zhang, Zhaomin Yao, Ruixue Zhao, Fengfeng Zhou
Non-lethal macular diseases greatly impact patients' life quality, and will cause vision loss at the late stages. Visual inspection of the optical coherence tomography (OCT) images by the experienced clinicians is the main diagnosis technique. We proposed a computer-aided diagnosis (CAD) model to discriminate age-related macular degeneration (AMD), diabetic macular edema (DME) and healthy macula. The linear configuration pattern (LCP) based features of the OCT images were screened by the Correlation-based Feature Subset (CFS) selection algorithm...
December 1, 2016: Biomedical Optics Express
https://www.readbyqxmd.com/read/28006676/multiple-machine-learning-based-descriptive-and-predictive-workflow-for-the-identification-of-potential-ptp1b-inhibitors
#17
Sharat Chandra, Jyotsana Pandey, Akhilesh Kumar Tamrakar, Mohammad Imran Siddiqi
In insulin and leptin signaling pathway, Protein-Tyrosine Phosphatase 1B (PTP1B) plays a crucial controlling role as a negative regulator, which makes it an attractive therapeutic target for both Type-2 Diabetes (T2D) and obesity. In this work, we have generated classification models by using the inhibition data set of known PTP1B inhibitors to identify new inhibitors of PTP1B utilizing multiple machine learning techniques like naïve Bayesian, random forest, support vector machine and k-nearest neighbors, along with structural fingerprints and selected molecular descriptors...
January 2017: Journal of Molecular Graphics & Modelling
https://www.readbyqxmd.com/read/27982668/data-driven-clinical-and-cost-pathways-for-chronic-care-delivery
#18
Yiye Zhang, Rema Padman
OBJECTIVES: This study illustrates a systematic methodology to embed medical costs into the exact flow of clinical events associated with chronic care delivery. We summarized and visualized the results using clinical and cost data, with the goal of empowering patients and care providers with actionable information as they navigate through a multitude of clinical events and medical expenses. STUDY DESIGN: We analyzed the electronic health records (EHRs) and medication cost data of 288 patients from 2009 to 2011, whose initial diagnoses included chronic kidney disease stage 3, hypertension, and diabetes...
December 2016: American Journal of Managed Care
https://www.readbyqxmd.com/read/27935995/into-the-bowels-of-depression-unravelling-medical-symptoms-associated-with-depression-by-applying-machine-learning-techniques-to-a-community-based-population-sample
#19
Joanna F Dipnall, Julie A Pasco, Michael Berk, Lana J Williams, Seetal Dodd, Felice N Jacka, Denny Meyer
BACKGROUND: Depression is commonly comorbid with many other somatic diseases and symptoms. Identification of individuals in clusters with comorbid symptoms may reveal new pathophysiological mechanisms and treatment targets. The aim of this research was to combine machine-learning (ML) algorithms with traditional regression techniques by utilising self-reported medical symptoms to identify and describe clusters of individuals with increased rates of depression from a large cross-sectional community based population epidemiological study...
2016: PloS One
https://www.readbyqxmd.com/read/27932531/development-of-type-2-diabetes-mellitus-phenotyping-framework-using-expert-knowledge-and-machine-learning-approach
#20
Rina Kagawa, Yoshimasa Kawazoe, Yusuke Ida, Emiko Shinohara, Katsuya Tanaka, Takeshi Imai, Kazuhiko Ohe
BACKGROUND: Phenotyping is an automated technique that can be used to distinguish patients based on electronic health records. To improve the quality of medical care and advance type 2 diabetes mellitus (T2DM) research, the demand for T2DM phenotyping has been increasing. Some existing phenotyping algorithms are not sufficiently accurate for screening or identifying clinical research subjects. OBJECTIVE: We propose a practical phenotyping framework using both expert knowledge and a machine learning approach to develop 2 phenotyping algorithms: one is for screening; the other is for identifying research subjects...
December 7, 2016: Journal of Diabetes Science and Technology
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