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https://www.readbyqxmd.com/read/28644378/an-energy-efficient-multi-tier-architecture-for-fall-detection-using-smartphones
#1
M Amac Guvensan, A Oguz Kansiz, N Cihan Camgoz, H Irem Turkmen, A Gokhan Yavuz, M Elif Karsligil
Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms...
June 23, 2017: Sensors
https://www.readbyqxmd.com/read/28638239/using-transfer-learning-for-improved-mortality-prediction-in-a-data-scarce-hospital-setting
#2
Thomas Desautels, Jacob Calvert, Jana Hoffman, Qingqing Mao, Melissa Jay, Grant Fletcher, Chris Barton, Uli Chettipally, Yaniv Kerem, Ritankar Das
Algorithm-based clinical decision support (CDS) systems associate patient-derived health data with outcomes of interest, such as in-hospital mortality. However, the quality of such associations often depends on the availability of site-specific training data. Without sufficient quantities of data, the underlying statistical apparatus cannot differentiate useful patterns from noise and, as a result, may underperform. This initial training data burden limits the widespread, out-of-the-box, use of machine learning-based risk scoring systems...
2017: Biomedical Informatics Insights
https://www.readbyqxmd.com/read/28631139/a-systematic-review-of-wearable-patient-monitoring-systems-current-challenges-and-opportunities-for-clinical-adoption
#3
Mirza Mansoor Baig, Hamid GholamHosseini, Aasia A Moqeem, Farhaan Mirza, Maria Lindén
The aim of this review is to investigate barriers and challenges of wearable patient monitoring (WPM) solutions adopted by clinicians in acute, as well as in community, care settings. Currently, healthcare providers are coping with ever-growing healthcare challenges including an ageing population, chronic diseases, the cost of hospitalization, and the risk of medical errors. WPM systems are a potential solution for addressing some of these challenges by enabling advanced sensors, wearable technology, and secure and effective communication platforms between the clinicians and patients...
July 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/28615794/deepinfer-open-source-deep-learning-deployment-toolkit-for-image-guided-therapy
#4
Alireza Mehrtash, Mehran Pesteie, Jorden Hetherington, Peter A Behringer, Tina Kapur, William M Wells, Robert Rohling, Andriy Fedorov, Purang Abolmaesumi
Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration...
February 11, 2017: Proceedings of SPIE
https://www.readbyqxmd.com/read/28613164/model-based-generation-of-large-databases-of-cardiac-images-synthesis-of-pathological-cine-mr-sequences-from-real-healthy-cases
#5
Nicolas Duchateau, Maxime Sermesant, Herve Delingette, Nicholas Ayache
Collecting large databases of annotated medical images is crucial for the validation and testing of feature extraction, statistical analysis and machine learning algorithms. Recent advances in cardiac electromechanical modeling and image synthesis provided a framework to generate synthetic images based on realistic mesh simulations. Nonetheless, their potential to augment an existing database with large amounts of synthetic cases requires further investigation. We build upon these works and propose a revised scheme for synthesizing pathological cardiac sequences from real healthy sequences...
June 9, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28599600/transcriptional-changes-in-the-mouse-retina-following-ocular-blast-injury-a-role-for-the-immune-system
#6
Felix L Struebing, Rebecca King, Ying Li, Micah A Chrenek, Polina N Lyuboslavsky, Curran S Sidhu, P Michael Iuvone, Eldon E Geisert
Ocular blast injury is a major medical concern for soldiers and explosion victims due to poor visual outcomes. To define the changes in gene expression following a blast injury to the eye, we examined retinal RNA expression in 54 mouse strains five days after a single 50psi overpressure air wave blast injury. We observe that almost 40% of genes are differentially expressed with a false discovery rate of <0.001, even though the nominal changes in RNA expression are rather small. Moreover, we find through machine learning approaches that genetic networks related to the innate and acquired immune system are activated...
June 9, 2017: Journal of Neurotrauma
https://www.readbyqxmd.com/read/28599216/fuzzy-risk-stratification-and-risk-assessment-model-for-clinical-monitoring-in-the-icu
#7
Albion Dervishi
BACKGROUND: The decisions that clinicians make in intensive care units (ICUs) based on monitored parameters reflecting physiological deterioration are of major medical and biomedical engineering interest. These parameters have been investigated and assessed for their usefulness in risk assessment. METHODS: Totally, 127 ICU adult patients were studied. They were selected from a MIMIC II Waveform Database Matched Subset and had continuous monitoring of heart rate, invasive blood pressure, and oxygen saturation...
June 2, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28596461/classification-of-pulmonary-pathology-from-breath-sounds-using-the-wavelet-packet-transform-and-an-extreme-learning-machine
#8
Rajkumar Palaniappan, Kenneth Sundaraj, Sebastian Sundaraj, N Huliraj, S S Revadi
BACKGROUND: Auscultation is a medical procedure used for the initial diagnosis and assessment of lung and heart diseases. From this perspective, we propose assessing the performance of the extreme learning machine (ELM) classifiers for the diagnosis of pulmonary pathology using breath sounds. METHODS: Energy and entropy features were extracted from the breath sound using the wavelet packet transform. The statistical significance of the extracted features was evaluated by one-way analysis of variance (ANOVA)...
June 8, 2017: Biomedizinische Technik. Biomedical Engineering
https://www.readbyqxmd.com/read/28585725/laboratory-parameter-based-machine-learning-model-for-excluding-non-alcoholic-fatty-liver-disease-nafld-in-the-general-population
#9
T C-F Yip, A J Ma, V W-S Wong, Y-K Tse, H L-Y Chan, P-C Yuen, G L-H Wong
BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) affects 20%-40% of the general population in developed countries and is an increasingly important cause of hepatocellular carcinoma. Electronic medical records facilitate large-scale epidemiological studies, existing NAFLD scores often require clinical and anthropometric parameters that may not be captured in those databases. AIM: To develop and validate a laboratory parameter-based machine learning model to detect NAFLD for the general population...
June 6, 2017: Alimentary Pharmacology & Therapeutics
https://www.readbyqxmd.com/read/28580415/kid-project-an-internet-based-digital-video-atlas-of-capsule-endoscopy-for-research-purposes
#10
Anastasios Koulaouzidis, Dimitris K Iakovidis, Diana E Yung, Emanuele Rondonotti, Uri Kopylov, John N Plevris, Ervin Toth, Abraham Eliakim, Gabrielle Wurm Johansson, Wojciech Marlicz, Georgios Mavrogenis, Artur Nemeth, Henrik Thorlacius, Gian Eugenio Tontini
BACKGROUND AND AIMS:  Capsule endoscopy (CE) has revolutionized small-bowel (SB) investigation. Computational methods can enhance diagnostic yield (DY); however, incorporating machine learning algorithms (MLAs) into CE reading is difficult as large amounts of image annotations are required for training. Current databases lack graphic annotations of pathologies and cannot be used. A novel database, KID, aims to provide a reference for research and development of medical decision support systems (MDSS) for CE...
June 2017: Endoscopy International Open
https://www.readbyqxmd.com/read/28579533/de-identification-of-clinical-notes-via-recurrent-neural-network-and-conditional-random-field
#11
Zengjian Liu, Buzhou Tang, Xiaolong Wang, Qingcai Chen
De-identification, identifying information from data, such as protected health information (PHI) present in clinical data, is a critical step to enable data to be shared or published. The 2016 Centers of Excellence in Genomic Science (CEGS) Neuropsychiatric Genome-scale and RDOC Individualized Domains (N-GRID) clinical natural language processing (NLP) challenge contains a de-identification track in de-identifying electronic medical records (EMRs) (i.e., track 1). The challenge organizers provide 1000 annotated mental health records for this track, 600 out of which are used as a training set and 400 as a test set...
June 1, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28576748/automatic-recognition-of-symptom-severity-from-psychiatric-evaluation-records
#12
Travis R Goodwin, Ramon Maldonado, Sanda M Harabagiu
This paper presents a novel method for automatically recognizing symptom severity by using natural language processing of psychiatric evaluation records to extract features that are processed by machine learning techniques to assign a severity score to each record evaluated in the 2016 RDoC for Psychiatry Challenge from CEGS/N-GRID. The natural language processing techniques focused on (a) discerning the discourse information expressed in questions and answers; (b) identifying medical concepts that relate to mental disorders; and (c) accounting for the role of negation...
May 30, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28557381/predicting-frequent-emergency-department-visits-among-children-with-asthma-using-ehr-data
#13
Lala T Das, Erika L Abramson, Anne E Stone, Janienne E Kondrich, Lisa M Kern, Zachary M Grinspan
OBJECTIVE: For children with asthma, emergency department (ED) visits are common, expensive, and often avoidable. Though several factors are associated with ED use (demographics, comorbidities, insurance, medications), its predictability using electronic health record (EHR) data is understudied. METHODS: We used a retrospective cohort study design and EHR data from one center to examine the relationship of patient factors in 1 year (2013) and the likelihood of frequent ED use (≥2 visits) in the following year (2014), using bivariate and multivariable statistics...
May 30, 2017: Pediatric Pulmonology
https://www.readbyqxmd.com/read/28542318/predicting-congenital-heart-defects-a-comparison-of-three-data-mining-methods
#14
Yanhong Luo, Zhi Li, Husheng Guo, Hongyan Cao, Chunying Song, Xingping Guo, Yanbo Zhang
Congenital heart defects (CHD) is one of the most common birth defects in China. Many studies have examined risk factors for CHD, but their predictive abilities have not been evaluated. In particular, few studies have attempted to predict risks of CHD from, necessarily unbalanced, population-based cross-sectional data. Therefore, we developed and validated machine learning models for predicting, before and during pregnancy, women's risks of bearing children with CHD. We compared the results of these models in a large-scale, comprehensive population-based retrospective cross-sectional epidemiological survey of birth defects in six counties in Shanxi Province, China, covering 2006 to 2008...
2017: PloS One
https://www.readbyqxmd.com/read/28539115/quad-phased-data-mining-modeling-for-dementia-diagnosis
#15
Sunjoo Bang, Sangjoon Son, Hyunwoong Roh, Jihye Lee, Sungyun Bae, Kyungwon Lee, Changhyung Hong, Hyunjung Shin
BACKGROUND: The number of people with dementia is increasing along with people's ageing trend worldwide. Therefore, there are various researches to improve a dementia diagnosis process in the field of computer-aided diagnosis (CAD) technology. The most significant issue is that the evaluation processes by physician which is based on medical information for patients and questionnaire from their guardians are time consuming, subjective and prone to error. This problem can be solved by an overall data mining modeling, which subsidizes an intuitive decision of clinicians...
May 18, 2017: BMC Medical Informatics and Decision Making
https://www.readbyqxmd.com/read/28539112/cascade-recurring-deep-networks-for-audible-range-prediction
#16
Yonghyun Nam, Oak-Sung Choo, Yu-Ri Lee, Yun-Hoon Choung, Hyunjung Shin
BACKGROUND: Hearing Aids amplify sounds at certain frequencies to help patients, who have hearing loss, to improve the quality of life. Variables affecting hearing improvement include the characteristics of the patients' hearing loss, the characteristics of the hearing aids, and the characteristics of the frequencies. Although the two former characteristics have been studied, there are only limited studies predicting hearing gain, after wearing Hearing Aids, with utilizing all three characteristics...
May 18, 2017: BMC Medical Informatics and Decision Making
https://www.readbyqxmd.com/read/28538622/towards-precision-medicine-accurate-predictive-modeling-of-infectious-complications-in-combat-casualties
#17
Christopher J Dente, Matthew Bradley, Seth Schobel, Beverly Gaucher, Timothy Buchman, Allan D Kirk, Eric Elster
BACKGROUND: The biomarker profile of trauma patients may allow for the creation of models to assist bedside decision making & prediction of complications. We sought to determine the utility of modeling in the prediction of bacteremia & pneumonia in combat casualties. METHODS: This is a prospective, observational trial of patients with complex wounds treated at Walter Reed National Military Medical Center (2007-2012). Tissue, serum and wound effluent samples were collected during operative interventions until wound closure...
May 22, 2017: Journal of Trauma and Acute Care Surgery
https://www.readbyqxmd.com/read/28534802/organ-location-determination-and-contour-sparse-representation-for-multi-organ-segmentation
#18
Siqi Li, Huiyan Jiang, Yu-Dong Yao, Benqiang Yang
Organ segmentation on computed tomography (CT) images is of great importance in medical diagnoses and treatment. This paper proposes organ location determination and contour sparse representation methods (OLD-CSR) for multiorgan segmentation (liver, kidney, and spleen) on abdomen CT images using an extreme learning machine (ELM) classifier. First, a location determination method is designed to obtain location information of each organ, which is used for coarse segmentation. Second, for coarse-to-fine segmentation, a contour gradient and rate change based feature point extraction method is proposed...
May 17, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28531339/limtox-a-web-tool-for-applied-text-mining-of-adverse-event-and-toxicity-associations-of-compounds-drugs-and-genes
#19
Andres Cañada, Salvador Capella-Gutierrez, Obdulia Rabal, Julen Oyarzabal, Alfonso Valencia, Martin Krallinger
A considerable effort has been devoted to retrieve systematically information for genes and proteins as well as relationships between them. Despite the importance of chemical compounds and drugs as a central bio-entity in pharmacological and biological research, only a limited number of freely available chemical text-mining/search engine technologies are currently accessible. Here we present LimTox (Literature Mining for Toxicology), a web-based online biomedical search tool with special focus on adverse hepatobiliary reactions...
May 22, 2017: Nucleic Acids Research
https://www.readbyqxmd.com/read/28524769/machine-learning-for-epigenetics-and-future-medical-applications
#20
Lawrence B Holder, M Muksitul Haque, Michael K Skinner
Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and novel ML approaches to predict genome-wide locations of critical epimutations. A combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used to address past problems with ML to develop a more efficient feature selection process and address the imbalance problem in all genomic data sets...
May 19, 2017: Epigenetics: Official Journal of the DNA Methylation Society
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