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medical machine learning

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https://www.readbyqxmd.com/read/28542318/predicting-congenital-heart-defects-a-comparison-of-three-data-mining-methods
#1
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
#2
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
#3
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
#4
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
#5
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
#6
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
#7
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
https://www.readbyqxmd.com/read/28516233/medical-image-data-and-datasets-in-the-era-of-machine-learning-whitepaper-from-the-2016-c-mimi-meeting-dataset-session
#8
Marc D Kohli, Ronald M Summers, J Raymond Geis
At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities...
May 17, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28512863/defining-a-multimodal-signature-of-remote-sports-concussions
#9
Sébastien Tremblay, Yasser Iturria-Medina, José María Mateos-Pérez, Alan C Evans, Louis De Beaumont
Sports-related concussions lead to persistent anomalies of the brain structure and function that interact with the effects of normal ageing. Although post-mortem investigations have proposed a bio-signature of remote concussions, there is still no clear in vivo signature. In the current study, we characterized white matter integrity in retired athletes with a history of remote concussions by conducting a full-brain, diffusion-based connectivity analysis. Next, we combined MRI diffusion markers with MR spectroscopic, MRI volumetric, neurobehavioral and genetic markers to identify a multidimensional in vivo signature of remote concussions...
May 16, 2017: European Journal of Neuroscience
https://www.readbyqxmd.com/read/28503676/learning-optimal-individualized-treatment-rules-from-electronic-health-record-data
#10
Yuanjia Wang, Peng Wu, Ying Liu, Chunhua Weng, Donglin Zeng
Medical research is experiencing a paradigm shift from "one-size-fits-all" strategy to a precision medicine approach where the right therapy, for the right patient, and at the right time, will be prescribed. We propose a statistical method to estimate the optimal individualized treatment rules (ITRs) that are tailored according to subject-specific features using electronic health records (EHR) data. Our approach merges statistical modeling and medical domain knowledge with machine learning algorithms to assist personalized medical decision making using EHR...
October 2016: IEEE International Conference on Healthcare Informatics IEEE International Conference on Healthcare Informatics
https://www.readbyqxmd.com/read/28500765/developing-bayesian-networks-from-a-dependency-layered-ontology-a-proof-of-concept-in-radiation-oncology
#11
Alan M Kalet, Jason N Doctor, John H Gennari, Mark H Phillips
PURPOSE: Bayesian networks (BNs) are graphical representations of probabilistic knowledge that offer normative reasoning under uncertainty and are well suited for use in medical domains. Traditional knowledge-based network development of BN topology requires that modeling experts establish relevant dependency links between domain concepts by searching and translating published literature, querying domain experts, or applying machine learning algorithms on data. For initial development these methods are time-intensive and this cost hinders the growth of BN applications in medical decision making...
May 13, 2017: Medical Physics
https://www.readbyqxmd.com/read/28495341/estimation-of-the-prevalence-of-adverse-drug-reactions-from-social-media
#12
Thin Nguyen, Mark E Larsen, Bridianne O'Dea, Dinh Phung, Svetha Venkatesh, Helen Christensen
This work aims to estimate the degree of adverse drug reactions (ADR) for psychiatric medications from social media, including Twitter, Reddit, and LiveJournal. Advances in lightning-fast cluster computing was employed to process large scale data, consisting of 6.4 terabytes of data containing 3.8 billion records from all the media. Rates of ADR were quantified using the SIDER database of drugs and side-effects, and an estimated ADR rate was based on the prevalence of discussion in the social media corpora...
June 2017: International Journal of Medical Informatics
https://www.readbyqxmd.com/read/28492856/treatment-of-low-bone-density-or-osteoporosis-to-prevent-fractures-in-men-and-women-a-clinical-practice-guideline-update-from-the-american-college-of-physicians
#13
Amir Qaseem, Mary Ann Forciea, Robert M McLean, Thomas D Denberg
Description: This guideline updates the 2008 American College of Physicians (ACP) recommendations on treatment of low bone density and osteoporosis to prevent fractures in men and women. This guideline is endorsed by the American Academy of Family Physicians. Methods: The ACP Clinical Guidelines Committee based these recommendations on a systematic review of randomized controlled trials; systematic reviews; large observational studies (for adverse events); and case reports (for rare events) that were published between 2 January 2005 and 3 June 2011...
May 9, 2017: Annals of Internal Medicine
https://www.readbyqxmd.com/read/28490744/precision-radiology-predicting-longevity-using-feature-engineering-and-deep-learning-methods-in-a-radiomics-framework
#14
Luke Oakden-Rayner, Gustavo Carneiro, Taryn Bessen, Jacinto C Nascimento, Andrew P Bradley, Lyle J Palmer
Precision medicine approaches rely on obtaining precise knowledge of the true state of health of an individual patient, which results from a combination of their genetic risks and environmental exposures. This approach is currently limited by the lack of effective and efficient non-invasive medical tests to define the full range of phenotypic variation associated with individual health. Such knowledge is critical for improved early intervention, for better treatment decisions, and for ameliorating the steadily worsening epidemic of chronic disease...
May 10, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28487745/machine-learning-applications-in-medical-image-analysis
#15
EDITORIAL
Ayman El-Baz, Georgy Gimel'farb, Kenji Suzuki
No abstract text is available yet for this article.
2017: Computational and Mathematical Methods in Medicine
https://www.readbyqxmd.com/read/28487265/effective-information-extraction-framework-for-heterogeneous-clinical-reports-using-online-machine-learning-and-controlled-vocabularies
#16
Shuai Zheng, James J Lu, Nima Ghasemzadeh, Salim S Hayek, Arshed A Quyyumi, Fusheng Wang
BACKGROUND: Extracting structured data from narrated medical reports is challenged by the complexity of heterogeneous structures and vocabularies and often requires significant manual effort. Traditional machine-based approaches lack the capability to take user feedbacks for improving the extraction algorithm in real time. OBJECTIVE: Our goal was to provide a generic information extraction framework that can support diverse clinical reports and enables a dynamic interaction between a human and a machine that produces highly accurate results...
May 9, 2017: JMIR Medical Informatics
https://www.readbyqxmd.com/read/28481395/a-systematic-review-on-machine-learning-in-neurosurgery-the-future-of-decision-making-in-patient-care
#17
Emrah Celtikci
The current practice of neurosurgery depends on clinical practice guidelines and evidence-based research publications that derive results using statistical methods. However, statistical analysis methods have some limitations like; inability to analyze non-linear variables, requiring setting of a level of significance, being impractical for analyzing large amounts of data and possibility of human bias. Machine learning is an emerging method for analyzing massive amounts of complex data which relies on algorithms that allow computers to learn and make accurate predictions...
March 25, 2017: Turkish Neurosurgery
https://www.readbyqxmd.com/read/28457678/research-pearls-the-significance-of-statistics-and-perils-of-pooling-part-2-predictive-modeling
#18
Erik Hohmann, Merrick J Wetzler, Ralph B D'Agostino
The focus of predictive modeling or predictive analytics is to use statistical techniques to predict outcomes and/or the results of an intervention or observation for patients that are conditional on a specific set of measurements taken on the patients prior to the outcomes occurring. Statistical methods to estimate these models include using such techniques as Bayesian methods; data mining methods, such as machine learning; and classical statistical models of regression such as logistic (for binary outcomes), linear (for continuous outcomes), and survival (Cox proportional hazards) for time-to-event outcomes...
April 27, 2017: Arthroscopy: the Journal of Arthroscopic & related Surgery
https://www.readbyqxmd.com/read/28456276/recent-developments-in-machine-learning-for-medical-imaging-applications
#19
EDITORIAL
Kelvin K L Wong, Liansheng Wang, Defeng Wang
No abstract text is available yet for this article.
April 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/28443027/personalized-medication-response-prediction-for-attention-deficit-hyperactivity-disorder-learning-in-the-model-space-vs-learning-in-the-data-space
#20
Hin K Wong, Paul A Tiffin, Michael J Chappell, Thomas E Nichols, Patrick R Welsh, Orla M Doyle, Boryana C Lopez-Kolkovska, Sarah K Inglis, David Coghill, Yuan Shen, Peter Tiño
Attention-Deficit Hyperactive Disorder (ADHD) is one of the most common mental health disorders amongst school-aged children with an estimated prevalence of 5% in the global population (American Psychiatric Association, 2013). Stimulants, particularly methylphenidate (MPH), are the first-line option in the treatment of ADHD (Reeves and Schweitzer, 2004; Dopheide and Pliszka, 2009) and are prescribed to an increasing number of children and adolescents in the US and the UK every year (Safer et al., 1996; McCarthy et al...
2017: Frontiers in Physiology
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