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https://www.readbyqxmd.com/read/28539083/patient-length-of-stay-and-mortality-prediction-a-survey
#1
Aya Awad, Mohamed Bader-El-Den, James McNicholas
Over the past few years, there has been increased interest in data mining and machine learning methods to improve hospital performance, in particular hospitals want to improve their intensive care unit statistics by reducing the number of patients dying inside the intensive care unit. Research has focused on prediction of measurable outcomes, including risk of complications, mortality and length of hospital stay. The length of stay is an important metric both for healthcare providers and patients, influenced by numerous factors...
May 2017: Health Services Management Research
https://www.readbyqxmd.com/read/28538622/towards-precision-medicine-accurate-predictive-modeling-of-infectious-complications-in-combat-casualties
#2
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/28524769/machine-learning-for-epigenetics-and-future-medical-applications
#3
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/28507231/biological-modelling-of-a-computational-spiking-neural-network-with-neuronal-avalanches
#4
Xiumin Li, Qing Chen, Fangzheng Xue
In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed neuronal avalanches. It has been hypothesized that the homeostatic level balanced between stability and plasticity of this critical state may be the optimal state for performing diverse neural computational tasks. However, the critical region for high performance is narrow and sensitive for spiking neural networks (SNNs)...
June 28, 2017: Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
https://www.readbyqxmd.com/read/28503676/learning-optimal-individualized-treatment-rules-from-electronic-health-record-data
#5
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/28503375/network-science-meets-respiratory-medicine-for-osas-phenotyping-and-severity-prediction
#6
Stefan Mihaicuta, Mihai Udrescu, Alexandru Topirceanu, Lucretia Udrescu
Obstructive sleep apnea syndrome (OSAS) is a common clinical condition. The way that OSAS risk factors associate and converge is not a random process. As such, defining OSAS phenotypes fosters personalized patient management and population screening. In this paper, we present a network-based observational, retrospective study on a cohort of 1,371 consecutive OSAS patients and 611 non-OSAS control patients in order to explore the risk factor associations and their correlation with OSAS comorbidities. To this end, we construct the Apnea Patients Network (APN) using patient compatibility relationships according to six objective parameters: age, gender, body mass index (BMI), blood pressure (BP), neck circumference (NC) and the Epworth sleepiness score (ESS)...
2017: PeerJ
https://www.readbyqxmd.com/read/28490744/precision-radiology-predicting-longevity-using-feature-engineering-and-deep-learning-methods-in-a-radiomics-framework
#7
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/28455973/markers-of-arterial-health-could-serve-as-accurate-non-invasive-predictors-of-human-biological-and-chronological-age
#8
Alexander Fedintsev, Daria Kashtanova, Olga Tkacheva, Irina Strazhesko, Anna Kudryavtseva, Ancha Baranova, Alexey Moskalev
The decline in functional capacity is unavoidable consequence of the process of aging. While many anti-aging interventions have been proposed, clinical investigations into anti-aging medicine are limited by lack of reliable techniques for evaluating the rate of ageing. Here we present simple, accurate and cost-efficient techniques for estimation of human biological age, Male and Female Arterial Indices. We started with developing a model which accurately predicts chronological age. Using machine learning, we arrived on a set of four predictors, all of which reflect the functioning of the cardiovascular system...
April 2017: Aging
https://www.readbyqxmd.com/read/28439010/cyclops-reveals-human-transcriptional-rhythms-in-health-and-disease
#9
Ron C Anafi, Lauren J Francey, John B Hogenesch, Junhyong Kim
Circadian rhythms modulate many aspects of physiology. Knowledge of the molecular basis of these rhythms has exploded in the last 20 years. However, most of these data are from model organisms, and translation to clinical practice has been limited. Here, we present an approach to identify molecular rhythms in humans from thousands of unordered expression measurements. Our algorithm, cyclic ordering by periodic structure (CYCLOPS), uses evolutionary conservation and machine learning to identify elliptical structure in high-dimensional data...
April 24, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/28438960/what-learning-machines-will-mean-for-medicine
#10
Lauren Vogel
No abstract text is available yet for this article.
April 24, 2017: CMAJ: Canadian Medical Association Journal, Journal de L'Association Medicale Canadienne
https://www.readbyqxmd.com/read/28421566/precision-global-health-in-the-digital-age
#11
Antoine Flahault, Antoine Geissbuhler, Idris Guessous, Philippe Guérin, Isabelle Bolon, Marcel Salathé, Gérard Escher
Precision global health is an approach similar to precision medicine, which facilitates, through innovation and technology, better targeting of public health interventions on a global scale, for the purpose of maximising their effectiveness and relevance. Illustrative examples include: the use of remote sensing data to fight vector-borne diseases; large databases of genomic sequences of foodborne pathogens helping to identify origins of outbreaks; social networks and internet search engines for tracking communicable diseases; cell phone data in humanitarian actions; drones to deliver healthcare services in remote and secluded areas...
April 19, 2017: Swiss Medical Weekly
https://www.readbyqxmd.com/read/28418441/an-integrated-anti-arrhythmic-target-network-of-a-chinese-medicine-compound-wenxin-keli-revealed-by-combined-machine-learning-and-molecular-pathway-analysis
#12
Taiyi Wang, Ming Lu, Qunqun Du, Xi Yao, Peng Zhang, Xiaonan Chen, Weiwei Xie, Zheng Li, Yuling Ma, Yan Zhu
Wenxin Keli (WK), a Chinese patent medicine, is known to be effective against cardiac arrhythmias and heart failure. Although a number of electrophysiological findings regarding its therapeutic effect have been reported, the active components and system-level characterizations of the component-target interactions of WK have yet to be elucidated. In the current study, we present the first report of a new protective effect of WK on suppressing anti-arrhythmic-agent-induced arrhythmias. In a model of isolated guinea pig hearts, rapid perfusion of quinidine altered the heart rate and prolonged the Q-T interval...
May 2, 2017: Molecular BioSystems
https://www.readbyqxmd.com/read/28365240/using-electronic-health-records-to-build-an-ophthalmological-data-warehouse-and-visualize-patients-data
#13
Karsten U Kortüm, Michael Müller, Christoph Kern, Alexander Babenko, Wolfgang J Mayer, Anselm Kampik, Thomas C Kreutzer, Siegfried Priglinger, Christoph Hirneiss
PURPOSE: To develop a near real-time data warehouse (DW) in an academic ophthalmological center to gain scientific use of increasing digital data from electronic medical records (EMR) and diagnostic devices. Design; Database development METHODS: Specific macular clinic user interfaces within the institutional hospital information system were created. Orders for imaging modalities were sent by an EMR -linked picture-archiving and communications system to the respective devices. All data of 325,767 patients since 2002 were gathered in a DW running on a SQL database...
March 29, 2017: American Journal of Ophthalmology
https://www.readbyqxmd.com/read/28285459/a-critical-review-for-developing-accurate-and-dynamic-predictive-models-using-machine-learning-methods-in-medicine-and-health-care
#14
Hamdan O Alanazi, Abdul Hanan Abdullah, Kashif Naseer Qureshi
Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed...
April 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/28272367/how-open-data-shapes-in-silico-transporter-modeling
#15
REVIEW
Floriane Montanari, Barbara Zdrazil
Chemical compound bioactivity and related data are nowadays easily available from open data sources and the open medicinal chemistry literature for many transmembrane proteins. Computational ligand-based modeling of transporters has therefore experienced a shift from local (quantitative) models to more global, qualitative, predictive models. As the size and heterogeneity of the data set rises, careful data curation becomes even more important. This includes, for example, not only a tailored cutoff setting for the generation of binary classes, but also the proper assessment of the applicability domain...
March 7, 2017: Molecules: a Journal of Synthetic Chemistry and Natural Product Chemistry
https://www.readbyqxmd.com/read/28268820/towards-sophisticated-learning-from-ehrs-increasing-prediction-specificity-and-accuracy-using-clinically-meaningful-risk-criteria
#16
Ieva Vasiljeva, Ognjen Arandjelovic
Computer based analysis of Electronic Health Records (EHRs) has the potential to provide major novel insights of benefit both to specific individuals in the context of personalized medicine, as well as on the level of population-wide health care and policy. The present paper introduces a novel algorithm that uses machine learning for the discovery of longitudinal patterns in the diagnoses of diseases. Two key technical novelties are introduced: one in the form of a novel learning paradigm which enables greater learning specificity, and another in the form of a risk driven identification of confounding diagnoses...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28263937/identifying-drug-drug-interactions-by-data-mining-a-pilot-study-of-warfarin-associated-drug-interactions
#17
Peter Wæde Hansen, Line Clemmensen, Thomas S G Sehested, Emil Loldrup Fosbøl, Christian Torp-Pedersen, Lars Køber, Gunnar H Gislason, Charlotte Andersson
BACKGROUND: Knowledge about drug-drug interactions commonly arises from preclinical trials, from adverse drug reports, or based on knowledge of mechanisms of action. Our aim was to investigate whether drug-drug interactions were discoverable without prior hypotheses using data mining. We focused on warfarin-drug interactions as the prototype. METHODS AND RESULTS: We analyzed altered prothrombin time (measured as international normalized ratio [INR]) after initiation of a novel prescription in previously INR-stable warfarin-treated patients with nonvalvular atrial fibrillation...
November 2016: Circulation. Cardiovascular Quality and Outcomes
https://www.readbyqxmd.com/read/28259405/novel-approaches-to-assess-the-quality-of-fertility-data-stored-in-dairy-herd-management-software
#18
K Hermans, W Waegeman, G Opsomer, B Van Ranst, J De Koster, M Van Eetvelde, M Hostens
Scientific journals and popular press magazines are littered with articles in which the authors use data from dairy herd management software. Almost none of such papers include data cleaning and data quality assessment in their study design despite this being a very critical step during data mining. This paper presents 2 novel data cleaning methods that permit identification of animals with good and bad data quality. The first method is a deterministic or rule-based data cleaning method. Reproduction and mutation or life-changing events such as birth and death were converted to a symbolic (alphabetical letter) representation and split into triplets (3-letter code)...
May 2017: Journal of Dairy Science
https://www.readbyqxmd.com/read/28257413/uncovering-precision-phenotype-biomarker-associations-in-traumatic-brain-injury-using-topological-data-analysis
#19
Jessica L Nielson, Shelly R Cooper, John K Yue, Marco D Sorani, Tomoo Inoue, Esther L Yuh, Pratik Mukherjee, Tanya C Petrossian, Jesse Paquette, Pek Y Lum, Gunnar E Carlsson, Mary J Vassar, Hester F Lingsma, Wayne A Gordon, Alex B Valadka, David O Okonkwo, Geoffrey T Manley, Adam R Ferguson
BACKGROUND: Traumatic brain injury (TBI) is a complex disorder that is traditionally stratified based on clinical signs and symptoms. Recent imaging and molecular biomarker innovations provide unprecedented opportunities for improved TBI precision medicine, incorporating patho-anatomical and molecular mechanisms. Complete integration of these diverse data for TBI diagnosis and patient stratification remains an unmet challenge. METHODS AND FINDINGS: The Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot multicenter study enrolled 586 acute TBI patients and collected diverse common data elements (TBI-CDEs) across the study population, including imaging, genetics, and clinical outcomes...
2017: PloS One
https://www.readbyqxmd.com/read/28256072/perceived-social-support-in-adults-with-autism-spectrum-disorder-and-attention-deficit-hyperactivity-disorder
#20
Sonia Alvarez-Fernandez, Hallie R Brown, Yihong Zhao, Jessica A Raithel, Somer L Bishop, Sarah B Kern, Catherine Lord, Eva Petkova, Adriana Di Martino
Perceived social support (PSS) has been related to physical and mental well-being in typically developing individuals, but systematic characterizations of PSS in autism spectrum disorder (ASD) are limited. We compared self-report ratings of the multidimensional scale of PSS (MSPSS) among age- and IQ-matched groups of adults (18-58 years) with cognitively high-functioning ASD (N = 41), or attention-deficit/hyperactivity disorder (ADHD; N = 69), and neurotypical controls (NC; N = 69). Accompanying group comparisons, we used machine learning random forest (RF) analyses to explore predictors among a range of psychopathological and socio-emotional variables...
May 2017: Autism Research: Official Journal of the International Society for Autism Research
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