Read by QxMD icon Read

machine learning medicine

Ryan Eshleman, Rahul Singh
BACKGROUND: Adverse drug events (ADEs) constitute one of the leading causes of post-therapeutic death and their identification constitutes an important challenge of modern precision medicine. Unfortunately, the onset and effects of ADEs are often underreported complicating timely intervention. At over 500 million posts per day, Twitter is a commonly used social media platform. The ubiquity of day-to-day personal information exchange on Twitter makes it a promising target for data mining for ADE identification and intervention...
October 6, 2016: BMC Bioinformatics
Alistair E W Johnson, Mohammad M Ghassemi, Shamim Nemati, Katherine E Niehaus, David A Clifton, Gari D Clifford
Clinical data management systems typically provide caregiver teams with useful information, derived from large, sometimes highly heterogeneous, data sources that are often changing dynamically. Over the last decade there has been a significant surge in interest in using these data sources, from simply re-using the standard clinical databases for event prediction or decision support, to including dynamic and patient-specific information into clinical monitoring and prediction problems. However, in most cases, commercial clinical databases have been designed to document clinical activity for reporting, liability and billing reasons, rather than for developing new algorithms...
February 2016: Proceedings of the IEEE
Byron C Wallace, Joël Kuiper, Aakash Sharma, Mingxi Brian Zhu, Iain J Marshall
Systematic reviews underpin Evidence Based Medicine (EBM) by addressing precise clinical questions via comprehensive synthesis of all relevant published evidence. Authors of systematic reviews typically define a Population/Problem, Intervention, Comparator, and Outcome (a PICO criteria) of interest, and then retrieve, appraise and synthesize results from all reports of clinical trials that meet these criteria. Identifying PICO elements in the full-texts of trial reports is thus a critical yet time-consuming step in the systematic review process...
2016: Journal of Machine Learning Research: JMLR
Frank Preiswerk, Matthew Toews, Cheng-Chieh Cheng, Jr-Yuan George Chiou, Chang-Sheng Mei, Lena F Schaefer, W Scott Hoge, Benjamin M Schwartz, Lawrence P Panych, Bruno Madore
PURPOSE: To combine MRI, ultrasound, and computer science methodologies toward generating MRI contrast at the high frame rates of ultrasound, inside and even outside the MRI bore. METHODS: A small transducer, held onto the abdomen with an adhesive bandage, collected ultrasound signals during MRI. Based on these ultrasound signals and their correlations with MRI, a machine-learning algorithm created synthetic MR images at frame rates up to 100 per second. In one particular implementation, volunteers were taken out of the MRI bore with the ultrasound sensor still in place, and MR images were generated on the basis of ultrasound signal and learned correlations alone in a "scannerless" manner...
October 13, 2016: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
Nicholas R Waytowich, Vernon J Lawhern, Addison W Bohannon, Kenneth R Ball, Brent J Lance
Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry, STIG), which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects...
2016: Frontiers in Neuroscience
Peter V Coveney, Edward R Dougherty, Roger R Highfield
The current interest in big data, machine learning and data analytics has generated the widespread impression that such methods are capable of solving most problems without the need for conventional scientific methods of inquiry. Interest in these methods is intensifying, accelerated by the ease with which digitized data can be acquired in virtually all fields of endeavour, from science, healthcare and cybersecurity to economics, social sciences and the humanities. In multiscale modelling, machine learning appears to provide a shortcut to reveal correlations of arbitrary complexity between processes at the atomic, molecular, meso- and macroscales...
November 13, 2016: Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
Ziad Obermeyer, Ezekiel J Emanuel
By now, it’s almost old news: big data will transform medicine. It’s essential to remember, however, that data by themselves are useless. To be useful, data must be analyzed, interpreted, and acted on. Thus, it is algorithms — not data sets — that will prove transformative. We believe, therefore,..
September 29, 2016: New England Journal of Medicine
T-D Ngo, T-D Tran, M-T Le, K-M Thai
The efflux pumps P-glycoprotein (P-gp) in humans and NorA in Staphylococcus aureus are of great interest for medicinal chemists because of their important roles in multidrug resistance (MDR). The high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of these transmembrane proteins lead us to combining ligand-based approaches, which in the case of this study were machine learning, perceptual mapping and pharmacophore modelling. For P-gp inhibitory activity, individual models were developed using different machine learning algorithms and subsequently combined into an ensemble model which showed a good discrimination between inhibitors and noninhibitors (acctrain-diverse = 84%; accinternal-test = 92% and accexternal-test = 100%)...
September 2016: SAR and QSAR in Environmental Research
Daniela M Borgmann, Sandra Mayr, Helene Polin, Susanne Schaller, Viktoria Dorfer, Lisa Obritzberger, Tanja Endmayr, Christian Gabriel, Stephan M Winkler, Jaroslaw Jacak
In transfusion medicine, the identification of the Rhesus D type is important to prevent anti-D immunisation in Rhesus D negative recipients. In particular, the detection of the very low expressed DEL phenotype is crucial and hence constitutes the bottleneck of standard immunohaematology. The current method of choice, adsorption-elution, does not provide unambiguous results. We have developed a complementary method of high sensitivity that allows reliable identification of D antigen expression. Here, we present a workflow composed of high-resolution fluorescence microscopy, image processing, and machine learning that - for the first time - enables the identification of even small amounts of D antigen on the cellular level...
2016: Scientific Reports
Adrien Ugon, Karima Sedki, Amina Kotti, Brigitte Seroussi, Carole Philippe, Jean-Gabriel Ganascia, Patrick Garda, Jacques Bouaud, Andrea Pinna
Scoring sleep stages can be considered as a classification problem. Once the whole recording segmented into 30-seconds epochs, features, extracted from raw signals, are typically injected into machine learning algorithms in order to build a model able to assign a sleep stage, trying to mimic what experts have done on the training set. Such approaches ignore the advances in sleep medicine, in which guidelines have been published by the AASM, providing definitions and rules that should be followed to score sleep stages...
2016: Studies in Health Technology and Informatics
Peter Ulz, Gerhard G Thallinger, Martina Auer, Ricarda Graf, Karl Kashofer, Stephan W Jahn, Luca Abete, Gunda Pristauz, Edgar Petru, Jochen B Geigl, Ellen Heitzer, Michael R Speicher
The analysis of cell-free DNA (cfDNA) in plasma represents a rapidly advancing field in medicine. cfDNA consists predominantly of nucleosome-protected DNA shed into the bloodstream by cells undergoing apoptosis. We performed whole-genome sequencing of plasma DNA and identified two discrete regions at transcription start sites (TSSs) where nucleosome occupancy results in different read depth coverage patterns for expressed and silent genes. By employing machine learning for gene classification, we found that the plasma DNA read depth patterns from healthy donors reflected the expression signature of hematopoietic cells...
October 2016: Nature Genetics
Matt Schwartzi, Martin Parkl, John H Phanl, May D Wang
Kidney cancer is of prominent concern in modern medicine. Predicting patient survival is critical to patient awareness and developing a proper treatment regimens. Previous prediction models built upon molecular feature analysis are limited to just gene expression data. In this study we investigate the difference in predicting five year survival between unimodal and multimodal analysis of RNA-seq data from gene, exon, junction, and isoform modalities. Our preliminary findings report higher predictive accuracy-as measured by area under the ROC curve (AUC)-for multimodal learning when compared to unimodal learning with both support vector machine (SVM) and k-nearest neighbor (KNN) methods...
November 2015: Proceedings
Patrizia Ferroni, Fabio Massimo Zanzotto, Noemi Scarpato, Silvia Riondino, Umberto Nanni, Mario Roselli, Fiorella Guadagni
OBJECTIVE: To design a precision medicine approach aimed at exploiting significant patterns in data, in order to produce venous thromboembolism (VTE) risk predictors for cancer outpatients that might be of advantage over the currently recommended model (Khorana score). DESIGN: Multiple kernel learning (MKL) based on support vector machines and random optimization (RO) models were used to produce VTE risk predictors (referred to as machine learning [ML]-RO) yielding the best classification performance over a training (3-fold cross-validation) and testing set...
August 4, 2016: Medical Decision Making: An International Journal of the Society for Medical Decision Making
Ilya Lipkovich, Alex Dmitrienko, Ralph B
It is well known that both the direction and magnitude of the treatment effect in clinical trials are often affected by baseline patient characteristics (generally referred to as biomarkers). Characterization of treatment effect heterogeneity plays a central role in the field of personalized medicine and facilitates the development of tailored therapies. This tutorial focuses on a general class of problems arising in data-driven subgroup analysis, namely, identification of biomarkers with strong predictive properties and patient subgroups with desirable characteristics such as improved benefit and/or safety...
August 3, 2016: Statistics in Medicine
Lingxi Peng, Wenbin Chen, Wubai Zhou, Fufang Li, Jin Yang, Jiandong Zhang
Breast cancer is the most frequently and world widely diagnosed life-threatening cancer, which is the leading cause of cancer death among women. Early accurate diagnosis can be a big plus in treating breast cancer. Researchers have approached this problem using various data mining and machine learning techniques such as support vector machine, artificial neural network, etc. The computer immunology is also an intelligent method inspired by biological immune system, which has been successfully applied in pattern recognition, combination optimization, machine learning, etc...
October 2016: Computer Methods and Programs in Biomedicine
Gisbert Schneider
No abstract text is available yet for this article.
September 2011: Molecular Informatics
Fabian Heinemann, Torsten Huber, Christian Meisel, Markus Bundschus, Ulf Leser
The development of cancer drugs is time-consuming and expensive. In particular, failures in late-stage clinical trials are a major cost driver for pharmaceutical companies. This puts a high demand on methods that provide insights into the success chances of new potential medicines. In this study, we systematically analyze publication patterns emerging along the drug discovery process of targeted cancer therapies, starting from basic research to drug approval - or failure. We find clear differences in the patterns of approved drugs compared with those that failed in Phase II/III...
July 18, 2016: Drug Discovery Today
Susan Gruber
The increasing availability of Big Data in healthcare encourages investigators to seek answers to big questions. However, nonparametric approaches to analyzing these data can suffer from the curse of dimensionality, and traditional parametric modeling does not necessarily scale. Targeted learning (TL) combines semiparametric methodology with advanced machine learning techniques to provide a sound foundation for extracting information from data. Predictive models, variable importance measures, and treatment benefits and risks can all be addressed within this framework...
December 2015: Big Data
N Baj, P Dubbins, J A Evans
The ultrasound techniques in pregnancy e-learning project is an online resource commissioned and supported by the Education Committee of the World Federation for Ultrasound in Medicine and Biology (WFUMB). This currently consists of 10 e-learning sessions aimed at midwives and other health workers in developing countries where WFUMB has Educational Centres of Excellence, and in particular at those based mainly in rural communities at considerable distance from urban training centres. The project covers all of the basics of obstetric ultrasound such as fetal and maternal anatomy, ultrasound techniques, assessment in both early and late pregnancy, prediction of pregnancy complications and identification of common abnormalities that might interfere with delivery...
February 2015: Ultrasound: Journal of the British Medical Ultrasound Society
Trieu-Du Ngo, Thanh-Dao Tran, Minh-Tri Le, Khac-Minh Thai
The human P-glycoprotein (P-gp) efflux pump is of great interest for medicinal chemists because of its important role in multidrug resistance (MDR). Because of the high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of this transmembrane protein, ligand-based, and structure-based approaches which were machine learning, homology modeling, and molecular docking were combined for this study. In ligand-based approach, individual two-dimensional quantitative structure-activity relationship models were developed using different machine learning algorithms and subsequently combined into the Ensemble model which showed good performance on both the diverse training set and the validation sets...
July 18, 2016: Molecular Diversity
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"