keyword
MENU ▼
Read by QxMD icon Read
search

machine learning medicine

keyword
https://www.readbyqxmd.com/read/27902695/text-mining-genotype-phenotype-relationships-from-biomedical-literature-for-database-curation-and-precision-medicine
#1
Ayush Singhal, Michael Simmons, Zhiyong Lu
The practice of precision medicine will ultimately require databases of genes and mutations for healthcare providers to reference in order to understand the clinical implications of each patient's genetic makeup. Although the highest quality databases require manual curation, text mining tools can facilitate the curation process, increasing accuracy, coverage, and productivity. However, to date there are no available text mining tools that offer high-accuracy performance for extracting such triplets from biomedical literature...
November 2016: PLoS Computational Biology
https://www.readbyqxmd.com/read/27901055/mediboost-a-patient-stratification-tool-for-interpretable-decision-making-in-the-era-of-precision-medicine
#2
Gilmer Valdes, José Marcio Luna, Eric Eaton, Charles B Simone, Lyle H Ungar, Timothy D Solberg
Machine learning algorithms that are both interpretable and accurate are essential in applications such as medicine where errors can have a dire consequence. Unfortunately, there is currently a tradeoff between accuracy and interpretability among state-of-the-art methods. Decision trees are interpretable and are therefore used extensively throughout medicine for stratifying patients. Current decision tree algorithms, however, are consistently outperformed in accuracy by other, less-interpretable machine learning models, such as ensemble methods...
November 30, 2016: Scientific Reports
https://www.readbyqxmd.com/read/27896973/patterns-in-biomedical-data-how-do-we-find-them
#3
Anna O Basile, Anurag Verma, Marta Byrska-Bishop, Sarah A Pendergrass, Christian Darabos, H Lester Kirchner
Given the exponential growth of biomedical data, researchers are faced with numerous challenges in extracting and interpreting information from these large, high-dimensional, incomplete, and often noisy data. To facilitate addressing this growing concern, the "Patterns in Biomedical Data-How do we find them?" session of the 2017 Pacific Symposium on Biocomputing (PSB) is devoted to exploring pattern recognition using data-driven approaches for biomedical and precision medicine applications. The papers selected for this session focus on novel machine learning techniques as well as applications of established methods to heterogeneous data...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27896759/identification-and-clinical-translation-of-biomarker-signatures-statistical-considerations
#4
Emanuel Schwarz
Powerful machine learning tools exist to extract biological patterns for diagnosis or prediction from high-dimensional datasets. Simultaneous advances in high-throughput profiling technologies have led to a rapid acceleration of biomarker discovery investigations across all areas of medicine. However, the translation of biomarker signatures into clinically useful tools has thus far been difficult. In this chapter, several important considerations are discussed that influence such translation in the context of classifier design...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/27873411/training-physicians-for-the-real-world-of-medicine-administration-based-learning
#5
Jason Rosenstock, Garrett M Sparks
Tired of outdated teaching formats like case-based learning (CBL), problem-based learning (PBL) and team-based learning (TBL)? We wanted something fresh for our medical school, something that would prepare our graduates for the modern practice of medicine, something that would satisfy regulatory agencies and our deans. After doing an extensive needs assessment, which we ignored, we decided to replace basic science in our curriculum with something more practical: administration-based learning (ABL). We taught students how to fix fax machines, how to deal with angry team members, and how to maximise revenue in private practice - lessons that were well received and were more consistent with what physicians really need to learn to be effective practitioners...
December 2016: Medical Education
https://www.readbyqxmd.com/read/27870246/materials-informatics-statistical-modeling-in-material-science
#6
REVIEW
Abraham Yosipof, Klimentiy Shimanovich, Hanoch Senderowitz
Material informatics is engaged with the application of informatic principles to materials science in order to assist in the discovery and development of new materials. Central to the field is the application of data mining techniques and in particular machine learning approaches, often referred to as Quantitative Structure Activity Relationship (QSAR) modeling, to derive predictive models for a variety of materials-related "activities". Such models can accelerate the development of new materials with favorable properties and provide insight into the factors governing these properties...
December 2016: Molecular Informatics
https://www.readbyqxmd.com/read/27848006/clinical-fracture-risk-evaluated-by-hierarchical-agglomerative-clustering
#7
C Kruse, P Eiken, P Vestergaard
: Clustering analysis can identify subgroups of patients based on similarities of traits. From data on 10,775 subjects, we document nine patient clusters of different fracture risks. Differences emerged after age 60 and treatment compliance differed by hip and lumbar spine bone mineral density profiles. INTRODUCTION: The purposes of this study were to establish and quantify patient clusters of high, average and low fracture risk using an unsupervised machine learning algorithm...
November 16, 2016: Osteoporosis International
https://www.readbyqxmd.com/read/27826573/a-gentle-introduction-to-artificial-neural-networks
#8
EDITORIAL
Zhongheng Zhang
Artificial neural network (ANN) is a flexible and powerful machine learning technique. However, it is under utilized in clinical medicine because of its technical challenges. The article introduces some basic ideas behind ANN and shows how to build ANN using R in a step-by-step framework. In topology and function, ANN is in analogue to the human brain. There are input and output signals transmitting from input to output nodes. Input signals are weighted before reaching output nodes according to their respective importance...
October 2016: Annals of Translational Medicine
https://www.readbyqxmd.com/read/27819294/big-genomics-and-clinical-data-analytics-strategies-for-precision-cancer-prognosis
#9
Ghim Siong Ow, Vladimir A Kuznetsov
The field of personalized and precise medicine in the era of big data analytics is growing rapidly. Previously, we proposed our model of patient classification termed Prognostic Signature Vector Matching (PSVM) and identified a 37 variable signature comprising 36 let-7b associated prognostic significant mRNAs and the age risk factor that stratified large high-grade serous ovarian cancer patient cohorts into three survival-significant risk groups. Here, we investigated the predictive performance of PSVM via optimization of the prognostic variable weights, which represent the relative importance of one prognostic variable over the others...
November 7, 2016: Scientific Reports
https://www.readbyqxmd.com/read/27798253/oxytocin-receptor-gene-variations-predict-neural-and-behavioral-response-to-oxytocin-in-autism
#10
Takamitsu Watanabe, Takeshi Otowa, Osamu Abe, Hitoshi Kuwabara, Yuta Aoki, Tatsunobu Natsubori, Hidemasa Takao, Chihiro Kakiuchi, Kenji Kondo, Masashi Ikeda, Nakao Iwata, Kiyoto Kasai, Tsukasa Sasaki, Hidenori Yamasue
Oxytocin appears beneficial for autism spectrum disorder (ASD), and more than 20 single-nucleotide polymorphisms (SNPs) in oxytocin receptor (OXTR) are relevant to ASD. However, neither biological functions of OXTR SNPs in ASD nor critical OXTR SNPs that determine oxytocin's effects on ASD remain unknown. Here, using a machine-learning algorithm that was designed to evaluate collective effects of multiple SNPs and automatically identify most informative SNPs, we examined relationships between 27 representative OXTR SNPs and six types of behavioral/neural response to oxytocin in ASD individuals...
October 19, 2016: Social Cognitive and Affective Neuroscience
https://www.readbyqxmd.com/read/27784037/deconstructing-pretest-risk-enrichment-to-optimize-prediction-of-psychosis-in-individuals-at-clinical-high-risk
#11
Paolo Fusar-Poli, Grazia Rutigliano, Daniel Stahl, André Schmidt, Valentina Ramella-Cravaro, Shetty Hitesh, Philip McGuire
Importance: Pretest risk estimation is routinely used in clinical medicine to inform further diagnostic testing in individuals with suspected diseases. To our knowledge, the overall characteristics and specific determinants of pretest risk of psychosis onset in individuals undergoing clinical high risk (CHR) assessment are unknown. Objectives: To investigate the characteristics and determinants of pretest risk of psychosis onset in individuals undergoing CHR assessment and to develop and externally validate a pretest risk stratification model...
October 26, 2016: JAMA Psychiatry
https://www.readbyqxmd.com/read/27781485/big-data-in-radiation-therapy-challenges-and-opportunities
#12
Tim Lustberg, Johan van Soest, Arthur Jochems, Timo Deist, Yvonka van Wijk, Sean Walsh, Philippe Lambin, Andre Dekker
Data collected and generated by radiation oncology can be classified by the 4Vs of Big Data (Volume, Variety, Velocity, and Veracity) because it is spread across different care providers and not easily shared due to patient privacy protection. The magnitude of the 4Vs is substantial in oncology, especially due to imaging modalities and unclear data definitions. To create useful models ideally all data of all care providers is understood and learned from, however this presents challenges in the guise of poor data quality, patient privacy concerns, geographical spread, interoperability, and the large volume...
October 26, 2016: British Journal of Radiology
https://www.readbyqxmd.com/read/27766937/leveraging-graph-topology-and-semantic-context-for-pharmacovigilance-through-twitter-streams
#13
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
https://www.readbyqxmd.com/read/27765959/machine-learning-and-decision-support-in-critical-care
#14
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
https://www.readbyqxmd.com/read/27746703/extracting-pico-sentences-from-clinical-trial-reports-using-supervised-distant-supervision
#15
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
https://www.readbyqxmd.com/read/27739101/hybrid-mri-ultrasound-acquisitions-and-scannerless-real-time-imaging
#16
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
https://www.readbyqxmd.com/read/27713685/spectral-transfer-learning-using-information-geometry-for-a-user-independent-brain-computer-interface
#17
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
https://www.readbyqxmd.com/read/27698035/big-data-need-big-theory-too
#18
REVIEW
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
https://www.readbyqxmd.com/read/27682033/predicting-the-future-big-data-machine-learning-and-clinical-medicine
#19
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
https://www.readbyqxmd.com/read/27667641/machine-learning-rule-and-pharmacophore-based-classification-on-the-inhibition-of-p-glycoprotein-and-nora
#20
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
keyword
keyword
120558
1
2
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"