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2014 2
2015 11
2016 11
2017 28
2018 28
2019 23
2020 0
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A systematic review of data mining and machine learning for air pollution epidemiology.
Bellinger C, et al. BMC Public Health 2017 - Review. PMID 29179711 Free PMC article.
To this end, data mining and machine learning algorithms are increasingly being applied to air pollution epidemiology. METHODS: We conducted a systematic literature review on the application of data mining and machine learning methods in air pollution epidemiology. ...Research articles applying data mining and machine learning methods to air pollution epidemiology were queried and reviewed. ...
To this end, data mining and machine learning algorithms are increasingly being applied to air pollution epidemiology. …
A review of machine learning in obesity.
DeGregory KW, et al. Obes Rev 2018 - Review. PMID 29426065
Here, we review machine learning methods that predict and/or classify such as linear and logistic regression, artificial neural networks, deep learning and decision tree analysis. ...This summary of machine learning algorithms provides a unique overview of the state of data analysis applied specifically to obesity....
Here, we review machine learning methods that predict and/or classify such as linear and logistic regression, artificial neura …
Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards.
Churpek MM, et al. Crit Care Med 2016 - Clinical Trial. PMID 26771782 Free PMC article.
OBJECTIVE: Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. ...CONCLUSIONS: In this multicenter study, we found that several machine learning methods more accurately predicted clinical deterioration than logistic regression. ...
OBJECTIVE: Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. …
A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine learning methods.
Moore JH, et al. Pac Symp Biocomput 2018. PMID 29218887 Free PMC article.
A central challenge of developing and evaluating artificial intelligence and machine learning methods for regression and classification is access to data that illuminates the strengths and weaknesses of different methods. ...Further, we introduce new methods for developing simulation models that generate data that specifically allows discrimination between different machine learning methods....
A central challenge of developing and evaluating artificial intelligence and machine learning methods for regression and class …
Use of Machine Learning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients.
Park E, et al. J Med Internet Res 2017. PMID 28420599 Free PMC article.
A series of machine learning techniques, namely support vector machine (SVM), radial basis function network (RBFN), and random forest (RF), were implemented to discriminate stroke patients from controls with leave-one-out cross-validation. ...Feature selection abstracted the major attributes from the 12 PDT features to elucidate the dominant characteristics of proximal weakness of stroke patients using machine learning classification. ...
A series of machine learning techniques, namely support vector machine (SVM), radial basis function network (RBFN), and …
End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography.
Ardila D, et al. Nat Med 2019. PMID 31110349
We propose a deep learning algorithm that uses a patient's current and prior computed tomography volumes to predict the risk of lung cancer. ...While the vast majority of patients remain unscreened, we show the potential for deep learning models to increase the accuracy, consistency and adoption of lung cancer screening worldwide....
We propose a deep learning algorithm that uses a patient's current and prior computed tomography volumes to predict the risk of lung …
Machine learning and deep analytics for biocomputing: call for better explainability.
Petkovic D, et al. Pac Symp Biocomput 2018. PMID 29218921 Free article.
The goals of this workshop are to discuss challenges in explainability of current Machine Leaning and Deep Analytics (MLDA) used in biocomputing and to start the discussion on ways to improve it. ...We believe that much greater effort is needed to address the issue of MLDA explainability because of: 1) the ever increasing use and dependence on MLDA in biocomputing including the need for increased adoption by non-MLD experts; 2) the diversity, complexity and scale of biocomputing data and MLDA algorithms; 3) the emerging importance of MLDA-based decisions in patient care, in daily research, as well as in the development of new costly medical procedures and drugs. ...
The goals of this workshop are to discuss challenges in explainability of current Machine Leaning and Deep Analytics (MLDA) used in b …
Data-driven advice for applying machine learning to bioinformatics problems.
Olson RS, et al. Pac Symp Biocomput 2018. PMID 29218881 Free PMC article.
As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used machine learning algorithms on a set of 165 publicly available classification problems in order to provide data-driven algorithm recommendations to current researchers. ...The analysis culminates in the recommendation of five algorithms with hyperparameters that maximize classifier performance across the tested problems, as well as general guidelines for applying machine learning to supervised classification problems....
As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. Here we contribute a thorough an …
Deep neural network improves fracture detection by clinicians
Lindsey R, et al. Proc Natl Acad Sci U S A 2018. PMID 30348771 Free PMC article.
We then ran a controlled experiment with emergency medicine clinicians to evaluate their ability to detect fractures in wrist radiographs with and without the assistance of the deep learning model. ...The significant improvements in diagnostic accuracy that we observed in this study show that deep learning methods are a mechanism by which senior medical specialists can deliver their expertise to generalists on the front lines of medicine, thereby providing substantial improvements to patient care....
We then ran a controlled experiment with emergency medicine clinicians to evaluate their ability to detect fractures in wrist radiographs wi …
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