Using Machine Learning to Provide Rapid Rainfall Forecasts Based on Radar-Derived Data

2015 ◽  
Author(s):  
Daryll Panaligan ◽  
Justine Arrnon Razon ◽  
Jaime Caro ◽  
Carlos Primo David
Keyword(s):  
Machine Learning ◽  
Derived Data

894. Suicide Prediction Using Machine Learning Techniques in Screening and Clinician-Derived Data

2017 ◽  
Vol 81 (10) ◽  
pp. S361 ◽  
Author(s):  
Laura Hack ◽  
Tanja Jovanovic ◽  
Sierra Carter ◽  
Kerry Ressler ◽  
Alicia Smith
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Suicide Prediction ◽  
Learning Techniques ◽  
Derived Data

scholarly journals Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging

2017 ◽  
Author(s):  
Dmitry Petrov ◽  
Boris A. Gutman ◽  
Shih-Hua (Julie) Yu ◽  
Theo G.M. van Erp ◽  
Jessica A. Turner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Support Vector ◽  
Shape Features ◽  
Shape Models ◽  
Boosted Decision Trees ◽  
Human Quality ◽  
Derived Data ◽  
Deep Brain ◽  
Comparable Size

AbstractAs very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.

scholarly journals Hierarchical confounder discovery in the experiment–machine learning cycle

2021 ◽  
Author(s):  
Alex Rogozhnikov ◽  
Pavan Ramkumar ◽  
Saul Kato ◽  
Sean Escola
Keyword(s):  
Machine Learning ◽  
Experimental Design ◽  
Natural Phenomena ◽  
Biomedical Image ◽  
Biological Dataset ◽  
Confounding Variables ◽  
Derived Data ◽  
High Dimensional Datasets ◽  
Practical Constraints ◽  
Frequent Presence

The promise of using machine learning (ML) to extract scientific insights from high dimensional datasets is tempered by the frequent presence of confounding variables, and it behooves scientists to determine whether or not a model has extracted the desired information or instead may have fallen prey to bias. Due both to features of many natural phenomena and to practical constraints of experimental design, complex bioscience datasets tend to be organized in nested hierarchies which can obfuscate the origin of a confounding effect and obviate traditional methods of confounder amelioration. We propose a simple non-parametric statistical method called the Rank-to-Group (RTG) score that can identify hierarchical confounder effects in raw data and ML-derived data embeddings. We show that RTG scores correctly assign the effects of hierarchical confounders in cases where linear methods such as regression fail. In a large public biomedical image dataset, we discover unreported effects of experimental design. We then use RTG scores to discover cross-modal correlated variability in a complex multi-phenotypic biological dataset. This approach should be of general use in experiment–analysis cycles and to ensure confounder robustness in ML models.

scholarly journals Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2637
Author(s):  
Padraig Davidson ◽  
Peter Düking ◽  
Christoph Zinner ◽  
Billy Sperlich ◽  
Andreas Hotho
Keyword(s):  
Machine Learning ◽  
Pilot Study ◽  
Perceived Exertion ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Rating Of Perceived Exertion ◽  
Sensor Data ◽  
Rate Step ◽  
Derived Data ◽  
Trained Runners

The rating of perceived exertion (RPE) is a subjective load marker and may assist in individualizing training prescription, particularly by adjusting running intensity. Unfortunately, RPE has shortcomings (e.g., underreporting) and cannot be monitored continuously and automatically throughout a training sessions. In this pilot study, we aimed to predict two classes of RPE (≤15 “Somewhat hard to hard” on Borg’s 6–20 scale vs. RPE > 15 in runners by analyzing data recorded by a commercially-available smartwatch with machine learning algorithms. Twelve trained and untrained runners performed long-continuous runs at a constant self-selected pace to volitional exhaustion. Untrained runners reported their RPE each kilometer, whereas trained runners reported every five kilometers. The kinetics of heart rate, step cadence, and running velocity were recorded continuously ( 1 Hz ) with a commercially-available smartwatch (Polar V800). We trained different machine learning algorithms to estimate the two classes of RPE based on the time series sensor data derived from the smartwatch. Predictions were analyzed in different settings: accuracy overall and per runner type; i.e., accuracy for trained and untrained runners independently. We achieved top accuracies of 84.8 % for the whole dataset, 81.8 % for the trained runners, and 86.1 % for the untrained runners. We predict two classes of RPE with high accuracy using machine learning and smartwatch data. This approach might aid in individualizing training prescriptions.

scholarly journals Machine-Learning-Based Prediction of Treatment Outcomes Using MR Imaging-Derived Quantitative Tumor Information in Patients with Sinonasal Squamous Cell Carcinomas: A Preliminary Study

Cancers ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 800 ◽  
Author(s):  
Noriyuki Fujima ◽  
Yukie Shimizu ◽  
Daisuke Yoshida ◽  
Satoshi Kano ◽  
Takatsugu Mizumachi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mr Imaging ◽  
Treatment Outcomes ◽  
Local Control ◽  
Predictive Value ◽  
Learning Algorithm ◽  
Squamous Cell Carcinomas ◽  
Machine Learning Algorithm ◽  
Derived Data ◽  
Mean Sensitivity

The purpose of this study was to determine the predictive power for treatment outcome of a machine-learning algorithm combining magnetic resonance imaging (MRI)-derived data in patients with sinonasal squamous cell carcinomas (SCCs). Thirty-six primary lesions in 36 patients were evaluated. Quantitative morphological parameters and intratumoral characteristics from T2-weighted images, tumor perfusion parameters from arterial spin labeling (ASL) and tumor diffusion parameters of five diffusion models from multi-b-value diffusion-weighted imaging (DWI) were obtained. Machine learning by a non-linear support vector machine (SVM) was used to construct the best diagnostic algorithm for the prediction of local control and failure. The diagnostic accuracy was evaluated using a 9-fold cross-validation scheme, dividing patients into training and validation sets. Classification criteria for the division of local control and failure in nine training sets could be constructed with a mean sensitivity of 0.98, specificity of 0.91, positive predictive value (PPV) of 0.94, negative predictive value (NPV) of 0.97, and accuracy of 0.96. The nine validation data sets showed a mean sensitivity of 1.0, specificity of 0.82, PPV of 0.86, NPV of 1.0, and accuracy of 0.92. In conclusion, a machine-learning algorithm using various MR imaging-derived data can be helpful for the prediction of treatment outcomes in patients with sinonasal SCCs.

scholarly journals CellProfiler Analyst 3.0: Accessible data exploration and machine learning for image analysis

2021 ◽  
Author(s):  
David R Stirling ◽  
Anne E Carpenter ◽  
Beth A Cimini
Keyword(s):  
Machine Learning ◽  
Open Source Software ◽  
Training Data ◽  
Data Set ◽  
Quantitative Image ◽  
Sample Data ◽  
Open Source Software Package ◽  
Neural Network Classifiers ◽  
Derived Data ◽  
Free Open Source

Abstract   Image-based experiments can yield many thousands of individual measurements describing each object of interest, such as cells in microscopy screens. CellProfiler Analyst is a free, open-source software package designed for the exploration of quantitative image-derived data and the training of machine learning classifiers with an intuitive user interface. We have now released CellProfiler Analyst 3.0, which in addition to enhanced performance adds support for neural network classifiers, identifying rare object subsets, and direct transfer of objects of interest from visualisation tools into the Classifier tool for use as training data. This release also increases interoperability with the recently released CellProfiler 4, making it easier for users to detect and measure particular classes of objects in their analyses. Availability CellProfiler Analyst binaries for Windows and MacOS are freely available for download at https://cellprofileranalyst.org/. Source code is implemented in Python 3 and is available at https://github.com/CellProfiler/CellProfiler-Analyst/. A sample data set is available at https://cellprofileranalyst.org/examples, based on images freely available from the Broad Bioimage Benchmark Collection (BBBC).

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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