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Invasive Mechanical Ventilation Duration Prediction using Survival Analysis
[article]
2022
medRxiv
pre-print
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Parametric and non-parametric methods were used to determine predictors of ventilation duration for first-day ventilated patients. ...
As part of the protocol for inclusion, a patient must have been connected to an invasive ventilator upon arrival to the ICU. ...
Acknowledgments We would like to thank the LHSC staff for providing the data used in this analysis. ...
doi:10.1101/2022.12.15.22283535
fatcat:rioz5c2uh5aizdv7koa4xvfdtm
Early Prediction of Multiple Organ Dysfunction in the Pediatric Intensive Care Unit
2021
Frontiers in Pediatrics
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MOD criteria. Early MOD prediction models were built using four machine learning methods: random forest, XGBoost, GLMBoost, and Lasso-GLM. ...
patient monitoring could provide more than 22 h of lead time for MOD onset, with ≥0.93 positive predictive value for a high-risk group identified pre-MOD. ...
We used different machine learning methods to build models that continuously output probability of developing MOD on a scale of 0-1, which we refer to as risk score in this study. ...
doi:10.3389/fped.2021.711104
pmid:34485201
pmcid:PMC8415553
fatcat:6ji3jmrynbalxbrjsv6pmo6dti
Intelligent Bio-Inspired Detection of Food Borne Pathogen by DNA Barcodes: The Case of Invasive Fish Species Lagocephalus Sceleratus
[chapter]
2015
Communications in Computer and Information Science
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Learning Machines. ...
The aim is the automated identification and control of the extremely dangerous for human health invasive fish species "Lagocephalus Sceleratus". ...
Numerous methods have been suggested for the creation of ensembles of learning algorithms: Using different subsets of training data with a single learning method. Using different training parameters ...
doi:10.1007/978-3-319-23983-5_9
fatcat:ujwzazp2azat7igsezst7454ke
Automated Breast Cancer Detection Models Based on Transfer Learning
2022
Sensors
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In this study, we introduce a framework focused on the principle of transfer learning. ...
The transfer learning method is being used to distinguish malignant and benign breast cancer by fine-tuning multiple pre-trained models. ...
So that in the second experiment, the MOD-RES model was trained only for 15 epochs using 10% of the training set as a validation set, a batch size of 32, 64, 128, and 265, and a learning rate ranging from ...
doi:10.3390/s22030876
pmid:35161622
pmcid:PMC8838322
fatcat:xbbkuo7fj5aubicigxpfoxc6pa
Breast Cancer Detection and Classification using Artificial Neural Network
2020
International Journal for Research in Applied Science and Engineering Technology
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This program uses image processing techniques and Deep learning which is a class of Machine learning and AI. ...
This model is implemented using python and some useful deep learning libraries like TensorFlow and Keras. This model yields a 95.6 percent sensitivity for cancer. ...
A 95.6 percent sensitivity for cancer is achieved using our proposed method. This method uses one of the most popular and accurate deep learning algorithms in Convolutional Neural Networks. ...
doi:10.22214/ijraset.2020.5043
fatcat:s5dz7brozzd4hjtmxyu6wc37ya
Potentials and Limitations of WorldView-3 Data for the Detection of Invasive Lupinus polyphyllus Lindl. in Semi-Natural Grasslands
2021
Remote Sensing
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Therefore, WorldView-3 multispectral sensor data was utilized to train multiple machine learning algorithms in an automatic machine learning workflow called 'H2O AutoML' to detect L. polyphyllus in a nature ...
Different degree of L. polyphyllus cover was collected on 3 × 3 m2 reference plots, and multispectral bands, indices, and texture features were used in a feature selection process to identify the most ...
We are also grateful to the government of Bavaria for permission to conduct our measurements in a nature conservation area. ...
doi:10.3390/rs13214333
doaj:81ca9fb6ec984a8ca7bc93899bd41e5b
fatcat:lxy5ng66qnas7c4lvfl3ddeevi
Location-only and use-availability data: analysis methods converge
2013
Journal of Animal Ecology
Preserved Fulltext
The Internet Archive has digitized a microfilm copy of this work. It may be possible to borrow a copy for reading.
. & Poff, N.L. (2008) Machine learning methods without tears: a primer for ecologists. The Quarterly Review of Biology, 83, 171-193. ...
Thus, lacking data on the absence of a species, practitioners of both regression and machine learning approaches attempt to resolve this problem through use of pseudo- or background absence for com- pleting ...
doi:10.1111/1365-2656.12145
pmid:24499378
fatcat:s7vaotrvdffipd3r3durjnn6ku
Model-based Feature Augmentation for Cardiac Ablation Target Learning from Images
2018
IEEE Transactions on Biomedical Engineering
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Conclusion: We presented a feature augmentation scheme based on biophysical cardiac electrophysiology modeling to increase the prediction scores of a machine learning framework for the RFA target prediction ...
Significance: The results derived from this study are a proof of concept that the use of model-based feature augmentation strengthens the performance of a purely image driven learning scheme for the prediction ...
The clinical significance of this work stems from the possibility of incorporating modelling and machine learning techniques into a clinical workflow which would require only non-invasive, pre-intervention ...
doi:10.1109/tbme.2018.2818300
pmid:29993400
fatcat:kd4ibmxdfra6re3mvh7d45cnoe
Research Progress of Gliomas in Machine Learning
2021
Cells
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Further, the existing solutions of machine learning methods and their limitations in glioma prediction and diagnostics, such as overfitting and class imbalanced, were critically analyzed. ...
Machine learning methods were applied as possible approaches to speed up the data mining processes. ...
Machine learning methods are similar to the methods that human beings usually use for learning; however it can draw a lot of energy from statistics and probability, fundamentally, it has more powerful ...
doi:10.3390/cells10113169
pmid:34831392
pmcid:PMC8622230
fatcat:xyfhug3yhvchzd6ayrz4u7ovnq
Characterising Alzheimer's Disease with EEG-based Energy Landscape Analysis
[article]
2021
arXiv
pre-print
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Energy landscape analysis is a method that can be used to quantify these dynamics. This work presents the first application of this method to both AD and EEG. ...
Accessible and non-invasive methods of diagnosing and characterising AD are therefore urgently required. Electroencephalography (EEG) fulfils these criteria and is often used when studying AD. ...
For all combinations of sampling frequencies, window sizes, bands and conditions, pMEMs are estimated, and two machine learning models are trained: using values of J parameters of pMEM (Connectivity) and ...
arXiv:2102.09882v2
fatcat:7drqnn2gffelpplfooujfzwmue
DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia from MR Images
2021
IEEE Access
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[17] propose a data-driven method for distinguishing subjects with AD, MCI, and HC by analyzing non-invasive recordings of EEG. ...
Neuroimaging increases diagnosis accuracy for various subtypes of dementia using machine learning. Specific pre-processing steps are needed to implement machine learning algorithms. ...
doi:10.1109/access.2021.3090474
fatcat:o3mnwtjd3neqjonfiwuykvzo2i
The Potential of a CT-Based Machine Learning Radiomics Analysis to Differentiate Brucella and Pyogenic Spondylitis
2023
Journal of Inflammation Research
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This study aimed to explore the potential of CT-based radiomics features combined with machine learning algorithms to differentiate PS from BS. ...
PyRadiomics, a Python package, was utilized to extract ROI features. Several methods were performed to reduce the dimensionality of the extracted features. ...
Early diagnosis of patients suspected of having PS or BS using innovative non-invasive measures can reduce the need for surgery and lower the overall surgical rate. ...
doi:10.2147/jir.s429593
pmid:38034044
pmcid:PMC10683663
fatcat:jewg4yh3gfg6dcwxnf577aa5c4
PHM SURVEY: Implementation of Prognostic Methods for Monitoring Industrial Systems
2022
Energies
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More specifically, this paper establishes a state of the art in prognostic methods used today in the PHM strategy. ...
PHM uses methods, tools and algorithms for monitoring, anomaly detection, cause diagnosis, prognosis of the remaining useful life (RUL) and maintenance optimization. ...
The results show a similarity in terms of performance between the predicted and real data. This technique can be used for the invasive monitoring of temperature. ...
doi:10.3390/en15196909
fatcat:73kxor2hyncdvdu4mptk4pseyq
Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications
2016
Neural Networks
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and functionality, can be visualised and interpreted for new knowledge discovery and for a better understanding of the data and the processes that generated it. eSTDM can be used for early event prediction ...
A framework for building eSTDM called NeuCube along with a design methodology for building eSTDM using it is presented. ...
Related papers, data, and software systems can be found at http:// www.kedri.aut.ac.nz and http://ncs.ethz/projects/ evospike/, where a NeuCube simulator can be downloaded free for use in research and ...
doi:10.1016/j.neunet.2015.09.011
pmid:26576468
fatcat:hytvg4eekjfazd4244z3o6cmdu
ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU
[article]
2019
arXiv
pre-print
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To address this, we propose a deep multi-scale convolutional architecture trained on the Medical Information Mart for Intensive Care III (MIMIC-III) for mortality prediction, and the use of concepts from ...
Nevertheless, a main impediment for the adoption of Deep Learning in healthcare is its reduced interpretability, for in this field it is crucial to gain insight on the why of predictions, to assure that ...
Despite this, several works have reported the successful use of EMRs and PTS to train Machine Learning/Deep Learning based models for diagnosis. ...
arXiv:1901.08201v1
fatcat:cycpptnl4vhbbjmz2rlr5wf2la
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