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1,943 results for “machine learning”
Comparison of Six Different Machine Learning Methods With Traditional Model for Low Anterior Resection Syndrome After Minimally Invasive Surgery for Rectal Cancer -- Development and External Validatio
ClinicalTrials.gov study NCT07267767. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Predicting Premature Treatment Termination in Inpatient Psychotherapy: A Machine Learning Approach
ClinicalTrials.gov study NCT06042595. IPD Sharing: NO. Countries: 0. Publications: 0.
Machine Learning From Fetal Flow Waveforms to Predict Adverse Perinatal Outcomes
ClinicalTrials.gov study NCT03398551. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Personalized Medication Software for BCL-2 Inhibitor in AML Patients Using Machine Learning and Genomics
ClinicalTrials.gov study NCT06295029. IPD Sharing: Not stated. Countries: 0. Publications: 0.
OCT-based Machine Learning FFR for Predicting Post-PCI FFR
ClinicalTrials.gov study NCT06341361. IPD Sharing: NO. Countries: 0. Publications: 0.
Machine Learning to Reduce Hypertension Treatment Clinical Inertia
ClinicalTrials.gov study NCT05406336. IPD Sharing: NO. Countries: 0. Publications: 0.
Machine Learning and 3D Image-based Modeling for Body Weight Estimation.
ClinicalTrials.gov study NCT06281938. IPD Sharing: YES. Countries: 0. Publications: 0.
Application of Machine Learning Algorithms to Identify Optimal Candidates for Primary Tumor Resection in Patients with Metastatic Non-small Cell Neuroendocrine Tumors
ClinicalTrials.gov study NCT06621147. IPD Sharing: Not stated. Countries: 0. Publications: 0.
In This Study, the Sponsor Would Like to Collaborate with Institution and Investigator to Aggregate Participants Data and to Pilot Its Software Algorithm Using Machine Learning and Threshold Based Met
ClinicalTrials.gov study NCT06798688. IPD Sharing: NO. Countries: 0. Publications: 0.
Study on the Performance of a Machine Learning Algorithm Recognizing and Triaging Large Vessel Occlusions Using Non-contrast CT Scans
ClinicalTrials.gov study NCT06216457. IPD Sharing: NO. Countries: 0. Publications: 0.
A Machine Learning Approach to Continuous Vital Sign Data Analysis
ClinicalTrials.gov study NCT01448161. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Machine Learning Ventilator Decision System VS. Standard Controlled Ventilation
ClinicalTrials.gov study NCT05132751. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
A Machine Learning Algorithm to Predict Health Clinical Situations in Primary Healthcare for Frail Older Adults.
ClinicalTrials.gov study NCT06013709. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Easy-Prime: a machine learning–based prime editor design tool
GEO Series GSE175955. Homo sapiens. 34 samples. Type: Other.
Signatures of GVHD and Relapse after Post-Transplant Cyclophosphamide Revealed by Immune Profiling and Machine Learning
GEO Series GSE182679. Homo sapiens. 101 samples. Type: Expression profiling by high throughput sequencing.
Circulating miRNAs and Machine Learning for Lateralizing Primary Aldosteronism
GEO Series GSE264578. Homo sapiens. 36 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Sparse Machine Learning Methods for Understanding Large Text Corpora
Sparse machine learning has recently emerged as powerful tool to obtain models of high-dimensional data with high degree of interpretability, at low computational cost. This paper posits that these methods can be extremely useful for understanding large collections of text documents, without requiring user expertise in machine learning. Our approach relies on three main ingredients: (a) multi-document text summarization and (b) comparative summarization of two corpora, both using parse regression or classifi cation; (c) sparse principal components and sparse graphical models for unsupervised analysis and visualization of large text corpora. We validate our approach using a corpus of Aviation Safety Reporting System (ASRS) reports and demonstrate that the methods can reveal causal and contributing factors in runway incursions. Furthermore, we show that the methods automatically discover four main tasks that pilots perform during flight, which can aid in further understanding the causal and contributing factors to runway incursions and other drivers for aviation safety incidents. Citation: L. El Ghaoui, G. C. Li, V. Duong, V. Pham, A. N. Srivastava, and K. Bhaduri, “Sparse Machine Learning Methods for Understanding Large Text Corpora,” Proceedings of the Conference on Intelligent Data Understanding, 2011.
Estimation and Bias Correction of Aerosol Abundance using Data-driven Machine Learning and Remote Sensing
Abstract—Air quality information is increasingly becoming a public health concern, since some of the aerosol particles pose harmful effects to peoples health. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. The comparison between the AOD measured from the ground-based Aerosol Robotic Network (AERONET) system and the satellite MODIS instruments at 550 nm shows that there is a bias between the two data products. We performed a comprehensive analysis exploring possible factors which may be contributing to the inter-instrumental bias between MODIS and AERONET. The analysis used several measured variables, including the MODIS AOD, as input in order to train a neural network in regression mode to predict the AERONET AOD values. This not only allowed us to obtain an estimate, but also allowed us to infer the optimal sets of variables that played an important role in the prediction. In addition, we applied machine learning to infer the global abundance of ground level PM2.5 from the AOD data and other ancillary satellite and meteorology products. This research is part of our goal to provide air quality information, which can also be useful for global epidemiology studies.
Machine Learning for Earth Observation Flight Planning Optimization
This paper is a progress report of an effort whose goal is to demonstrate the effectiveness of automated data mining and planning for the daily management of Earth Science missions. Currently, data mining and machine learning technologies are being used by scientists at research labs for validating Earth science models. However, few if any of these advancedtechniques are currently being integrated into daily mission operations. Consequently, there are significant gaps in the knowledge that can be derived from the models and data that are used each day for guiding mission activities. The result can be sub-optimal observation plans, lack of useful data, and wasteful use of resources. Recent advances in data mining, machine learning, and planning make it feasible to migrate these technologies into the daily mission planning cycle. This paper describes the design of a closed loop system for data acquisition, processing, and flight planning that integrates the results of machine learning into the flight planning process.
Multi-omics and machine learning reveal context-specific gene regulatory activities of PML-RARA in Acute Promyelocytic Leukemia [RNA-seq]
GEO Series GSE173754. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.