Assessment of Diagnostic Performance of Dermatologists Cooperating With a Convolutional Neural Network in a Prospective Clinical Study: Human With Machine.
Sign Up to like & getrecommendations! Published in 2023 at "JAMA dermatology"
DOI: 10.1001/jamadermatol.2023.0905
Abstract: Importance Studies suggest that convolutional neural networks (CNNs) perform equally to trained dermatologists in skin lesion classification tasks. Despite the approval of the first neural networks for clinical use, prospective studies demonstrating benefits of human… read more here.
Keywords: human machine; melanocytic lesions; machine; convolutional neural ... See more keywords
Development and Validation of an Electronic Health Record–Based Machine Learning Model to Estimate Delirium Risk in Newly Hospitalized Patients Without Known Cognitive Impairment
Sign Up to like & getrecommendations! Published in 2018 at "JAMA Network Open"
DOI: 10.1001/jamanetworkopen.2018.1018
Abstract: Key Points Question Can machine learning be used to predict incident delirium in newly hospitalized patients using only data available in the electronic health record shortly after admission? Findings In this cohort study, classification models… read more here.
Keywords: machine; machine learning; electronic health; health record ... See more keywords
Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions
Sign Up to like & getrecommendations! Published in 2019 at "JAMA Network Open"
DOI: 10.1001/jamanetworkopen.2019.0968
Abstract: Key Points Question Can machine-learning approaches predict opioid overdose risk among fee-for-service Medicare beneficiaries? Findings In this prognostic study of the administrative claims data of 560 057 Medicare beneficiaries, the deep neural network and gradient boosting… read more here.
Keywords: machine; risk; machine learning; opioid overdose ... See more keywords
Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression
Sign Up to like & getrecommendations! Published in 2020 at "JAMA Network Open"
DOI: 10.1001/jamanetworkopen.2020.6653
Abstract: Key Points Question Can machine learning models predict improvement of various depressive symptoms with antidepressant treatment based on pretreatment symptom scores and electroencephalographic measures? Findings In this prognostic study, using the machine learning approach of… read more here.
Keywords: machine; machine learning; based pretreatment; antidepressant treatment ... See more keywords
Development and Validation of an Explainable Machine Learning Model for Major Complications After Cytoreductive Surgery
Sign Up to like & getrecommendations! Published in 2022 at "JAMA Network Open"
DOI: 10.1001/jamanetworkopen.2022.12930
Abstract: Key Points Question Can machine learning provide superior risk prediction compared with the current statistical methods for patients undergoing cytoreductive surgery? Findings In this prognostic study, an optimized machine learning model demonstrated superior capability of… read more here.
Keywords: learning model; machine; machine learning; risk ... See more keywords
A Prehospital Triage System to Detect Traumatic Intracranial Hemorrhage Using Machine Learning Algorithms
Sign Up to like & getrecommendations! Published in 2022 at "JAMA Network Open"
DOI: 10.1001/jamanetworkopen.2022.16393
Abstract: Key Points Question Can machine learning algorithms be used to triage patients with head trauma according to their severity before transportation? Findings In this cohort study of 2123 patients with head trauma, a machine learning–based… read more here.
Keywords: learning algorithms; triage; machine; traumatic intracranial ... See more keywords
Randomized Clinical Trials of Machine Learning Interventions in Health Care
Sign Up to like & getrecommendations! Published in 2022 at "JAMA Network Open"
DOI: 10.1001/jamanetworkopen.2022.33946
Abstract: Key Points Question How are machine learning interventions being incorporated into randomized clinical trials (RCTs) in health care? Findings In this systematic review of 41 RCTs of machine learning interventions, despite the large number of… read more here.
Keywords: clinical trials; machine; learning interventions; machine learning ... See more keywords
Machine Learning Predicts Laboratory Earthquakes
Sign Up to like & getrecommendations! Published in 2017 at "Geophysical Research Letters"
DOI: 10.1002/2017gl074677
Abstract: We apply machine learning to data sets from shear laboratory experiments, with the goal of identifying hidden signals that precede earthquakes. Here we show that by listening to the acoustic signal emitted by a laboratory… read more here.
Keywords: machine; fault; machine learning; learning predicts ... See more keywords
Predicting machine's performance record using the stacked long short‐term memory (LSTM) neural networks
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Applied Clinical Medical Physics"
DOI: 10.1002/acm2.13558
Abstract: Abstract Purpose The record of daily quality control (QC) items shows machine performance patterns and potentially provides warning messages for preventive actions. This study developed a neural network model that could predict the record and… read more here.
Keywords: machine; record; stacked lstm; machine performance ... See more keywords
Insensitivity of machine log files to MLC leaf backlash and effect of MLC backlash on clinical dynamic MLC motion: An experimental investigation
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Applied Clinical Medical Physics"
DOI: 10.1002/acm2.13660
Abstract: Abstract Purpose Multi‐leaf‐collimator (MLC) leaf position accuracy is important for accurate dynamic radiotherapy treatment plan delivery. Machine log files have become widely utilized for quality assurance (QA) of such dynamic treatments. The primary aim is… read more here.
Keywords: mlc; machine; log files; mlc leaf ... See more keywords
Machine Learning Approaches for Thermoelectric Materials Research
Sign Up to like & getrecommendations! Published in 2019 at "Advanced Functional Materials"
DOI: 10.1002/adfm.201906041
Abstract: Thermoelectric (TE) materials provide a solid‐state solution in waste heat recovery and refrigeration. During the past few decades, considerable effort has been devoted towards improving the performance of TE materials, which requires the optimization of… read more here.
Keywords: thermoelectric materials; machine; machine learning; learning approaches ... See more keywords