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Clinical Trials About "Evolution circuits machine learning" RSS

03:32 EST 27th February 2020 | BioPortfolio

We list hundreds of Clinical Trials about "Evolution circuits machine learning" on BioPortfolio. We draw our references from global clinical trials data listed on ClinicalTrials.gov and refresh our database daily.

More Information about "Evolution circuits machine learning" on BioPortfolio

We have published hundreds of Evolution circuits machine learning news stories on BioPortfolio along with dozens of Evolution circuits machine learning Clinical Trials and PubMed Articles about Evolution circuits machine learning for you to read. In addition to the medical data, news and clinical trials, BioPortfolio also has a large collection of Evolution circuits machine learning Companies in our database. You can also find out about relevant Evolution circuits machine learning Drugs and Medications on this site too.

Showing "Evolution circuits machine learning" Clinical Trials 1–25 of 3,400+

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Comparison of CPAP Machines With Reusable vs Disposable Circuits

The study aims to assess the basic functionality of a newly designed CPAP machine with reusable circuits to existing machines with disposable circuits, for treatment of newborn infants diagnosed with respiratory distress syndrome. The assessment will compare a comprehensive list of physiological parameters over the first 72 hours of treatment, and will also monitor rates of side effects and adverse events. The null hypothesis is that infants treated on the two categories of mac...


Machine Learning From Fetal Flow Waveforms to Predict Adverse Perinatal Outcomes

The aim of this study is to get a proof of concept for using a computational model of fetal haemodynamics, combined with machine learning based on Doppler patterns of the fetal cardiovascular, cerebral and placental flows, to identify those at increased risk of adverse perinatal outcomes such as stillbirth, perinatal mortality and other neonatal morbidities. We will also compare the sensitivity and specificity of UmbiFlow device with the machine learning model in predicting ad...

Machine Learning-Based Risk Profile Classification of Patients Undergoing Elective Heart Valve Surgery

Machine learning methods potentially provide a highly accurate and detailed assessment of expected individual patient risk before elective cardiac surgery. Correct anticipation of this risk allows for improved counseling of patients and avoidance of possible complications. The investigators therefore investigate the benefit of modern machine learning methods in personalized risk prediction in patients undergoing elective heart valve surgery.


Telemedicine Notifications With Machine Learning for Postoperative Care

The ODIN-Report study will be a randomized controlled trial of the effect of providing machine learning risk forecasts to providers caring for patients immediately after surgery on serious complications. The complications studied will be kidney injury, delirium, unplanned or prolonged ventilator use, and 30 day mortality.

Lung Nodule Imaging Biobank for Radiomics and AI Research

This study will collect retrospective CT scan images and clinical data from patients with incidental lung nodules seen in hospitals across London. We will research whether we can use machine learning to predict which patients will develop lung cancer, to improve early diagnosis.

Subpopulation-Specific Sepsis Identification Using Machine Learning

The investigators propose to develop and evaluate a hospital department-specific machine learning based clinical decision support (CDS) system for early sepsis prediction, focused on improving the accuracy of sepsis predictions. Existing sepsis screening tools are often non-specific, resulting in a high false alarm rate which can be detrimental to patient care. The investigators will train a machine learning algorithm to learn the patterns in patient vital signs in eight specif...

System for High-Intensity Evaluation During Radiotherapy

This quality improvement project will evaluate the implementation of a previously described intervention (twice per week on-treatment clinical evaluations) in a feasible fashion using a previously described machine learning algorithm identifying patients identified at high risk for an emergency visit or hospitalization during radiation therapy.

Machine-learning Optimization for Prostate Brachytherapy Planning

The proposed, mono-institutional, randomized-controlled trial aims to determine whether the dosimetric outcomes following prostate Low-Dose-Rate (LDR) brachytherapy, planned using a novel machine learning (ML-LDR) algorithm, are equivalent to manual treatment planning techniques. Forty-two patients with low-to-intermediate-risk prostate cancer will be planned using ML-LDR and expert manual treatment planning over the course of the 12-month study. Expert radiation oncology (RO) ...

Machine Learning for Handheld Vascular Studies

The use of handheld arterial 'stethoscopes' (continuous wave Doppler devices) are ubiquitous in clinical practice. However, most users have received no formal training in their use or the interpretation of the returned data. This leads to delays in diagnosis and errors in diagnosis. The investigators intend to create a novel machine-learning algorithm to assist clinicians in the use of this data. This study will allow the investigators to collect sound files from the use...

Heuristics, Algorithms and Machine Learning: Evaluation & Testing in Radiation Therapy

The Hamlet.rt study is a prospective data collection and patient questionnaire study for patients undergoing image-guided radiotherapy with curative intent. The aim of the study is to use novel machine learning and mathematical techniques to build a model that can predict the risk of significant side effects from radiotherapy treatment for an individual patient: using calculations of normal tissue dose from radiotherapy treatment planning and patient baseline characteristics d...

Machine Learning Assisted Recognition of Out-of-Hospital Cardiac Arrest During Emergency Calls.

Emergency medical Services Copenhagen has developed a machine learning model that analyzes the calls to 1-1-2 (9-1-1) in real time. The model are able to recognize calls where a cardiac arrest is suspected. The aim of the study is to investigate the effect of a computer generated alert in calls where cardiac arrest is suspected. The study will investigate 1. whether a potential increase in recognitions is due to machine alerts or the increased focus of the medica...

Physiological Validation of Current Machine Learning Models for Hemodynamic Instability in Humans

This study will be collecting data on participants undergoing lower body negative pressure (LBNP) to simulate progressive blood loss. The goal of the study is to collect data to allow for development of an algorithm with machine learning to predict blood pressure responses to hyporvolemia by analyzing the arterial waveforms collected during LBNP.

The HEADWIND-Study

To analyse driving behavior of individuals with type 1 diabetes in eu- and progressive hypoglycaemia using a validated research driving simulator. Based on the driving variables provided by the simulator the investigators aim at establishing algorithms capable of discriminating eu- and hypoglycemic driving patterns using machine learning neural networks (deep machine learning classifiers).

Cardiopulmonary Circuits in the Pediatric Population

During open-heart surgery, blood flow is supported by a heart-lung machine that both pumps the blood and gives it oxygen. A problem associated with a heart-lung machine is the damage to some of the blood caused by protein in the blood cell sticking to the sides of the heart-lung machine tubing. This breakdown of the blood cell affects the platelets, which help the blood to clot. Use of the un-treated circuit will be phased out within the next few years, as newer technology is a...

Artificial Intelligence/Machine Learning Modeling on Time to Palliative Care Review in an Inpatient Hospital Population

Investigators are testing whether machine learning prediction models integrated into a health care model will accurately identify participants who may benefit from a comprehensive review by a palliative care specialist, and decrease time to receiving a palliative care consult in an inpatient setting.

Leveraging Machine Learning to Effortlessly Track Patient Movement in the Clinic.

The objective of this study is the development of a system that will allow for the precise measurement of movement kinematics in a clinical exam setting using natural video from three cameras and machine learning to track points of interest. The investigators aim to implement such system in an unobtrusive and simply-incorporated way into the physical exam to provide exact, objective measures to detect patient movement abnormalities in ways not feasible with current tracking tec...

Using the Neuroscience of Fear Extinction for Anxiety Reduction

Social anxiety disorder affects as many as 12% of Americans, resulting in significant distress and disability. Although exposure therapy is one of the best treatments available, as many as 25% of patients do not respond and we do not know why. Extinction learning is thought to be the mechanism of exposure therapy, and the neuroscience of extinction learning has advanced significantly since exposure therapy was developed; however, there has been little application towards improv...

Evaluation of Domestic Hemodialysis Machine: A Multi-center Clinical Study

An important reason for the costs of hemodialysis treatment in China are expensive is the hemodialysis machine and related products mainly rely on imports. Hemodialysis machine is the basis equipment of the hemodialysis treatment. After years of research and development, China has had the domestic hemodialysis machine. However, due to the lack of control studies of domestic and imported hemodialysis machine, thus causing the domestic hemodialysis machine promotion has been hind...

Assessment of Utility of accelerateIQ in the Care of Patients Participating in a Pulmonary Rehabilitation Program

The proposed study seeks to assess the performance of continuous biosensor data and machine learning analytics in assessment of health patient status in a pulmonary rehabilitation program. It is hypothesized that using continuous physiologic biosensor data and machine learning analytics to detect changes in physiology may play a role in managing patients in the pulmonary rehabilitation setting.

Association of Eye With Hepatobiliary Disorders :Qualitative Analysis Via Deep Learning

Artificial Intelligence and Machine Learning techniques may provide insight into exploring the potential covert association behind and reveal some early ocular architecture changes in individuals with hepatobiliary disorders. We conducted a pioneer work to explore the association between the eye and liver via deep learning, to develop and evaluate different deep-learning models to predict the hepatobiliary disease by using ocular images.

Functional MRI of Cognitive Control in Autism

This study aims to investigate the role of fronto-striatal circuits and cognitive control in the perseverative and inflexible behavior that is a defining feature of autism. We hypothesize that deficits in the development of fronto-striatal circuitry may underlie cognitive inflexibility in autism. Specifically, we hypothesize that repetitive, inflexible behavior arises as (1) fronto-striatal systems are capable of learning patterns present in the environment (as in implicit lear...

Optimized Multi-modality Machine Learning Approach During Cardio-toxic Chemotherapy to Predict Arising Heart Failure

The present project will develop an automated machine learning approach using multi-modality data (imaging, laboratory, electrocardiography and questionnaire) to increase the understanding and prediction of arising heart failure in patients scheduled for cardio-toxic chemotherapy. This algorithmus will be developed by the technical cooperation partner at Technion, the institut for biomedical engineering in Haifa, Israel.

Validation of DRAGON Versus a Simplified DRAGON/Machine Learning

The CT-DRAGON score can predict long-term functional outcome after acute stroke treated by thrombolysis. However, implementation in clinical practice is hampered by a lack of validation in the broad spectrum of stroke patients undergoing thrombectomy, whether or not in combination with thrombolysis or conservative treatment. Furthermore, the CT-DRAGON score considers multiple items, which are not always readily available in every setting. This study aims to investigate whether ...

Motor Learning in a Customized Body-Machine Interface

People with tetraplegia often retain some level of mobility of the upper body. The proposed study will test the hypothesis that it is possible to develop personalized interfaces, which utilize the residual mobility to enable paralyzed persons to control computers, wheelchairs and other assistive devices. If successful the project will result into the establishment of a new family of human-machine interfaces based on wearable sensors that adapt their functions to their users' ab...

Personalizing Mediterranean Diet in Children.

Investigating glucose response to Mediterranean and regular diets in healthy children in order to develop specific pediatric machine-learning for predicting the personalized glucose response to food for individual children. The prediction will be based on multiple measurements, including blood tests, personal lifestyle and gut microbiome. This will allow investigators to design personalized Mediterranean machine-learning-based diets which may potentially reduce the burden of d...


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