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Clinical Trials About "Machine learning poised accelerate drug discovery" RSS

20:19 EST 12th December 2018 | BioPortfolio

We list hundreds of Clinical Trials about "Machine learning poised accelerate drug discovery" on BioPortfolio. We draw our references from global clinical trials data listed on ClinicalTrials.gov and refresh our database daily.

More Information about "Machine learning poised accelerate drug discovery" on BioPortfolio

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

Showing "Machine learning poised accelerate drug discovery" Clinical Trials 1–25 of 5,200+

Extremely Relevant

Can we Accelerate Learning in Healthy Subjects?

The purpose of this study is to investigate whether we can accelerate learning and improve associative memory performance in healthy subjects, by (1) applying transcranial Direct Current Stimulation (tDCS) during a verbal paired-associate learning task and by (2) optimizing the learning method with repeated retrieval practice.


Using 'Guided-Discovery-Learning' to Optimize and Maximize Transfer of Surgical Simulation

The study is a randomized experimental study comparing two forms of learning; guided-discovery-learning and traditional instructional learning. Recruiting sixty-four participants, the investigators plan on comparing these two groups through a procedural skill in the form of suturing. In the case of guided-discovery-learning, the group will be allowed a discovery phase before instruction. In contrast, the control group will receive traditional instruction-lead-learning, in which...

Relevant

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.

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...

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...

Investigating Accelerated Learning in Tinnitus Participants Implanted With Vagus Nerve Stimulation

The purpose of this study is to investigate whether it is possible to accelerate learning and improve memory performance in VNS implanted tinnitus participants by pairing VNS with a verbal paired-associate learning task.

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...

Program of Intensive Support in Emergency Departments for Care: Partners of Cognitively Impaired Patients

Dementia is a common problem for older patients presenting to emergency departments and for the family caregivers who often lack support, understanding, and skills to manage the problems related to the need for emergency department visits. The purpose of Program of Intensive Support in Emergency Departments for Care Partners of Cognitively Impaired Patients (POISED-CPCIP, here on referred to as POISED) randomized controlled trial is to use previously established quality improve...

Investigating Accelerated Learning in Healthy Subjects: Trigeminal Nerve Stimulation

The purpose of this study is to investigate whether we can accelerate learning and improve associative memory performance in healthy subjects, by applying transcranial Direct Current Stimulation (tDCS) targeting the trigeminal nerve during a verbal paired-associate learning task

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.

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...

Smartphone Enabled Detection of Nocturnal Cough Rate and Sleep Quality as a Prognostic Marker for Asthma Control

The purpose of the study is to explore the value which cough rate might provide for asthma self-management. In this study, the focus will be specifically on nocturnal cough rate. The plan is to use a longitudinal study design, in order to investigate to which extent trends in the nocturnal cough rates might have meaningful implications for future asthma control and asthma exacerbations of patients. The incidence of nocturnal cough in asthmatics will be described and visualized ...

Prediction of the Cognitive Effects of Electroconvulsive Therapy Via Machine Learning and Neuroimaging

The study aims to use machine learning to predict the occurrence of episodic and autobiographical memory deficits as well as treatment response following a course of electroconvulsive therapy. Additionally, the neurophysiological correlates of the cognitive effects after a course of ECT will be investigated. Therefore, structural, resting-state and diffusion tensor images will be collected within one week before the first and after the last ECT treatment from severely depresse...

Letrozole in the Treatment of 1st and 2nd Line Hormone Receptor Positive Breast Cancer: Pre-therapeutic Risk Assessment

The course of the disease in female patients with metastatic mammary carcinoma can vary greatly. In this connection, the individual prognosis depends on a complex interaction of tumor- and patient-related factors. To take account of such differences, it is necessary to employ individual methods of treatment which are suited to the course of each patient's disease. Prof. Possinger and Dr. Schmid (Charite Berlin) and Prof. Wischnewsky (University of Bremen) have developed an appr...

Discovery Elbow Multi-Center Prospective Study

The purpose of this prospective clinical trial is to document the performance and clinical outcomes of Biomet Discovery Elbow.

Investigating Accelerated Learning and Memory in Healthy Subjects Using a Face Name Memory Task

The purpose of this study is to investigate whether we can accelerate learning and improve associative memory performance in healthy subjects by applying transcranial Direct Current Stimulation (tDCS) during a Face Name memory task.

Effect of Reward on Learning in Motor Cortex

Background: - Magnetic resonance imaging (MRI) uses a strong magnetic field and radio waves to take pictures of the brain. Some MRI studies suggest that this technique reveals brain differences in patients with a nervous system illness when compared to adults without a nervous system illness. Objectives: - To study functional changes in the brain that may be observed in people without any nervous system illness. - To learn more...

Comparison of Manual Cardiopulmonary Resuscitation (CPR) Versus Automatic CPR Machine During Ambulance Transport.

This is a randomized controlled prospective study which assigned patient to receive manual CPR or automatic CPR machine use. The quality and efficacy between manual CPR and machine CPR will be evaluated.

Machine Learning in Myeloma Response

Diffusion-weighted Whole Body Magnetic Resonance Imaging (WB-MRI) is a new technique that builds on existing Magnetic Resonance Imaging (MRI) technology. It uses the movement of water molecules in human tissue to define with great accuracy cancerous cells from normal cells. Using this technique the investigators can much more accurately define the spread and rate of cancer growth. This information is vital in the selection of patients' treatment pathways. WB-MRI images are obta...

Deep-Learning for Automatic Polyp Detection During Colonoscopy

The primary objective of this study is to examine the role of machine learning and computer aided diagnostics in automatic polyp detection and to determine whether a combination of colonoscopy and an automatic polyp detection software is a feasible way to increase adenoma detection rate compared to standard colonoscopy.

Identification of Clinically Occult Glioma Cells and Characterization of Glioma Behavior Through Machine Learning Analysis of Advanced Imaging Technology

Gliomas are one of the most challenging tumors to treat, because areas of the apparently normal brain contain microscopic deposits of glioma cells; indeed, these occult cells are known to infiltrate several centimeters beyond the clinically apparent lesion visualized on standard computer tomography or magnetic resonance imaging (MR). Since it is not feasible to remove or radiate large volumes of the brain, it is important to target only the visible tumor and the infiltrated re...

E-learning to Prevent Drug Errors in Anesthesia

The purpose of this observational study is to assess the efficiency of an e-learning in order to put into practice some preventive measures to avoid medication errors during anesthesia.


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