AIMC Topic: Proof of Concept Study

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Home care aides' observations and machine learning algorithms for the prediction of visits to emergency departments by older community-dwelling individuals receiving home care assistance: A proof of concept study.

PloS one Aug 13, 2019
BACKGROUND: Older individuals receiving home assistance are at high risk for emergency visits and unplanned hospitalization. Anticipating their health difficulties could prevent these events. This study investigated the effectiveness of an at-home mo...
Surveys and Questionnaires Proof of Concept Study Female Frail Elderly Independent Living Hospitalization Predictive Value of Tests Home Care Services Aged Emergency Service, Hospital Humans Home Health Aides Aged, 80 and over Geriatric Assessment Machine Learning Male Algorithms Prospective Studies
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Use of Natural Language Processing Tools to Identify and Classify Periprosthetic Femur Fractures.

The Journal of arthroplasty Jul 24, 2019
BACKGROUND: Manual chart review is labor-intensive and requires specialized knowledge possessed by highly trained medical professionals. The cost and infrastructure challenges required to implement this is prohibitive for most hospitals. Natural lang...
Aged Cohort Studies Natural Language Processing Aged, 80 and over Proof of Concept Study Periprosthetic Fractures Sensitivity and Specificity Femoral Fractures Male Orthopedics Surgeons Humans Algorithms Electronic Health Records Language Female
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Development and Evaluation of ClientBot: Patient-Like Conversational Agent to Train Basic Counseling Skills.

Journal of medical Internet research Jul 15, 2019
BACKGROUND: Training therapists is both expensive and time-consuming. Degree-based training can require tens of thousands of dollars and hundreds of hours of expert instruction. Counseling skills practice often involves role-plays, standardized patie...
Proof of Concept Study Counseling Humans Communication Deep Learning Psychotherapy
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Deep learning to differentiate parkinsonian disorders separately using single midsagittal MR imaging: a proof of concept study.

European radiology Jul 1, 2019
OBJECTIVES: To evaluate the diagnostic performance of deep learning with the convolutional neural networks (CNN) to distinguish each representative parkinsonian disorder using MRI.
Female Aged Aged, 80 and over Adult Male Humans Neural Networks, Computer Retrospective Studies Diagnosis, Differential Parkinsonian Disorders Proof of Concept Study Multiple System Atrophy Middle Aged Deep Learning ROC Curve Magnetic Resonance Imaging Parkinson Disease Supranuclear Palsy, Progressive
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Artificial intelligence and machine learning for predicting acute kidney injury in severely burned patients: A proof of concept.

Burns : journal of the International Society for Burn Injuries Jun 21, 2019
BACKGROUND: Burn critical care represents a high impact population that may benefit from artificial intelligence and machine learning (ML). Acute kidney injury (AKI) recognition in burn patients could be enhanced by ML. The goal of this study was to ...
Artificial Intelligence Machine Learning Acute Kidney Injury Female Young Adult Humans Burns Creatinine Natriuretic Peptide, Brain Urine Proof of Concept Study Adult Male Middle Aged Peptide Fragments Lipocalin-2
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Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features.

European radiology May 15, 2019
OBJECTIVES: To develop a proof-of-concept "interpretable" deep learning prototype that justifies aspects of its predictions from a pre-trained hepatic lesion classifier.
Bile Duct Neoplasms Proof of Concept Study Cholangiocarcinoma Bile Ducts, Intrahepatic Aged Adult Male Middle Aged Deep Learning Liver Neoplasms Carcinoma, Hepatocellular Magnetic Resonance Imaging Predictive Value of Tests Image Interpretation, Computer-Assisted Neural Networks, Computer Machine Learning Algorithms Retrospective Studies Female Humans
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DeepCEST: 9.4 T Chemical exchange saturation transfer MRI contrast predicted from 3 T data - a proof of concept study.

Magnetic resonance in medicine Feb 25, 2019
PURPOSE: To determine the feasibility of employing the prior knowledge of well-separated chemical exchange saturation transfer (CEST) signals in the 9.4 T Z-spectrum to separate overlapping CEST signals acquired at 3 T, using a deep learning approach...
Contrast Media Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Proof of Concept Study Humans Neural Networks, Computer Brain Brain Neoplasms Imaging, Three-Dimensional
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Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence.

Nature medicine Feb 11, 2019
Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have already demonstrated strong performance in image-based diagnoses, analysis of diverse and massive e...
Artificial Intelligence Machine Learning Retrospective Studies Electronic Health Records Female Infant, Newborn Reproducibility of Results Male Infant Diagnosis, Computer-Assisted Humans Natural Language Processing Pediatrics Proof of Concept Study Deep Learning China Adolescent Child, Preschool Child
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The use of artificial neural network analysis can improve the risk-stratification of patients presenting with suspected deep vein thrombosis.

British journal of haematology Feb 6, 2019
Artificial neural networks are machine-learning algorithms designed to analyse data without a pre-existing hypothesis as to any associations that may exist. This technique has not previously been applied to the risk stratification of patients referre...
Predictive Value of Tests False Negative Reactions Proof of Concept Study Fibrin Fibrinogen Degradation Products Algorithms Humans Biomarkers Neural Networks, Computer Retrospective Studies Male Female Middle Aged Adult Risk Assessment Ultrasonography Venous Thrombosis
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Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance Jan 7, 2019
BACKGROUND: Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction. Advances in machine learning ha...
Proof of Concept Study Aged Blood Flow Velocity Humans Male Machine Learning Aorta Middle Aged Prospective Studies Myocardial Perfusion Imaging Hemodynamics Retrospective Studies Aortic Valve Magnetic Resonance Imaging, Cine United States Female Predictive Value of Tests Reproducibility of Results Heart Diseases Image Interpretation, Computer-Assisted Automation
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