AIMC Topic:
Databases, Factual

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Deep learning for automated cerebral aneurysm detection on computed tomography images.

International journal of computer assisted radiology and surgery Feb 13, 2020
PURPOSE: Cerebrovascular aneurysms are being observed with rapidly increasing incidence. Therefore, tools are needed for accurate and efficient detection of aneurysms. We used deep learning techniques with CT angiography acquired from multiple medica...
Image Processing, Computer-Assisted Cerebral Angiography Intracranial Aneurysm Databases, Factual Tomography, X-Ray Computed Sensitivity and Specificity Deep Learning Humans
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PTML Model of ChEMBL Compounds Assays for Vitamin Derivatives.

ACS combinatorial science Feb 13, 2020
Determining the biological activity of vitamin derivatives is needed given that organic synthesis of analogs of vitamins is an active field of interest for medicinal chemistry, pharmaceuticals, and food additives. Accordingly, scientists from differe...
Molecular Structure Bayes Theorem Vitamins Combinatorial Chemistry Techniques Machine Learning Databases, Factual Models, Statistical
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Development and Validation of a Deep Neural Network for Accurate Evaluation of Endoscopic Images From Patients With Ulcerative Colitis.

Gastroenterology Feb 12, 2020
BACKGROUND & AIMS: There are intra- and interobserver variations in endoscopic assessment of ulcerative colitis (UC) and biopsies are often collected for histologic evaluation. We sought to develop a deep neural network system for consistent, objecti...
Remission Induction Middle Aged Image Interpretation, Computer-Assisted Colon Humans Colonoscopy Databases, Factual Aged, 80 and over Wound Healing Prospective Studies Severity of Illness Index Young Adult Diagnosis, Computer-Assisted Colitis, Ulcerative Adult Deep Learning Female Predictive Value of Tests Male Adolescent Reproducibility of Results Biopsy Treatment Outcome Aged
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Deep Learning in Physiological Signal Data: A Survey.

Sensors (Basel, Switzerland) Feb 11, 2020
Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially expl...
Signal Processing, Computer-Assisted Electromyography Humans Databases, Factual Deep Learning Electrocardiography Electroencephalography
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Development and application of artificial intelligence in cardiac imaging.

The British journal of radiology Feb 6, 2020
In this review, we describe the technical aspects of artificial intelligence (AI) in cardiac imaging, starting with radiomics, basic algorithms of deep learning and application tasks of algorithms, until recently the availability of the public databa...
Vascular Calcification Humans Computed Tomography Angiography Cardiomyopathies Artificial Intelligence Heart Coronary Stenosis Algorithms Myocardial Infarction Fractional Flow Reserve, Myocardial Databases, Factual Heart Ventricles Deep Learning Prognosis Coronary Disease Magnetic Resonance Imaging Adipose Tissue
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Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach.

BMC medical informatics and decision making Feb 6, 2020
BACKGROUND: A common problem in machine learning applications is availability of data at the point of decision making. The aim of the present study was to use routine data readily available at admission to predict aspects relevant to the organization...
Humans Machine Learning Decision Support Techniques Databases, Factual Prognosis Logistic Models Germany Hospitals, Psychiatric Adult Electronic Health Records Male Female Middle Aged Hospitalization ROC Curve Aged
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Deep learning for prediction of cardiac indices from photoplethysmographic waveform: A virtual database approach.

International journal for numerical methods in biomedical engineering Feb 4, 2020
Deep learning methods combined with large datasets have recently shown significant progress in solving several medical tasks. However, collecting and annotating large datasets can be a very cumbersome and expensive task. We tackle these problems with...
Humans Machine Learning Databases, Factual Computer Simulation Deep Learning
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Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone.

BMC medical informatics and decision making Feb 3, 2020
BACKGROUND: Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of...
Forecasting Databases, Factual Electronic Health Records Female Humans Machine Learning Algorithms Creatinine Heart Failure Survival Analysis Adult Male Stroke Volume Middle Aged Risk Factors Aged Aged, 80 and over
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User-controlled pipelines for feature integration and head and neck radiation therapy outcome predictions.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) Feb 2, 2020
PURPOSE: Precision cancer medicine is dependent on accurate prediction of disease and treatment outcome, requiring integration of clinical, imaging and interventional knowledge. User controlled pipelines are capable of feature integration with varied...
Databases, Factual Prognosis Tomography, X-Ray Computed Logistic Models Humans Machine Learning Radiotherapy Planning, Computer-Assisted Head Radiotherapy Dosage Phantoms, Imaging Treatment Outcome Head and Neck Neoplasms Neck Area Under Curve
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Towards Domain Invariant Heart Sound Abnormality Detection Using Learnable Filterbanks.

IEEE journal of biomedical and health informatics Jan 31, 2020
OBJECTIVE: Cardiac auscultation is the most practiced non-invasive and cost-effective procedure for the early diagnosis of heart diseases. While machine learning based systems can aid in automatically screening patients, the robustness of these syste...
Databases, Factual Humans Heart Sounds Sensitivity and Specificity Phonocardiography Signal Processing, Computer-Assisted Neural Networks, Computer Heart Diseases Algorithms
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