AIMC Topic:
Predictive Value of Tests

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Predicting mechanical restraint of psychiatric inpatients by applying machine learning on electronic health data.

Acta psychiatrica Scandinavica Jul 6, 2019
OBJECTIVE: Mechanical restraint (MR) is used to prevent patients from harming themselves or others during inpatient treatment. The objective of this study was to investigate whether incident MR occurring in the first 3 days following admission could ...
Machine Learning Mental Disorders Electronic Health Records Female Hospitalization Denmark Humans Predictive Value of Tests Self-Injurious Behavior Sensitivity and Specificity Restraint, Physical Male Early Warning Score Case-Control Studies Time Factors Inpatients
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Development and validation of three machine-learning models for predicting multiple organ failure in moderately severe and severe acute pancreatitis.

BMC gastroenterology Jul 4, 2019
BACKGROUND: Multiple organ failure (MOF) is a serious complication of moderately severe (MASP) and severe acute pancreatitis (SAP). This study aimed to develop and assess three machine-learning models to predict MOF.
Humans Male Middle Aged Risk Assessment Severity of Illness Index Logistic Models Machine Learning Prognosis Retrospective Studies Female Adult Multiple Organ Failure Predictive Value of Tests Acute Disease APACHE Pancreatitis
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Artificial Intelligence Algorithms to Assess Hormonal Status From Tissue Microarrays in Patients With Breast Cancer.

JAMA network open Jul 3, 2019
IMPORTANCE: Immunohistochemistry (IHC) is the most widely used assay for identification of molecular biomarkers. However, IHC is time consuming and costly, depends on tissue-handling protocols, and relies on pathologists' subjective interpretation. I...
Receptor, ErbB-2 Receptors, Estrogen British Columbia Tissue Array Analysis Middle Aged ROC Curve Predictive Value of Tests Feasibility Studies Immunohistochemistry Humans Artificial Intelligence Algorithms Retrospective Studies Female Reproducibility of Results Breast Neoplasms Biomarkers, Tumor
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Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment Study.

Eye (London, England) Jul 2, 2019
OBJECTIVES: To evaluate the performance of a deep learning based Artificial Intelligence (AI) software for detection of glaucoma from stereoscopic optic disc photographs, and to compare this performance to the performance of a large cohort of ophthal...
Photography Ophthalmologists Diagnosis, Computer-Assisted Glaucoma, Open-Angle Humans Optometrists Deep Learning Artificial Intelligence Clinical Competence Retrospective Studies ROC Curve Reproducibility of Results Predictive Value of Tests Europe Sensitivity and Specificity Observer Variation Optic Disk Optic Nerve Diseases False Positive Reactions
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CT texture analysis for the prediction of KRAS mutation status in colorectal cancer via a machine learning approach.

European journal of radiology Jul 2, 2019
PURPOSE: This study aimed to investigate whether a machine learning-based computed tomography (CT) texture analysis could predict the mutation status of V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) in colorectal cancer.
Radiographic Image Interpretation, Computer-Assisted Proto-Oncogene Proteins p21(ras) Colorectal Neoplasms Retrospective Studies Male Humans Tomography, X-Ray Computed ROC Curve Machine Learning Female Predictive Value of Tests Aged Mutation
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Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis.

Journal of clinical pathology Jun 29, 2019
AIMS: Morphological differentiation among different blast cell lineages is a difficult task and there is a lack of automated analysers able to recognise these abnormal cells. This study aims to develop a machine learning approach to predict the diagn...
Leukemia Humans Leukocytes Reproducibility of Results Machine Learning Image Interpretation, Computer-Assisted Staining and Labeling Pattern Recognition, Automated Diagnosis, Differential Blood Specimen Collection Predictive Value of Tests Cell Lineage Acute Disease
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Utilization of Artificial Intelligence in Echocardiography.

Circulation journal : official journal of the Japanese Circulation Society Jun 29, 2019
Echocardiography has a central role in the diagnosis and management of cardiovascular disease. Precise and reliable echocardiographic assessment is required for clinical decision-making. Even if the development of new technologies (3-dimentional echo...
Workflow Diffusion of Innovation Cardiovascular Diseases Image Interpretation, Computer-Assisted Humans Echocardiography Machine Learning Diagnosis, Computer-Assisted Deep Learning Forecasting Predictive Value of Tests Reproducibility of Results
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A Machine Learning-Based Approach for the Prediction of Acute Coronary Syndrome Requiring Revascularization.

Journal of medical systems Jun 28, 2019
The aim of this study is to predict acute coronary syndrome (ACS) requiring revascularization in those patients presenting early-stage angina-like symptom using machine learning algorithms. We obtained data from 2344 ACS patients, who required revasc...
Algorithms Female Male Risk Assessment ROC Curve Predictive Value of Tests Acute Coronary Syndrome Myocardial Revascularization Humans Machine Learning
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Identification of a Multiplex Biomarker Panel for Hypertrophic Cardiomyopathy Using Quantitative Proteomics and Machine Learning.

Molecular & cellular proteomics : MCP Jun 26, 2019
Hypertrophic cardiomyopathy (HCM) is defined by pathological left ventricular hypertrophy (LVH). It is the commonest inherited cardiac condition and a significant number of high risk cases still go undetected until a sudden cardiac death (SCD) event....
Humans Machine Learning Severity of Illness Index Case-Control Studies Proteomics Predictive Value of Tests Young Adult Mutation Phenotype Adult Cardiomyopathy, Hypertrophic Male Prospective Studies Hypertrophy, Left Ventricular Middle Aged Biomarkers Sarcomeres Peptides Female Aged
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Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention.

JACC. Cardiovascular interventions Jun 26, 2019
OBJECTIVES: This study sought to determine whether machine learning can be used to better identify patients at risk for death or congestive heart failure (CHF) rehospitalization after percutaneous coronary intervention (PCI).
Heart Failure Humans Machine Learning Patient Readmission Predictive Value of Tests Minnesota Decision Support Techniques Female Middle Aged Registries Clinical Decision-Making Reproducibility of Results Percutaneous Coronary Intervention Risk Assessment Risk Factors Coronary Artery Disease Time Factors Aged Hospital Mortality Male Treatment Outcome
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