AIMC Topic: Aged, 80 and over

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Prediction, pattern recognition and modelling of complications post-endovascular infra renal aneurysm repair by artificial intelligence.

Vascular Aug 24, 2020
OBJECTIVES: The study evaluates the plausibility and applicability of prediction, pattern recognition and modelling of complications post-endovascular aneurysm repair (EVAR) by artificial intelligence for more accurate surveillance in practice.
Aortic Aneurysm, Abdominal Pattern Recognition, Automated Blood Vessel Prosthesis Implantation Endovascular Procedures Graft Occlusion, Vascular Blood Vessel Prosthesis Humans Risk Factors Artificial Intelligence Aged Postoperative Complications Prospective Studies Aged, 80 and over Time Factors Female Male Treatment Outcome Middle Aged Support Vector Machine Risk Assessment Decision Trees Foreign-Body Migration Endoleak
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Artificial Intelligence-Driven Oncology Clinical Decision Support System for Multidisciplinary Teams.

Sensors (Basel, Switzerland) Aug 20, 2020
Watson for Oncology (WfO) is a clinical decision support system driven by artificial intelligence. In Korea, WfO is used by multidisciplinary teams (MDTs) caring for cancer patients. This study aimed to investigate the effect of WfO use on hospital s...
Female Middle Aged Reproducibility of Results Decision Support Systems, Clinical Aged Humans Patient Satisfaction Aged, 80 and over Patient Care Team Artificial Intelligence Adult Interdisciplinary Communication Retrospective Studies Male Republic of Korea
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Machine learning-based automated classification of headache disorders using patient-reported questionnaires.

Scientific reports Aug 20, 2020
Classification of headache disorders is dependent on a subjective self-report from patients and its interpretation by physicians. We aimed to apply objective data-driven machine learning approaches to analyze patient-reported symptoms and test the fe...
Adolescent Child Headache Patient Reported Outcome Measures Automation Aged, 80 and over Patients Young Adult Adult Humans Male Machine Learning Middle Aged Female Surveys and Questionnaires Aged
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The FUTUREPAIN study: Validating a questionnaire to predict the probability of having chronic pain 7-10 years into the future.

PloS one Aug 20, 2020
OBJECTIVES: The FUTUREPAIN study develops a short general-purpose questionnaire, based on the biopsychosocial model, to predict the probability of developing or maintaining moderate-to-severe chronic pain 7-10 years into the future.
ROC Curve Chronic Pain Aged Humans Aged, 80 and over Machine Learning Adult Algorithms Male Retrospective Studies Middle Aged Female Surveys and Questionnaires
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Using Machine Learning to Make Predictions in Patients Who Fall.

The Journal of surgical research Aug 18, 2020
BACKGROUND: As the population ages, the incidence of traumatic falls has been increasing. We hypothesize that a machine learning algorithm can more accurately predict mortality after a fall compared with a standard logistic regression (LR) model base...
Aged Aged, 80 and over Adult Male Middle Aged Length of Stay Accidental Falls ROC Curve Decision Trees Patient Discharge Injury Severity Score Trauma Centers Humans Machine Learning Algorithms Retrospective Studies Intensive Care Units Female Glasgow Coma Scale
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Utilization of machine-learning models to accurately predict the risk for critical COVID-19.

Internal and emergency medicine Aug 18, 2020
Among patients with Coronavirus disease (COVID-19), the ability to identify patients at risk for deterioration during their hospital stay is essential for effective patient allocation and management. To predict patient risk for critical COVID-19 base...
Pneumonia, Viral Humans Male Machine Learning Middle Aged Pandemics Risk Assessment COVID-19 ROC Curve Retrospective Studies Female Coronavirus Infections Critical Illness APACHE Hospitalization Aged Aged, 80 and over Adult
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Predicting In-Hospital Mortality at Admission to the Medical Ward: A Big-Data Machine Learning Model.

The American journal of medicine Aug 15, 2020
BACKGROUND: General medical wards admit high-risk patients. Artificial intelligence algorithms can use big data for developing models to assess patients' risk stratification. The aim of this study was to develop a mortality prediction machine learnin...
Adolescent Hospital Mortality Inpatients Humans Machine Learning Female Risk Factors Aged Aged, 80 and over Young Adult Adult Male Middle Aged Big Data
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Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets.

Nature communications Aug 14, 2020
Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other ...
Pandemics COVID-19 Algorithms Tomography, X-Ray Computed Female SARS-CoV-2 Humans Artificial Intelligence Lung Deep Learning Adolescent Child, Preschool Child Imaging, Three-Dimensional Aged Aged, 80 and over Young Adult Adult Male Middle Aged Pneumonia, Viral Radiographic Image Interpretation, Computer-Assisted Coronavirus Infections COVID-19 Testing Clinical Laboratory Techniques Betacoronavirus
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Identifying Phenogroups in patients with subclinical diastolic dysfunction using unsupervised statistical learning.

BMC cardiovascular disorders Aug 14, 2020
BACKGROUND: Subclinical diastolic dysfunction is a precursor for developing heart failure with preserved ejection fraction (HFpEF); yet not all patients progress to HFpEF. Our objective was to evaluate clinical and echocardiographic variables to iden...
Asymptomatic Diseases Diastole Unsupervised Machine Learning Predictive Value of Tests Aged Peptide Fragments Heart Failure Aged, 80 and over Ventricular Dysfunction, Left Male Humans Natriuretic Peptide, Brain Ventricular Function, Left Biomarkers Stroke Volume Retrospective Studies Echocardiography Female Middle Aged Risk Factors Risk Assessment Disease Progression Diagnosis, Computer-Assisted Deep Learning Cluster Analysis
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Automated classification of hip fractures using deep convolutional neural networks with orthopedic surgeon-level accuracy: ensemble decision-making with antero-posterior and lateral radiographs.

Acta orthopaedica Aug 12, 2020
Background and purpose - Deep-learning approaches based on convolutional neural networks (CNNs) are gaining interest in the medical imaging field. We evaluated the diagnostic performance of a CNN to discriminate femoral neck fractures, trochanteric f...
Humans Neural Networks, Computer Femoral Neck Fractures Female Aged, 80 and over Sensitivity and Specificity Outcome Assessment, Health Care Hip Fractures Male Femur Diagnosis, Computer-Assisted Medical Records, Problem-Oriented Deep Learning Orthopedic Surgeons Diagnosis, Differential Radiography
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