AIMC Topic: Mortality

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Logistic regression and machine learning predicted patient mortality from large sets of diagnosis codes comparably.

Journal of clinical epidemiology Jan 22, 2021
OBJECTIVE: The objective of the study was to compare the performance of logistic regression and boosted trees for predicting patient mortality from large sets of diagnosis codes in electronic healthcare records.
Machine Learning Forecasting Electronic Health Records Female Aged Aged, 80 and over Humans Age Factors Myocardial Infarction Sex Factors Hip Fractures International Classification of Diseases Mortality Colorectal Neoplasms Male Middle Aged England Logistic Models
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Machine learning to predict mortality after rehabilitation among patients with severe stroke.

Scientific reports Nov 18, 2020
Stroke is among the leading causes of death and disability worldwide. Approximately 20-25% of stroke survivors present severe disability, which is associated with increased mortality risk. Prognostication is inherent in the process of clinical decisi...
Stroke Humans Medicare Machine Learning Mortality Aged Algorithms Stroke Rehabilitation Male Clinical Decision-Making Middle Aged Female ROC Curve Logistic Models United States
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Predicting Postoperative Mortality After Metastatic Intraspinal Neoplasm Excision: Development of a Machine-Learning Approach.

World neurosurgery Nov 16, 2020
OBJECTIVE: Mortality following surgical resection of spinal tumors is a devastating outcome. Naïve Bayes machine learning algorithms may be leveraged in surgical planning to predict mortality. In this investigation, we use a Naïve Bayes classificatio...
Humans Machine Learning Female Aged Pulmonary Disease, Chronic Obstructive Hypertension Body Mass Index Bayes Theorem Ascites Hypoalbuminemia Dyspnea Male Middle Aged Risk Assessment Respiration, Artificial Weight Loss Odds Ratio Serum Albumin Mortality Preoperative Care Laminectomy Blood Coagulation Disorders Smoking Metastasectomy Spinal Neoplasms Multivariate Analysis
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Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study.

Scientific reports Oct 30, 2020
The rapid spread of COVID-19 has resulted in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model ...
Middle Aged Models, Statistical Coronavirus Infections Mortality Pneumonia, Viral Humans Machine Learning Pandemics COVID-19 Republic of Korea Female Aged Aged, 80 and over Adult Male
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Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients.

European radiology Sep 18, 2020
OBJECTIVE: To evaluate whether the initial chest X-ray (CXR) severity assessed by an AI system may have prognostic utility in patients with COVID-19.
Age Factors Radiography Emergency Service, Hospital Italy Artificial Intelligence Neurodegenerative Diseases COVID-19 Coronary Artery Disease Prognosis Sex Factors Retrospective Studies Proportional Hazards Models Tomography, X-Ray Computed Radiography, Thoracic Humans Intensive Care Units Mortality Male Female Middle Aged Comorbidity SARS-CoV-2 Severity of Illness Index Hospitalization Deep Learning Aged Pulmonary Disease, Chronic Obstructive
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Real-time AI prediction for major adverse cardiac events in emergency department patients with chest pain.

Scandinavian journal of trauma, resuscitation and emergency medicine Sep 11, 2020
BACKGROUND: A big-data-driven and artificial intelligence (AI) with machine learning (ML) approach has never been integrated with the hospital information system (HIS) for predicting major adverse cardiac events (MACE) in patients with chest pain in ...
Humans Models, Statistical Taiwan Emergency Service, Hospital Myocardial Infarction Artificial Intelligence Mortality Adult Algorithms Chest Pain Female Sensitivity and Specificity Aged Male Young Adult Middle Aged Risk Assessment
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Development, implementation, and prospective validation of a model to predict 60-day end-of-life in hospitalized adults upon admission at three sites.

BMC medical informatics and decision making Sep 7, 2020
BACKGROUND: Automated systems that use machine learning to estimate a patient's risk of death are being developed to influence care. There remains sparse transparent reporting of model generalizability in different subpopulations especially for imple...
Mortality Adult Female Male Hospitalization Middle Aged Aged Adolescent Humans Aged, 80 and over Patient Admission Machine Learning Young Adult Prospective Studies Prognosis Electronic Health Records
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A machine learning approach for mortality prediction only using non-invasive parameters.

Medical & biological engineering & computing Jul 20, 2020
At present, the traditional scoring methods generally utilize laboratory measurements to predict mortality. It results in difficulties of early mortality prediction in the rural areas lack of professional laboratorians and medical laboratory equipmen...
Machine Learning Algorithms Female Reproducibility of Results Male Hospital Mortality Humans Area Under Curve Critical Care Mortality
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Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network.

Nature medicine May 11, 2020
The electrocardiogram (ECG) is a widely used medical test, consisting of voltage versus time traces collected from surface recordings over the heart. Here we hypothesized that a deep neural network (DNN) can predict an important future clinical event...
Female Area Under Curve Middle Aged Humans Neural Networks, Computer Aged Cause of Death Risk Assessment Algorithms Aged, 80 and over Deep Learning Proportional Hazards Models Prognosis Adult ROC Curve Mortality Retrospective Studies Male Electrocardiography Cardiologists
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Update to 'Deep-learning model for predicting 30-day postoperative mortality' (Br J Anaesth 2019; 123: 688-95).

British journal of anaesthesia May 7, 2020
Forecasting Humans Deep Learning Postoperative Period Mortality
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