AIMC Topic: Risk Factors

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Enhanced neonatal surgical site infection prediction model utilizing statistically and clinically significant variables in combination with a machine learning algorithm.

American journal of surgery Jul 24, 2018
BACKGROUND: Machine-learning can elucidate complex relationships/provide insight to important variables for large datasets. This study aimed to develop an accurate model to predict neonatal surgical site infections (SSI) using different statistical m...
Area Under Curve Surgical Wound Infection Risk Factors Male Risk Assessment Infant ROC Curve Logistic Models Algorithms Decision Support Techniques Retrospective Studies Female Infant, Newborn Humans Machine Learning
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Estimating risk of severe neonatal morbidity in preterm births under 32 weeks of gestation.

The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians Jul 18, 2018
A large recent study analyzed the relationship between multiple factors and neonatal outcome and in preterm births. Study variables included the reason for admission, indication for delivery, optimal steroid use, gestational age, and other potential...
Infant, Newborn Premature Birth Risk Factors Adult Male Risk Assessment Machine Learning Prospective Studies Prognosis Pregnancy Infant Mortality Female Infant Humans Gestational Age Severity of Illness Index Predictive Value of Tests Stillbirth Morbidity Infant, Premature, Diseases Probability Diagnostic Techniques, Obstetrical and Gynecological Infant, Small for Gestational Age
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A review of statistical and machine learning methods for modeling cancer risk using structured clinical data.

Artificial intelligence in medicine Jul 14, 2018
Advancements are constantly being made in oncology, improving prevention and treatment of cancers. To help reduce the impact and deadliness of cancers, they must be detected early. Additionally, there is a risk of cancers recurring after potentially ...
Data Mining Recurrence Nomograms Early Detection of Cancer Decision Trees Data Interpretation, Statistical Neoplasm Staging Humans Machine Learning Decision Support Techniques Electronic Health Records Clinical Decision-Making Risk Factors Risk Assessment Neoplasms Diagnosis, Computer-Assisted
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Machine learning algorithm-based risk prediction model of coronary artery disease.

Molecular biology reports Jul 11, 2018
In view of high mortality associated with coronary artery disease (CAD), development of an early predicting tool will be beneficial in reducing the burden of the disease. The database comprising demographic, conventional, folate/xenobiotic genetic ri...
Humans Machine Learning Algorithms Forecasting Middle Aged Case-Control Studies Genetic Predisposition to Disease Polymorphism, Single Nucleotide Coronary Artery Disease Folic Acid Female Risk Factors Aged Adult Male Epistasis, Genetic Multifactor Dimensionality Reduction Xenobiotics Cytochrome P-450 CYP1A1 Glycine Hydroxymethyltransferase
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Assessment of the Feasibility of automated, real-time clinical decision support in the emergency department using electronic health record data.

BMC emergency medicine Jul 3, 2018
BACKGROUND: The use of big data and machine learning within clinical decision support systems (CDSSs) has the potential to transform medicine through better prognosis, diagnosis and automation of tasks. Real-time application of machine learning algor...
Male Middle Aged Severity of Illness Index Time Factors Decision Support Systems, Clinical Humans Age Factors Machine Learning Emergency Service, Hospital Electronic Health Records Documentation Female Vital Signs Reproducibility of Results Racial Groups Risk Factors
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Bioimpedance and New-Onset Heart Failure: A Longitudinal Study of >500 000 Individuals From the General Population.

Journal of the American Heart Association Jun 29, 2018
BACKGROUND: Heart failure constitutes a high burden on patients and society, but although lifetime risk is high, it is difficult to predict without costly or invasive testing. We aimed to establish new risk factors of heart failure, which potentially...
Age Factors Humans United Kingdom Prospective Studies Middle Aged Machine Learning Body Composition Leg Prognosis Risk Assessment Decision Support Techniques Myocardial Infarction Female Incidence Heart Failure Risk Factors Time Factors Sex Factors Aged Longitudinal Studies Electric Impedance Male
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An Algorithm Based on Deep Learning for Predicting In-Hospital Cardiac Arrest.

Journal of the American Heart Association Jun 26, 2018
BACKGROUND: In-hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track-and-trigger systems are used to predict cardiac arrest early, they have limitations, with low sensitivity and high fal...
Resuscitation Seoul Time Factors Inpatients Predictive Value of Tests Early Diagnosis Vital Signs Heart Arrest Retrospective Studies Male Female Middle Aged Reproducibility of Results Risk Assessment Humans Risk Factors Diagnosis, Computer-Assisted Aged Decision Support Techniques Deep Learning Adult Prognosis
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Predicting Survival From Large Echocardiography and Electronic Health Record Datasets: Optimization With Machine Learning.

JACC. Cardiovascular imaging Jun 13, 2018
OBJECTIVES: The goal of this study was to use machine learning to more accurately predict survival after echocardiography.
Humans Machine Learning Databases, Factual Prognosis Retrospective Studies Electronic Health Records Reproducibility of Results Risk Factors Echocardiography Risk Assessment Time Factors Predictive Value of Tests Data Mining Heart Diseases
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Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study.

PloS one Jun 7, 2018
Identification of risk factors of treatment resistance may be useful to guide treatment selection, avoid inefficient trial-and-error, and improve major depressive disorder (MDD) care. We extended the work in predictive modeling of treatment resistant...
Antidepressive Agents Adult Humans Drug Resistance Male Machine Learning Depressive Disorder, Major Databases, Factual Predictive Value of Tests Female Models, Biological Risk Factors
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Machine-learning-derived classifier predicts absence of persistent pain after breast cancer surgery with high accuracy.

Breast cancer research and treatment Jun 6, 2018
BACKGROUND: Prevention of persistent pain following breast cancer surgery, via early identification of patients at high risk, is a clinical need. Supervised machine-learning was used to identify parameters that predict persistence of significant pain...
Aged Adult Breast Neoplasms Humans Middle Aged Machine Learning Time Factors Follow-Up Studies Prognosis Pain, Postoperative Female Mastectomy Reproducibility of Results Supervised Machine Learning Risk Factors
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