AIMC Topic: Intensive Care Units

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Toward the "Plateau of Productivity": Enhancing the Value of Machine Learning in Critical Care.

Critical care medicine Jul 1, 2018
Humans Machine Learning Algorithms Intensive Care Units Critical Care
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Predicting Intensive Care Unit Readmission with Machine Learning Using Electronic Health Record Data.

Annals of the American Thoracic Society Jul 1, 2018
RATIONALE: Patients transferred from the intensive care unit to the wards who are later readmitted to the intensive care unit have increased length of stay, healthcare expenditure, and mortality compared with those who are never readmitted. Improving...
Patient Readmission Illinois Risk Assessment Humans Time Factors Machine Learning Intensive Care Units ROC Curve Retrospective Studies Electronic Health Records Follow-Up Studies Critical Illness Female Male Middle Aged
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Inclusion of Unstructured Clinical Text Improves Early Prediction of Death or Prolonged ICU Stay.

Critical care medicine Jul 1, 2018
OBJECTIVES: Early prediction of undesired outcomes among newly hospitalized patients could improve patient triage and prompt conversations about patients' goals of care. We evaluated the performance of logistic regression, gradient boosting machine, ...
Humans Machine Learning Decision Support Techniques Middle Aged Length of Stay Retrospective Studies Intensive Care Units Hospital Mortality Female Natural Language Processing Aged Patient Care Planning Male
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Development and Evaluation of an Automated Machine Learning Algorithm for In-Hospital Mortality Risk Adjustment Among Critical Care Patients.

Critical care medicine Jun 1, 2018
OBJECTIVES: Risk adjustment algorithms for ICU mortality are necessary for measuring and improving ICU performance. Existing risk adjustment algorithms are not widely adopted. Key barriers to adoption include licensing and implementation costs as wel...
Algorithms Intensive Care Units Female Male Middle Aged Humans Hospital Mortality Models, Statistical Unsupervised Machine Learning Risk Adjustment
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[Application of support vector machine in predicting in-hospital mortality risk of patients with acute kidney injury in ICU].

Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences Apr 18, 2018
OBJECTIVE: To construct an in-hospital mortality prediction model for patients with acute kidney injury (AKI) in intensive care unit (ICU) by using support vector machine (SVM), and compare it with the simplified acute physiology score II (SAPS-II) w...
Critical Care Humans Prognosis Sensitivity and Specificity Acute Kidney Injury Hospital Mortality Intensive Care Units ROC Curve Support Vector Machine
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An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.

Critical care medicine Apr 1, 2018
OBJECTIVES: Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-ti...
Electrocardiography Age Factors Blood Pressure Heart Rate Sex Factors Hospital Mortality ROC Curve Decision Support Systems, Clinical Socioeconomic Factors Academic Medical Centers Time-to-Treatment Organ Dysfunction Scores Vital Signs Electronic Health Records Humans Female Machine Learning Aged Critical Illness Male Middle Aged Intensive Care Units Comorbidity Severity of Illness Index Sepsis Time Factors
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Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach.

Critical care medicine Oct 1, 2017
OBJECTIVES: Identifying subgroups of ICU patients with similar clinical needs and trajectories may provide a framework for more efficient ICU care through the design of care platforms tailored around patients' shared needs. However, objective methods...
Male Middle Aged Cluster Analysis Humans Critical Care Needs Assessment Machine Learning California Intensive Care Units Female Aged
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Patient-Specific Classification of ICU Sedation Levels From Heart Rate Variability.

Critical care medicine Jul 1, 2017
OBJECTIVE: To develop a personalizable algorithm to discriminate between sedation levels in ICU patients based on heart rate variability.
Algorithms Intensive Care Units Humans Support Vector Machine Male Female Heart Rate Middle Aged Aged Anesthesia Respiration, Artificial Boston Pilot Projects Electrocardiography
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Prevalence of cytomegalovirus disease in kidney transplant patients in an intensive care unit.

Revista Brasileira de terapia intensiva Jan 1, 2017
OBJECTIVES: To define the frequency of cytomegalovirus disease among kidney transplant patients in an intensive care unit in which this complication was suspected and to identify predisposing factors and their possible impact on clinical outcome.
Female Cytomegalovirus Prevalence Humans Aged Time Factors Cytomegalovirus Infections Kidney Transplantation Adult Logistic Models Polymerase Chain Reaction Retrospective Studies Male Immunosuppressive Agents Antilymphocyte Serum Intensive Care Units Middle Aged
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Cell Free DNA and Procalcitonin as Early Markers of Complications in ICU Patients with Multiple Trauma and Major Surgery.

Clinical laboratory Dec 1, 2016
BACKGROUND: Cell free DNA (cfDNA) was recently suggested as a new marker of sepsis and poor outcome in ICU patients. Procalcitonin has also been the focus of attention as an early marker for systemic inflammation and sepsis.
Lactic Acid Injury Severity Score Organ Dysfunction Scores Calcitonin Area Under Curve Reproducibility of Results DNA Risk Factors Early Diagnosis Adult Surgical Procedures, Operative Male ROC Curve Biomarkers Middle Aged Postoperative Complications Humans Cell-Free Nucleic Acids Length of Stay Prognosis Multiple Trauma Sepsis Intensive Care Units Predictive Value of Tests Time Factors Female C-Reactive Protein Hospital Mortality
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