AIMC Topic: Intensive Care Units

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Machine learning-based dynamic mortality prediction after traumatic brain injury.

Scientific reports Nov 27, 2019
Our aim was to create simple and largely scalable machine learning-based algorithms that could predict mortality in a real-time fashion during intensive care after traumatic brain injury. We performed an observational multicenter study including adul...
Logistic Models Brain Injuries, Traumatic Intracranial Pressure Cerebrovascular Circulation Glasgow Coma Scale Aged Adult Male Middle Aged ROC Curve Humans Machine Learning Prognosis Intensive Care Units Female
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Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction.

Journal of healthcare engineering Nov 3, 2019
OBJECTIVE: Achieving accurate prediction of sepsis detection moment based on bedside monitor data in the intensive care unit (ICU). A good clinical outcome is more probable when onset is suspected and treated on time, thus early insight of sepsis ons...
Analysis of Variance Vital Signs Male Middle Aged Case-Control Studies Diagnosis, Computer-Assisted Sepsis Female Humans Aged Machine Learning Aged, 80 and over Software Young Adult Algorithms Adolescent Adult Intensive Care Units Support Vector Machine Area Under Curve Models, Biological Early Diagnosis
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Machine learning to predict 30-day quality-adjusted survival in critically ill patients with cancer.

Journal of critical care Nov 2, 2019
PURPOSE: To develop and compare the predictive performance of machine-learning algorithms to estimate the risk of quality-adjusted life year (QALY) lower than or equal to 30 days (30-day QALY).
Adult Sensitivity and Specificity Male Middle Aged Humans Brazil Machine Learning Critical Illness Algorithms Intensive Care Units Prospective Studies Female Prognosis Hospitalization Signal Processing, Computer-Assisted Neoplasms Aged Probability Quality of Life Pattern Recognition, Automated ROC Curve False Positive Reactions Decision Trees Hospitals, Public Area Under Curve
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ICU staffing feature phenotypes and their relationship with patients' outcomes: an unsupervised machine learning analysis.

Intensive care medicine Oct 8, 2019
PURPOSE: To study whether ICU staffing features are associated with improved hospital mortality, ICU length of stay (LOS) and duration of mechanical ventilation (MV) using cluster analysis directed by machine learning.
Physical Therapists Hospital Bed Capacity Cluster Analysis Unsupervised Machine Learning Odds Ratio Organ Dysfunction Scores Personnel Staffing and Scheduling Humans Physicians Retrospective Studies Intensive Care Units Brazil Length of Stay Nurses Time Factors Hospital Mortality Logistic Models
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Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort study.

Journal of translational medicine Oct 1, 2019
BACKGROUND: To develop a machine learning model for predicting acute respiratory distress syndrome (ARDS) events through commonly available parameters, including baseline characteristics and clinical and laboratory parameters.
Humans Machine Learning Algorithms China Cohort Studies ROC Curve Models, Theoretical Respiratory Distress Syndrome Intensive Care Units Female Aged Male Middle Aged
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Leveraging implicit expert knowledge for non-circular machine learning in sepsis prediction.

Artificial intelligence in medicine Sep 24, 2019
Sepsis is the leading cause of death in non-coronary intensive care units. Moreover, a delay of antibiotic treatment of patients with severe sepsis by only few hours is associated with increased mortality. This insight makes accurate models for early...
Diagnosis, Computer-Assisted Surveys and Questionnaires Sepsis Models, Statistical Observer Variation Machine Learning Intensive Care Units Female Male Middle Aged Humans
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Use of machine learning to analyse routinely collected intensive care unit data: a systematic review.

Critical care (London, England) Aug 22, 2019
BACKGROUND: Intensive care units (ICUs) face financial, bed management, and staffing constraints. Detailed data covering all aspects of patients' journeys into and through intensive care are now collected and stored in electronic health records: mach...
Humans Machine Learning Data Analysis Intensive Care Units Electronic Health Records Female Adult Male
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ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU.

Journal of biomedical informatics Aug 17, 2019
To improve the performance of Intensive Care Units (ICUs), the field of bio-statistics has developed scores which try to predict the likelihood of negative outcomes. These help evaluate the effectiveness of treatments and clinical practice, and also ...
Electronic Health Records Reproducibility of Results Sensitivity and Specificity Deep Learning Hospital Mortality ROC Curve Area Under Curve Critical Care Medical Informatics Neural Networks, Computer Machine Learning Humans Algorithms Retrospective Studies Intensive Care Units
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Prevalence of Financial Considerations Documented in Primary Care Encounters as Identified by Natural Language Processing Methods.

JAMA network open Aug 2, 2019
IMPORTANCE: Quantifying patient-physician cost conversations is challenging but important as out-of-pocket spending by US patients increases and patients are increasingly interested in discussing costs with their physicians.
Health Expenditures Ethnicity Income Health Care Costs Cost of Illness Medicaid Prevalence Primary Health Care United States Natural Language Processing Medicare Hospitalization Aged Adult Male Middle Aged Humans Intensive Care Units Female
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Combining patient visual timelines with deep learning to predict mortality.

PloS one Jul 31, 2019
BACKGROUND: Deep learning algorithms have achieved human-equivalent performance in image recognition. However, the majority of clinical data within electronic health records is inherently in a non-image format. Therefore, creating visual representati...
Retrospective Studies Humans Patient Admission Intensive Care Units Male Neural Networks, Computer Electronic Health Records Vital Signs Middle Aged Female Machine Learning Deep Learning Hospitalization Hospital Mortality ROC Curve
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