attention, autocorrelation, causality, data, exercise, experiment, eye, false positives and false negatives, graph (discrete mathematics), information, intersectionality, london, moon, mutual information, polymerase chain reaction, power of a test, reliability (statistics), sample size determination, sepsis, statistic, statistical significance, test statistic, time series, truth, type i and type ii errors

Pediatric Hospital Medicine Fellow at Johns Hopkins All Children’s Hospital

attention, breathing, communication, deep learning, electronic health record, hospital, hospital medicine, hospital readmission, information, intuition, johns hopkins all children's hospital, learning, machine learning, medical record, neural network, patient, pediatrics, point of care, positive and negative predictive values, predictive modelling, risk, risk assessment, sample size determination, sensitivity and specificity, truth

Physician at Johns Hopkins All Children's Hospital

attention, breathing, communication, deep learning, electronic health record, hospital, hospital medicine, hospital readmission, information, intuition, johns hopkins all children's hospital, learning, machine learning, medical record, neural network, patient, pediatrics, point of care, positive and negative predictive values, predictive modelling, risk, risk assessment, sample size determination, sensitivity and specificity, truth

Assistant Professor of Pediatrics at The Johns Hopkins University School of Medicine

attention, breathing, communication, deep learning, electronic health record, hospital, hospital medicine, hospital readmission, information, intuition, johns hopkins all children's hospital, learning, machine learning, medical record, neural network, patient, pediatrics, point of care, positive and negative predictive values, predictive modelling, risk, risk assessment, sample size determination, sensitivity and specificity, truth

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