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
adverse event, bread, child, clinician, experience, false positives and false negatives, hospital, intensive care unit, internal medicine, lyme disease, medicine, palliative care, patient, positive and negative predictive values, sensitivity and specificity, silence, subset, toronto, type i and type ii errors, university of toronto
adverse event, bread, child, clinician, experience, false positives and false negatives, hospital, intensive care unit, internal medicine, lyme disease, medicine, palliative care, patient, positive and negative predictive values, sensitivity and specificity, silence, subset, toronto, type i and type ii errors, university of toronto
Data Science and Advanced Analytics at Unity Health Toronto
adverse event, bread, child, clinician, experience, false positives and false negatives, hospital, intensive care unit, internal medicine, lyme disease, medicine, palliative care, patient, positive and negative predictive values, sensitivity and specificity, silence, subset, toronto, type i and type ii errors, university of toronto
adverse event, bread, child, clinician, experience, false positives and false negatives, hospital, intensive care unit, internal medicine, lyme disease, medicine, palliative care, patient, positive and negative predictive values, sensitivity and specificity, silence, subset, toronto, type i and type ii errors, university of toronto
adverse event, bread, child, clinician, experience, false positives and false negatives, hospital, intensive care unit, internal medicine, lyme disease, medicine, palliative care, patient, positive and negative predictive values, sensitivity and specificity, silence, subset, toronto, type i and type ii errors, university of toronto
Internist and Clinician Scientist at University of Toronto
adverse event, bread, child, clinician, experience, false positives and false negatives, hospital, intensive care unit, internal medicine, lyme disease, medicine, palliative care, patient, positive and negative predictive values, sensitivity and specificity, silence, subset, toronto, type i and type ii errors, university of toronto
adverse event, bread, child, clinician, experience, false positives and false negatives, hospital, intensive care unit, internal medicine, lyme disease, medicine, palliative care, patient, positive and negative predictive values, sensitivity and specificity, silence, subset, toronto, type i and type ii errors, university of toronto
adverse event, bread, child, clinician, experience, false positives and false negatives, hospital, intensive care unit, internal medicine, lyme disease, medicine, palliative care, patient, positive and negative predictive values, sensitivity and specificity, silence, subset, toronto, type i and type ii errors, university of toronto
adverse event, bias, bread, child, clinic, clinician, data, disease, experience, false positives and false negatives, feedback, force, goal, hospital, information, intensive care unit, internal medicine, learning, lyme disease, machine, machine learning, medicine, memory, mortality rate, orange line (cta), palliative care, parameter, patient, positive and negative predictive values, prediction, regularization (mathematics), reinforcement, risk, sensitivity and specificity, silence, subset, throughput, tire, toronto, type i and type ii errors, university of toronto
Professor of Computer Science at Harvard / Radcliffe Institute for Advanced Study
accuracy and precision, analysis, applied and computational geometry, applied mathematics, bias, causality, census, computer programming, computer science, computing, data analysis, data science, decision-making, differential privacy, economics, expected value, experiment, facebook, false positives and false negatives, intelligence analysis, internet privacy, logarithm, machine learning, math, mathematics, mind, odds, overfitting, positive and negative predictive values, prime number, probability distribution, random walk, randomness, research, sampling (statistics), simulation and modeling, standard deviation, statistical classification, statistics, student financial aid (united states), system, tax, total information awareness, total variation, truth, variance
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