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Brittany Casey
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
Paola Dees
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
John Morrison
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
Mohamed Rehman
Eric Kobren Professor of Applied Health Informatics at The Johns Hopkins University School of Medicine
analytics, anesthesia, attention, bayesian inference, breathing, census, child, communication, decision tree, deep learning, demography, disability, electronic health record, health care, hospital, hospital medicine, hospital readmission, information, intuition, johns hopkins all children's hospital, learning, machine learning, medical history, medical record, music, neural network, neurology, patient, pediatrics, perioperative, point of care, positive and negative predictive values, prediction, predictive modelling, risk, risk assessment, sample size determination, sensitivity and specificity, surgery, truth, visual analytics
Luis Ahumada
ДолжностьDirector Center for Pediatric Data Science and Analytic Methodology at Johns Hopkins All Children's Hospital
analytics, anesthesia, attention, bayesian inference, breathing, census, child, communication, decision tree, deep learning, demography, disability, electronic health record, health care, hospital, hospital medicine, hospital readmission, information, intuition, johns hopkins all children's hospital, learning, machine learning, medical history, medical record, music, neural network, neurology, patient, pediatrics, perioperative, point of care, positive and negative predictive values, prediction, predictive modelling, risk, risk assessment, sample size determination, sensitivity and specificity, surgery, truth, visual analytics
Ali Jalali
Senior Data Scientist at Biofourmis
analytics, anesthesia, attention, bayesian inference, breathing, census, child, communication, decision tree, deep learning, demography, disability, electronic health record, health care, hospital, hospital medicine, hospital readmission, information, intuition, johns hopkins all children's hospital, learning, machine learning, medical history, medical record, music, neural network, neurology, patient, pediatrics, perioperative, point of care, positive and negative predictive values, prediction, predictive modelling, risk, risk assessment, sample size determination, sensitivity and specificity, surgery, truth, visual analytics
Hannah Lonsdale
Clinical Research Associate at The Johns Hopkins University
analytics, anesthesia, attention, bayesian inference, breathing, census, child, communication, decision tree, deep learning, demography, disability, electronic health record, health care, hospital, hospital medicine, hospital readmission, information, intuition, johns hopkins all children's hospital, learning, machine learning, medical history, medical record, music, neural network, neurology, patient, pediatrics, perioperative, point of care, positive and negative predictive values, prediction, predictive modelling, risk, risk assessment, sample size determination, sensitivity and specificity, surgery, truth, visual analytics
Muhammad Mamdani
Director at St. Michael's Hospital
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
David Dai
Data Scientist at St. Michael's Hospital
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
Sebnem Kuzulugil
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
Joshua Murray
Director, Advanced Analytics at MSc
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
Chloe Pou-Prom
Sessional Lecturer 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
Amol Verma
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
Liam McCoy
Junior Fellow at Massey College
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
Bret Nestor
PHD Researcher at Vector Institute
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
Marzyeh Ghassemi
Assistant Professor at University of Toronto
accuracy and precision, active learning, adolescence, adverse event, ai, algorithmic bias, almond, arctic monkeys, artificial intelligence, artificial intelligence in healthcare, artificial neural network, asthma, automation bias, average, bias, bias of an estimator, big data, big data in precision health, brain, bread, bronchitis, child, chronic condition, chronic obstructive pulmonary disease, clinical trial, clinician, computer vision, convolutional neural network, craigslist, data mining, data quality, dialysis, disease, ecology, electronic health record, emergency department, end-of-life care, epidemiology, ethics, experience, expert, fairness (machine learning), false positives and false negatives, fei-fei li, funny people, generative model, gianni versace, gold standard (test), gregory house, hawaii, health, health care, health system, hospital, imagenet, inductive reasoning, informatics, information, informed consent, intelligence, intensive care unit, interdisciplinarity, internal medicine, learning, lexus, logistic regression, long short-term memory, lyme disease, machine learning, marzyeh ghassemi, matrix (mathematics), medical diagnosis, medical privacy, medical record, medicine, mental disorder, mental health, natural language processing, neural network, new york (state), oneonta, new york, palliative care, patient, pediatrics, positive and negative predictive values, precedent, precision health, precision medicine, prediction, privacy, property, radiology, rain, random forest, randomized controlled trial, receiver operating characteristic, recurrent neural network, refrigerant, research, respiratory disease, risk, robotics, science, self-report study, sensitivity and specificity, sepsis, silence, smartphone, stanford, stanford medicine, statistics, subset, supervised learning, surgery, synthetic data, system, technology, temecula, california, toronto, type i and type ii errors, understanding, university of toronto, use case, wheeze, youtube
Anna Goldenberg
Scientist at The Hospital for Sick Children
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
Cynthia Dwork
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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