Speakers

anxiety, breast cancer, cancer, chronic condition, covid-19, covid-19 pandemic, depression (mood), disease, disease management (health), emergency department, food choice, health, institute, intensive care unit, medicaid, medicare (united states), medication, mental health, milken, myocardial infarction, obesity, pain, screening (medicine), social stigma, stroke, technology, telehealth

anxiety, breast cancer, cancer, chronic condition, covid-19, covid-19 pandemic, depression (mood), disease, disease management (health), emergency department, food choice, health, institute, intensive care unit, medicaid, medicare (united states), medication, mental health, milken, myocardial infarction, obesity, pain, screening (medicine), social stigma, stroke, technology, telehealth

anxiety, breast cancer, cancer, chronic condition, covid-19, covid-19 pandemic, depression (mood), disease, disease management (health), emergency department, food choice, health, institute, intensive care unit, medicaid, medicare (united states), medication, mental health, milken, myocardial infarction, obesity, pain, screening (medicine), social stigma, stroke, technology, telehealth

anxiety, breast cancer, cancer, chronic condition, covid-19, covid-19 pandemic, depression (mood), disease, disease management (health), emergency department, food choice, health, institute, intensive care unit, medicaid, medicare (united states), medication, mental health, milken, myocardial infarction, obesity, pain, screening (medicine), social stigma, stroke, technology, telehealth

analytics leadership, ai 2020, analogy, analytics & data science, analytics & research, analytics conference 2020, artificial intelligence, asymptomatic, black lives matter, business analytics, cancer, community, consultant, conversation, covid-19, covid-19 pandemic, covid-19 scratch models, data analytics, data science, data visualization , decision making, decision- making process, decision-making, disease, emergency department, emotion, end user, expert, explanation, feeling, gender, goal, health, hospital, infection, infection prevention and control, information, intensive care unit, isolation (health care), lighting, love, machine learning, machine learning & artificial intelligence, misinformation, percentage, planning and recovery, police, prediction, public health, reason, research, screening (medicine), severe acute respiratory syndrome coronavirus 2, social distancing, social inequality, stretcher, transmission (medicine), twitter, type i and type ii errors, uc center for business analytics, understanding, unemployment, university of cincinnati, vaccine, virtual analytics summit 2020 , visualization, visualization (graphics), yale university

legal access to cannabis, addiction, americans for safe access, asa unity, asa unity 2021, cannabidiol, cannabis, cannabis (drug), cannabis conference 2021, chemistry, doctor of philosophy, epilepsy, food and drug administration, gas, gas chromatography, intensive care medicine, intensive care unit, legislative advocacy, marijuana, marijuana conference 2021, medical cannabis, molecule, national medical cannabis unity conference, opioid, organic chemistry, pain, post-traumatic stress disorder, science, scientist, seizure, social media, suicide, tetrahydrocannabinol, tetrahydrocannabinolic acid, translational medicine, unity2021, veteran

legal access to cannabis, addiction, americans for safe access, asa unity, asa unity 2021, cannabidiol, cannabis, cannabis (drug), cannabis conference 2021, chemistry, doctor of philosophy, epilepsy, food and drug administration, gas, gas chromatography, intensive care medicine, intensive care unit, legislative advocacy, marijuana, marijuana conference 2021, medical cannabis, molecule, national medical cannabis unity conference, opioid, organic chemistry, pain, post-traumatic stress disorder, science, scientist, seizure, social media, suicide, tetrahydrocannabinol, tetrahydrocannabinolic acid, translational medicine, unity2021, veteran

legal access to cannabis, addiction, americans for safe access, asa unity, asa unity 2021, cannabidiol, cannabis, cannabis (drug), cannabis conference 2021, chemistry, doctor of philosophy, epilepsy, food and drug administration, gas, gas chromatography, intensive care medicine, intensive care unit, legislative advocacy, marijuana, marijuana conference 2021, medical cannabis, molecule, national medical cannabis unity conference, opioid, organic chemistry, pain, post-traumatic stress disorder, science, scientist, seizure, social media, suicide, tetrahydrocannabinol, tetrahydrocannabinolic acid, translational medicine, unity2021, veteran

legal access to cannabis, addiction, americans for safe access, asa unity, asa unity 2021, cannabidiol, cannabis, cannabis (drug), cannabis conference 2021, chemistry, doctor of philosophy, epilepsy, food and drug administration, gas, gas chromatography, intensive care medicine, intensive care unit, legislative advocacy, marijuana, marijuana conference 2021, medical cannabis, molecule, national medical cannabis unity conference, opioid, organic chemistry, pain, post-traumatic stress disorder, science, scientist, seizure, social media, suicide, tetrahydrocannabinol, tetrahydrocannabinolic acid, translational medicine, unity2021, veteran

ageing, ai, ai in mumbai, analytics, artificial intelligence, artificialintelligence, automation, big data, bigdata, business, childbirth, chronic condition, code, coding, ct scan, data, datascience, deeplearning, entrepreneur, fintech, fintechs, future, futureofwork, going global, health, health care, health system, hospital, hospital readmission, india, information technology, infrastructure, innovation, intensive care medicine, intensive care unit, iot, machinelearning, ml, mumbai, mumbai ai, nursing, personalized medicine, prediction, prescriptive analytics, programmer, programming, python, radiology, robotics, science, startup, startups, surgery, tech, tech enthusiast, technology, telehealth, therapy, transitional care, womenintech

accuracy and precision, acm 2020, algorithmic bias, artificial intelligence 2020, artificial intelligence in healthcare, artificial neural network, assessment of kidney function, association for computing machinery, asthma, automation bias, bias, bias of an estimator, bronchitis, chronic condition, chronic obstructive pulmonary disease, clinical psychology, clinical trial, comorbidity, computer science, computer science event 2020, dialysis, disparate impact, electronic health record, emergency department, entropy (information theory), fairness (machine learning), generalized entropy index, health economics, health system, hospital readmission, income inequality metrics, information theory, intensive care unit, k-nearest neighbors algorithm, logistic regression, machine learning, mental health, psychological evaluation, radiology, random forest, respiratory disease, robotics, sampling (statistics), sensitivity and specificity, statistical classification, statistics, supervised learning, therapy, wheeze

acm 2020, artificial intelligence 2020, assessment of kidney function, association for computing machinery, bias, chronic condition, clinical psychology, comorbidity, computer science, computer science event 2020, disparate impact, electronic health record, emergency department, entropy (information theory), fairness (machine learning), generalized entropy index, health economics, hospital readmission, income inequality metrics, information theory, intensive care unit, k-nearest neighbors algorithm, mental health, psychological evaluation, sampling (statistics), sensitivity and specificity, statistical classification, statistics, therapy

acm 2020, artificial intelligence 2020, assessment of kidney function, association for computing machinery, bias, big data, chronic condition, clearbanc, clinical psychology, comorbidity, computer science, computer science event 2020, crowdsourcing, disparate impact, electronic health record, emergency department, emerging technologies, entrepreneurship, entropy (information theory), ethics of artificial intelligence, facebook, fairness (machine learning), gender, generalized entropy index, health economics, hospital readmission, immigration, income inequality metrics, information theory, intensive care unit, k-nearest neighbors algorithm, leadership, medicine, mental health, mentorship, neuromorphic engineering, nvidia, privacy, psychological evaluation, research, research and development, robotics, sampling (statistics), sensitivity and specificity, social support, statistical classification, statistics, technology, therapy, transparency (behavior), venture capital, venturebeat

acm 2020, artificial intelligence 2020, assessment of kidney function, association for computing machinery, bias, chronic condition, clinical psychology, comorbidity, computer science, computer science event 2020, disparate impact, electronic health record, emergency department, entropy (information theory), fairness (machine learning), generalized entropy index, health economics, hospital readmission, income inequality metrics, information theory, intensive care unit, k-nearest neighbors algorithm, mental health, psychological evaluation, sampling (statistics), sensitivity and specificity, statistical classification, statistics, therapy

artificial intelligence 2020, data science 2020, acm 2020, artificial intelligence 2020, artificial neural network, assessment of kidney function, association for computing machinery, atom, atomic nucleus, autoencoder, bias, boltzmann distribution, boolean satisfiability problem, chronic condition, clinical psychology, cluster analysis, comorbidity, complexity class, computational complexity theory, computer science, computer science event 2020, conceptual model, copernican heliocentrism, data science, decision problem, deep learning, disparate impact, electrical resistivity and conductivity, electron, electronic health record, emergency department, entropy, entropy (information theory), expert, fairness (machine learning), generalized entropy index, geocentric model, hall effect, health economics, heat, heliocentrism, history of physics, hospital readmission, hydrogen atom, income inequality metrics, information theory, intensive care unit, k-nearest neighbors algorithm, kalman filter, kdd2020 tutorials, kepler's laws of planetary motion, light, machine learning, mechanics, mental health, neural network, newton's law of universal gravitation, nondeterministic turing machine, orbit, p (complexity), physics, psychological evaluation, quantum entanglement, quantum field theory, quantum hall effect, quantum mechanics, quantum state, regularization (mathematics), sampling (statistics), second law of thermodynamics, sensitivity and specificity, statistical classification, statistical mechanics, statistics, supervised learning, therapy, time complexity, unsupervised learning, wave, wave function

artificial intelligence, attention, attention models, cardiovascular disease, complexity, computer vision, deep learning, diabetic retinopathy, educational technology, english language, exercise, facebook, food, google research, health care, health system, heart, hospital, hypertension, information, intensive care unit, language, language processing in the brain, learning, machine learning, machine translation, medicine, memory, myocardial infarction, natural language processing, nature, non-governmental organization, noun, paragraph, patient, pronoun, question, research, sense, sentence (linguistics), short-term memory, speech, stroke, sustainability, tank, technology, translation, visual impairment

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, 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, 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

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

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

analysis, api, autoscaling, bias, bioc2020, bioconductor, bioinformatics, clinic, clinician, computer programming, conference, copy-number variation, data, data science, database, disease, education, false positives and false negatives, feedback, file format, force, gene, genetics, goal, icon (computing), information, intensive care unit, learning, machine, machine learning, memory, mortality rate, mutation, orange line (cta), parameter, patient, pharmacology, phenotype, precision medicine, prediction, r (programming language), regularization (mathematics), reinforcement, research, risk, rna-seq, sequencing, the cancer genome atlas, throughput, tire, training, use case, virtual conference, website, whole genome sequencing, world wide web

bias, clinic, clinician, data, disease, false positives and false negatives, feedback, force, goal, information, intensive care unit, learning, machine, machine learning, memory, mortality rate, orange line (cta), parameter, patient, prediction, regularization (mathematics), reinforcement, risk, throughput, tire

bias, clinic, clinician, data, disease, false positives and false negatives, feedback, force, goal, information, intensive care unit, learning, machine, machine learning, memory, mortality rate, orange line (cta), parameter, patient, prediction, regularization (mathematics), reinforcement, risk, throughput, tire

attention, bouncer (doorman), coffee, data, disease, electronic health record, experiment, expert, flight, glitch, health, hospital, hospital readmission, immortality, information, intensive care unit, international classification of diseases, interpolation, knowledge, knowledge transfer, long short-term memory, losartan, memory, mercury (element), mortality rate, motel, mother, motivation, narrative, patient, prediction, pressure, probability, risk, salt lake city, short-term memory, softball, statistical significance, suffolk, tennessee, text messaging