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
4dx, amazon (company), apache kafka, api, apple inc., application firewall, application for employment, application programming interface, application software, architecture, banks, blubaugh, brownfield land, business transformation, cat power, central processing unit, chief technology officer, client (computing), cloud computing, cognition, community, computer hardware, computer monitor, computer network, computer performance, computing, confidence, consumer, control plane, cream, culture, cumin, customer, data, data center, data model, database, dayton, ohio, digital transformation, distributed computing, electricity, emotion, encryption, engineer, enterprise, exception handling, firewall (computing), food, frog, function as a service, gateway (telecommunications), glee (tv series), google, grafana, greenfield project, guidebook, ice, ice cream, indenture, information, infrastructure, integration, internet of things, java (programming language), joe, kong, kubernetes , language, latency (engineering), leadership, lightstep, lincoln, england, linkedin, logging, login, machine, mainstream top 40, marco, marketplace, matrix (mathematics), matter, mesh networking, microservices, mind, mobile app, modern architecture, monolith, motion, motorcycle, naproxen, new world, nuclear option, observable, onboarding, open source, open-source model, open-source software, open-source-software movement, operating system, organization, outsourcing, overtime, palladino, personal computer, pete, plug-in (computing), police, pricing, programming language, prophecy, proxy pattern, proxy server, reason, reliability engineering, republic of ireland, reserved word, risk, router (computing), routing, scalability, security, server (computing), sidecar, software , software framework, speed, stack (abstract data type), standing on the shoulders of giants, steam (service), street, subroutine, subset, surveillance, system, technology, the den, time, traffic sign, transport layer security, tree, uncertainty, understanding, velocity, virtual machine, web application, web application firewall, world, world wide web
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