Chien-Ming Huang, Ph.D
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Clustering Longitudinal Clinical Marker Trajectories from Electronic Health Data: Applications to Phenotyping and Endotype Discovery
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence Clustering Longitudinal Clinical Marker Trajectories from Electronic Health Data: Applications to Phenotyping and Endotype Discovery Peter Schulam Fredrick Wigley Suchi Saria Department of Computer Science Division of Rheumatology Department of Computer Science Johns Hopkins University Johns Hopkins School of Medicine Department of Health Policy & Mgmt. 3400 N. Charles St. 733. N. Broadway Johns Hopkins University Baltimore, MD 21218 Baltimore, MD 21205 3400 N. Charles St. [email protected] [email protected] Baltimore, MD 21218 [email protected] Abstract more thorough retrospective or prospective study to confirm their existence (e.g. Barr et al. 1999). Recently, however, Diseases such as autism, cardiovascular disease, and the au- literature in the medical community has noted the need for toimmune disorders are difficult to treat because of the re- markable degree of variation among affected individuals. more objective methods for discovering subtypes (De Keu- Subtyping research seeks to refine the definition of such com- lenaer and Brutsaert 2009). Growing repositories of health plex, multi-organ diseases by identifying homogeneous pa- data stored in electronic health record (EHR) databases tient subgroups. In this paper, we propose the Probabilis- and patient registries (Blumenthal 2009; Shea and Hripc- tic Subtyping Model (PSM) to identify subgroups based on sak 2010) present an exciting opportunity to identify disease clustering individual clinical severity markers. This task is subtypes in an objective, data-driven manner using tools challenging due to the presence of nuisance variability— from machine learning that can help to tackle the problem variations in measurements that are not due to disease of combing through these massive databases. -
Artificial Intelligence in Health Care: the Hope, the Hype, the Promise, the Peril
Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril Michael Matheny, Sonoo Thadaney Israni, Mahnoor Ahmed, and Danielle Whicher, Editors WASHINGTON, DC NAM.EDU PREPUBLICATION COPY - Uncorrected Proofs NATIONAL ACADEMY OF MEDICINE • 500 Fifth Street, NW • WASHINGTON, DC 20001 NOTICE: This publication has undergone peer review according to procedures established by the National Academy of Medicine (NAM). Publication by the NAM worthy of public attention, but does not constitute endorsement of conclusions and recommendationssignifies that it is the by productthe NAM. of The a carefully views presented considered in processthis publication and is a contributionare those of individual contributors and do not represent formal consensus positions of the authors’ organizations; the NAM; or the National Academies of Sciences, Engineering, and Medicine. Library of Congress Cataloging-in-Publication Data to Come Copyright 2019 by the National Academy of Sciences. All rights reserved. Printed in the United States of America. Suggested citation: Matheny, M., S. Thadaney Israni, M. Ahmed, and D. Whicher, Editors. 2019. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. NAM Special Publication. Washington, DC: National Academy of Medicine. PREPUBLICATION COPY - Uncorrected Proofs “Knowing is not enough; we must apply. Willing is not enough; we must do.” --GOETHE PREPUBLICATION COPY - Uncorrected Proofs ABOUT THE NATIONAL ACADEMY OF MEDICINE The National Academy of Medicine is one of three Academies constituting the Nation- al Academies of Sciences, Engineering, and Medicine (the National Academies). The Na- tional Academies provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. -
100 #Ai Influencers Worth Following – Pete Trainor – Medium 8/23/18, 12 12
100 #Ai Influencers Worth Following – Pete Trainor – Medium 8/23/18, 1212 Pete Trainor Follow Husband. Dad. Author @thehippobook. Co-Founder @talktousai. Human-Focused Designer. @BIMA #Ai Chair. Mens #mentalhealth campaigner #SaveTheMale #EghtEB Aug 1 · 12 min read 100 #Ai In)uencers Worth Following Over the last month or two, I’ve put my head together with the brilliant members of the BIMA Ai Think Tank, to bring you a list of the 100 people we think are shaping the conversation around Ai. For context, we’ve chose the people who are right at the coalface… working in it, debating it, researching it, and people who haven’t been afraid to start their own companies to drive the agenda of Ai forward. 53 amazing women, and 47 astonishing men… all trying to make the world a new place using this most divisive technology. It’s also worth noting that some people will call out names of people who are notably absence from the list. We debated the merits of the list a lot, and removed some names because we felt they were either ‘negative’ inMuencers to the topic, or pop-scientists jumping on the band-wagon. So here we go, in alphabetical order; Adam Geitgey — @ageitgey — Adam is a Software Engineer, consultant and writer of the Machine Learning is Fun blog. He also creates courses for Lynda.com. Adelyn Zhou — @adelynzhou — Adelyn is a recognized top inMuencer in marketing and artiWcial intelligence. She is a speaker, author and leader in the space. Aimee van Wynsberghe — @aimeevanrobot Alex Champandard — @alexjc Alex Smola — @smolix https://medium.com/@petetrainor/100-ai-influencers-worth-following-7a1f1ce7c18b Page 1 of 12 100 #Ai Influencers Worth Following – Pete Trainor – Medium 8/23/18, 1212 Amber Osborne — @missdestructo — Amber is the co-founder and CMO at MeshWre, an Ai-powered social media management platform. -
Clairvoyance: Apipeline Toolkit for Medical Time Series
Published as a conference paper at ICLR 2021 CLAIRVOYANCE: APIPELINE TOOLKIT FOR MEDICAL TIME SERIES Daniel Jarrett⇤ Jinsung Yoon⇤ University of Cambridge, UK Google Cloud AI, Sunnyvale, USA [email protected] University of California, Los Angeles, USA [email protected] Ioana Bica University of Oxford, UK Zhaozhi Qian The Alan Turing Institute, UK University of Cambridge, UK [email protected] [email protected] Ari Ercole Mihaela van der Schaar University of Cambridge, UK University of Cambridge, UK Cambridge University Hospitals NHS Foun- University of California, Los Angeles, USA dation Trust, UK The Alan Turing Institute, UK [email protected] [email protected] ABSTRACT Time-series learning is the bread and butter of data-driven clinical decision support, and the recent explosion in ML research has demonstrated great potential in various healthcare settings. At the same time, medical time-series problems in the wild are challenging due to their highly composite nature: They entail design choices and interactions among components that preprocess data, impute missing values, select features, issue predictions, estimate uncertainty, and interpret models. Despite exponential growth in electronic patient data, there is a remarkable gap between the potential and realized utilization of ML for clinical research and decision support. In particular, orchestrating a real-world project lifecycle poses challenges in engi- neering (i.e. hard to build), evaluation (i.e. hard to assess), and efficiency (i.e. hard to optimize). Designed to address these issues simultaneously, Clairvoyance pro- poses a unified, end-to-end, autoML-friendly pipeline that serves as a (i) software toolkit, (ii) empirical standard, and (iii) interface for optimization. -
Top 100 AI Leaders in Drug Discovery and Advanced Healthcare Introduction
Top 100 AI Leaders in Drug Discovery and Advanced Healthcare www.dka.global Introduction Over the last several years, the pharmaceutical and healthcare organizations have developed a strong interest toward applying artificial intelligence (AI) in various areas, ranging from medical image analysis and elaboration of electronic health records (EHRs) to more basic research like building disease ontologies, preclinical drug discovery, and clinical trials. The demand for the ML/AI technologies, as well as for ML/AI talent, is growing in pharmaceutical and healthcare industries and driving the formation of a new interdisciplinary industry (‘data-driven healthcare’). Consequently, there is a growing number of AI-driven startups and emerging companies offering technology solutions for drug discovery and healthcare. Another important source of advanced expertise in AI for drug discovery and healthcare comes from top technology corporations (Google, Microsoft, Tencent, etc), which are increasingly focusing on applying their technological resources for tackling health-related challenges, or providing technology platforms on rent bases for conducting research analytics by life science professionals. Some of the leading pharmaceutical giants, like GSK, AstraZeneca, Pfizer and Novartis, are already making steps towards aligning their internal research workflows and development strategies to start embracing AI-driven digital transformation at scale. However, the pharmaceutical industry at large is still lagging behind in adopting AI, compared to more traditional consumer industries -- finance, retail etc. The above three main forces are driving the growth in the AI implementation in pharmaceutical and advanced healthcare research, but the overall success depends strongly on the availability of highly skilled interdisciplinary leaders, able to innovate, organize and guide in this direction. -
Preparing for the Future of Artificial Intelligence
PREPARING FOR THE FUTURE OF ARTIFICIAL INTELLIGENCE Executive Office of the President National Science and Technology Council National Science and Technology Council Committee on Technology October 2016 About the National Science and Technology Council The National Science and Technology Council (NSTC) is the principal means by which the Executive Branch coordinates science and technology policy across the diverse entities that make up the Federal research and development (R&D) enterprise. One of the NSTC’s primary objectives is establishing clear national goals for Federal science and technology investments. The NSTC prepares R&D packages aimed at accomplishing multiple national goals. The NSTC’s work is organized under five committees: Environment, Natural Resources, and Sustainability; Homeland and National Security; Science, Technology, Engineering, and Mathematics (STEM) Education; Science; and Technology. Each of these committees oversees subcommittees and working groups that are focused on different aspects of science and technology. More information is available at www.whitehouse.gov/ostp/nstc. About the Office of Science and Technology Policy The Office of Science and Technology Policy (OSTP) was established by the National Science and Technology Policy, Organization, and Priorities Act of 1976. OSTP’s responsibilities include advising the President in policy formulation and budget development on questions in which science and technology are important elements; articulating the President’s science and technology policy and programs; and fostering strong partnerships among Federal, state, and local governments, and the scientific communities in industry and academia. The Director of OSTP also serves as Assistant to the President for Science and Technology and manages the NSTC. More information is available at www.whitehouse.gov/ostp. -
Suchi Saria, Phd Assistant Professor of Computer Science, Statistics, and Health Policy, Johns Hopkins University
Biostatistics Seminars Winter 2017 Suchi Saria, PhD Assistant Professor of Computer Science, Statistics, and Health Policy, Johns Hopkins University Scalable Joint Models for Reliable Event Prediction: Application to Monitoring Adverse Events using Electronic Health Record Data Tuesday, 7 February 2017 3:30 pm – 4:30 pm - Purvis Hall, 1020 Pine Ave. West, Room 24 ALL ARE WELCOME Abstract: Many life-threatening adverse events such as sepsis and cardiac arrest are treatable if detected early. Towards this, one can leverage the vast number of longitudinal signals---e.g., repeated heart rate, respiratory rate, blood cell counts, creatinine measurements---that are already recorded by clinicians to track an individual's health. Motivated by this problem, we propose a reliable event prediction framework comprising two key innovations. First, we extend existing state-of-the-art in joint-modeling to tackle settings with large-scale, (potentially) correlated, high-dimensional multivariate longitudinal data. For this, we propose a flexible Bayesian nonparametric joint model along with scalable stochastic variational inference techniques for estimation. Second, we use a decision-theoretic approach to derive an optimal detector that trades-off the cost of delaying correct adverse-event detections against making incorrect assessments. On a challenging clinical dataset on patients admitted to an Intensive Care Unit, we see significant gains in early event-detection performance over state-of-the-art techniques. Bio: Suchi Saria is an assistant professor of computer science, (bio)statistics, and health policy at Johns Hopkins University. Her research interests are in statistical machine learning and "precision" healthcare. Specifically, her focus is in designing novel data-driven computing tools for optimizing care delivery. -
Reliable Decision Support Using Counterfactual Models
Reliable Decision Support using Counterfactual Models Peter Schulam Suchi Saria Department of Computer Science Department of Computer Science Johns Hopkins University Johns Hopkins University Baltimore, MD 21211 Baltimore, MD 21211 [email protected] [email protected] Abstract Decision-makers are faced with the challenge of estimating what is likely to happen when they take an action. For instance, if I choose not to treat this patient, are they likely to die? Practitioners commonly use supervised learning algorithms to fit predictive models that help decision-makers reason about likely future outcomes, but we show that this approach is unreliable, and sometimes even dangerous. The key issue is that supervised learning algorithms are highly sensitive to the policy used to choose actions in the training data, which causes the model to capture relationships that do not generalize. We propose using a different learning objective that predicts counterfactuals instead of predicting outcomes under an existing action policy as in supervised learning. To support decision-making in temporal settings, we introduce the Counterfactual Gaussian Process (CGP) to predict the counterfactual future progression of continuous-time trajectories under sequences of future actions. We demonstrate the benefits of the CGP on two important decision-support tasks: risk prediction and “what if?” reasoning for individualized treatment planning. 1 Introduction Decision-makers are faced with the challenge of estimating what is likely to happen when they take an action. One use of such an estimate is to evaluate risk; e.g. is this patient likely to die if I do not intervene? Another use is to perform “what if?” reasoning by comparing outcomes under alternative actions; e.g. -
AAAI-15 / IAAI-15 Conference Program January 25 – 30, 2015 Hyatt Regency Austin, Austin, Texas, USA
Twenty-Ninth AAAI Conference on Artificial Intelligence Twenty-Seventh Conference on Innovative Applications of Artificial Intelligence AAAI-15 / IAAI-15 Conference Program January 25 – 30, 2015 Hyatt Regency Austin, Austin, Texas, USA Sponsored by the Association for the Advancement of Artificial Intelligence Cosponsored by the National Science Foundation, AI Journal, Baidu, Infosys, Microsoft Research, BigML, Disney Research, Google, NSERC Canadian Field Robotics Network, University of Southern California/Information Sciences Institute, Yahoo Labs!, ACM/SIGAI, CRA Computing Community Consortium, and David E. Smith In cooperation with the University of Texas at Austin Computer Science Department, IEEE Robotics and Automation Society, and the RoboCup Federation MORNING AFTERNOON EVENING Sunday, January 25 Student Newcomer Lunch Tutorial Forum Tutorial Forum Workshops / NSF Workshop Workshops / NSF Workshop AAAI/SIGAI DC AAAI/SIGAI DC Monday, January 26 Tutorial Forum Tutorial Forum Speed Dating Workshops Workshops Opening Reception AAAI/SIGAI DC AAAI/SIGAI DC Open House Open House Invited Talks Robotics / RoboCup Robotics / RoboCup Tuesday, January 27 AAAI / IAAI Welcome / AAAI Awards Invited Talks: Bagnell and Etzioni Shaky Celebration AAAI / IAAI Technical Program AAAI / IAAI Technical Program Poster / Demo Session 1 Funding Information Session Senior Member Blue Sky / Doctoral Consortium, What’s Hot Talks / RSS Talks Virtual Agent Demos Robotics / RoboCup / Exhibits Robotics / RoboCup / Exhibits Robotics Demos General Game Playing -
Ssuchi Saria ’04 SUCHI SARIA, CLASS of 2004, the Alumnae Association of Mount Holyoke College Is Pleased to Honor You with the Mary Lyon Award
ALUMNAE ASSOCIATION OF MOUNT HOLYOKE COLLEGE Mary Lyon Award 2018 PRESENTED TO SSuchi Saria ’04 SUCHI SARIA, CLASS OF 2004, the Alumnae Association of Mount Holyoke College is pleased to honor you with the Mary Lyon Award. This award is presented to an alumna who graduated no more than fifteen years ago and who has demonstrated sustained achievements in her life and career consistent with the humane values that Mary Lyon exemplified and inspired in others. Suchi, as a widely recognized academic scholar and scientist, your exceptional creativity, expertise, and passion have helped put forth major medical advancements in health care in the United States and around the world. Upon graduation from Mount Holyoke, you completed your doctorate at Stanford University. You received a National Science Foundation Computing Innovation Award, one of only seventeen awarded nationally, to join Harvard University as a National Science Foundation computing fellow. From there, you moved to your current position as assistant professor in the computer science department at Johns Hopkins University, where your innovative research has led to an early warning system used in hospitals that can accurately predict which patients may fall victim to septic shock, a condition that kills nearly 200,000 people annually. Your work has not only directly improved patient outcomes but has positively impacted the delivery of health care around the world. You have published numerous scientific papers focused on health informatics and machine learning, including two feature articles in Science Translational Medicine. One of these articles, published in 2010, presented your methodology for predicting the severity of future illness in preterm infants. -
Towards AI-Assisted Healthcare: System Design and Deployment for Machine Learning Based Clinical Decision Support Andong Zhan
Towards AI-assisted Healthcare: System Design and Deployment for Machine Learning based Clinical Decision Support by Andong Zhan A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy. Baltimore, Maryland March, 2018 c Andong Zhan 2018 All rights reserved Abstract Over the last decade, American hospitals have adopted electronic health records (EHRs) widely. In the next decade, incorporating EHRs with clinical decision sup- port (CDS) together into the process of medicine has the potential to change the way medicine has been practiced and advance the quality of patient care. It is a unique opportunity for machine learning (ML), with its ability to process massive datasets beyond the scope of human capability, to provide new clinical insights that aid physicians in planning and delivering care, ultimately leading to better outcomes, lower costs of care, and increased patient satisfaction. However, applying ML-based CDS has to face steep system and application challenges. No open platform is there to support ML and domain experts to develop, deploy, and monitor ML-based CDS; and no end-to-end solution is available for machine learning algorithms to consume heterogenous EHRs and deliver CDS in real-time. Build ML-based CDS from scratch can be expensive and time-consuming. In this dissertation, CDS-Stack, an open cloud-based platform, is introduced to help ML practitioners to deploy ML-based CDS into healthcare practice. The CDS- ii ABSTRACT Stack integrates various components into the infrastructure for the development, deployment, and monitoring of the ML-based CDS. It provides an ETL engine to transform heterogenous EHRs, either historical or online, into a common data model (CDM) in parallel so that ML algorithms can directly consume health data for train- ing or prediction. -
Using Machine Learning for Early Prediction of Cardiogenic Shock in Patients with Acute Heart Failure
Using Machine Learning for Early Prediction of Cardiogenic Shock in Patients with Acute Heart Failure Faisal Rahman ( [email protected] ) Johns Hopkins University Noam Finkelstein Johns Hopkins University Anton Alyakin Johns Hopkins University Nisha Gilotra Johns Hopkins University Jeff Trost Johns Hopkins University Steven Schulman Johns Hopkins University Suchi Saria Johns Hopkins University Research Article Keywords: acute decompensated heart failure, cardiogenic shock, machine learning, risk prediction, Posted Date: April 27th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-453102/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/18 Abstract Objective: Despite technological and treatment advancements over the past two decades, cardiogenic shock (CS) mortality has remained between 40-60%. A number of factors can lead to delayed diagnosis of CS, including gradual onset and nonspecic symptoms. Our objective was to develop an algorithm that can continuously monitor heart failure patients, and partition them into cohorts of high- and low-risk for CS. Methods: We retrospectively studied 24,461 patients hospitalized with acute decompensated heart failure, 265 of whom developed CS, in the Johns Hopkins Healthcare system. Our cohort identication approach is based on logistic regression, and makes use of vital signs, lab values, and medication administrations recorded during the normal course of care. Results: Our algorithm identied patients at high-risk of CS. Patients in the high-risk cohort had 10.2 times (95% condence interval 6.1-17.2) higher prevalence of CS than those in the low-risk cohort. Patients who experienced cardiogenic shock while in the high-risk cohort were rst deemed high-risk a median of 1.7 days (interquartile range 0.8 to 4.6) before cardiogenic shock diagnosis was made by their clinical team.