- 142
- 89 905
Dept. Biomedical Informatics Columbia University
United States
Приєднався 6 жов 2016
Located on the Columbia University Medical Center (CUIMC) campus, the Department of Biomedical Informatics (DBMI) is both an academic department and an information services partner to NewYork-Presbyterian Hospital, a major healthcare provider in greater New York.
One of the oldest informatics departments in the nation, faculty and students at DBMI have set the path for design of clinical information systems, methodologies in clinical natural language processing, and machine learning over electronic health record data. Faculty research includes the development and evaluation of innovative information technologies, which has led to enhancements in both health and healthcare.
Both faculty and students work in a highly collaborative environment, applying informatics from the atomic level to global populations.
For more information, please visit www.dbmi.columbia.edu/.
One of the oldest informatics departments in the nation, faculty and students at DBMI have set the path for design of clinical information systems, methodologies in clinical natural language processing, and machine learning over electronic health record data. Faculty research includes the development and evaluation of innovative information technologies, which has led to enhancements in both health and healthcare.
Both faculty and students work in a highly collaborative environment, applying informatics from the atomic level to global populations.
For more information, please visit www.dbmi.columbia.edu/.
The role of genetic evidence to improve productivity in drug discovery & development (Nelson, 10/28)
Title: The role of genetic evidence to improve productivity in drug discovery and development
Presenter: Matt Nelson, Chief Executive Officer of Genscience
Abstract: The cost of drug discovery and development is driven primarily by failure, with just ~10% of clinical programs eventually receiving approval. The most important step in a successful drug discovery and development program is selecting the drug mechanism, usually in the form of a target, for the intended patient population. We previously estimated that human genetic evidence doubles the success rate from clinical development to approval. We have expanded on this work leveraging the growth in genetic evidence over the past decade to better understand the characteristics that distinguish clinical success and failure. We estimate the probability of success for drug mechanisms with genetic support is 2.6 times greater than those without. This relative success varies among therapy areas and development phases, and improves with increasing confidence in the causal gene, but is largely unaffected by genetic effect size, minor allele frequency, or year of discovery. We further demonstrate the value genetics can play in anticipating potential on-target side effects to predict and mitigate those risks early in the development process. These results suggest we are far from reaching peak genetic insights to aid the discovery of targets for more effective drugs.
Bio: Matthew Nelson, Ph.D., is Chief Executive Officer of Genscience, a tech-focused company to improve integration of genetic evidence into drug discovery. Genscience is an affiliate of Deerfield, which Dr. Nelson joined as VP, Genetics & Genomics in 2019. Prior to Deerfield, Dr. Nelson spent almost 15 years at GlaxoSmithKline and was most recently the Head of Genetics. Prior to GlaxoSmithKline, Dr. Nelson was the Director of Biostatistics at Sequenom and Director of Genomcis at Esperion Therapeutics. He is co-author on 80+ publications. Dr. Nelson was an Adjunct Associate Professor of Biostatistics at the University of North Carolina from 2010 to 2016. He holds a Ph.D. in Human Genetics and an M.A. in Statistics from the University of Michigan.
Presenter: Matt Nelson, Chief Executive Officer of Genscience
Abstract: The cost of drug discovery and development is driven primarily by failure, with just ~10% of clinical programs eventually receiving approval. The most important step in a successful drug discovery and development program is selecting the drug mechanism, usually in the form of a target, for the intended patient population. We previously estimated that human genetic evidence doubles the success rate from clinical development to approval. We have expanded on this work leveraging the growth in genetic evidence over the past decade to better understand the characteristics that distinguish clinical success and failure. We estimate the probability of success for drug mechanisms with genetic support is 2.6 times greater than those without. This relative success varies among therapy areas and development phases, and improves with increasing confidence in the causal gene, but is largely unaffected by genetic effect size, minor allele frequency, or year of discovery. We further demonstrate the value genetics can play in anticipating potential on-target side effects to predict and mitigate those risks early in the development process. These results suggest we are far from reaching peak genetic insights to aid the discovery of targets for more effective drugs.
Bio: Matthew Nelson, Ph.D., is Chief Executive Officer of Genscience, a tech-focused company to improve integration of genetic evidence into drug discovery. Genscience is an affiliate of Deerfield, which Dr. Nelson joined as VP, Genetics & Genomics in 2019. Prior to Deerfield, Dr. Nelson spent almost 15 years at GlaxoSmithKline and was most recently the Head of Genetics. Prior to GlaxoSmithKline, Dr. Nelson was the Director of Biostatistics at Sequenom and Director of Genomcis at Esperion Therapeutics. He is co-author on 80+ publications. Dr. Nelson was an Adjunct Associate Professor of Biostatistics at the University of North Carolina from 2010 to 2016. He holds a Ph.D. in Human Genetics and an M.A. in Statistics from the University of Michigan.
Переглядів: 94
Відео
Navigating AI in Medicine: Opportunities & Risks of Large Language Models in Real-World Tasks (Z Lu)
Переглядів 31021 день тому
Title: Navigating AI in Medicine: Opportunities and Risks of Large Language Models in Real-World Tasks Presenter: Zhiyong Lu, Senior Investigator, NIH/NLM Abstract: The explosion of biomedical big data and information in the past decade or so has created new opportunities for discoveries to improve the treatment and prevention of human diseases. As such, the field of medicine is undergoing a pa...
Patients and Clinicians at the heart of health innovation: OpenNotes Lab and Cornell Tech ...
Переглядів 5 тис.Місяць тому
Title: Patients and Clinicians at the heart of health innovation: OpenNotes Lab and Cornell Tech Health Tech Hub Presenter: Chethan Sarabu, Clinical Assistant Professor, Pediatrics, Stanford University Bio: Chethan Sarabu MD, FAAP, FAMIA, trained in landscape architecture, pediatrics, and clinical informatics, builds bridges across these fields to design healthier environments and systems. He i...
GWAS in the age of AI (Degui Zhi)
Переглядів 2,7 тис.Місяць тому
Title: GWAS in the age of AI Presenter: Degui Zhi, Professor and Chair, Department of Bioinformatics and Systems Medicine, UTHealth Houston Abstract: While genome-wide association studies (GWAS) have fueled the amazing genetic discovery in the past 15 years or so, most existing studies were using traditional phenotypes. With deep learning-based AI, it is possible to generate many new phenotypes...
Reflections on AI in (NYC) government (Neal Parikh, Sept. 16 Seminar)
Переглядів 370Місяць тому
Title: Reflections on AI in (NYC) government Presenter: Neal Parikh, Director of AI for New York City Abstract: AI and machine learning have emerged as increasingly ubiquitous technologies in a wide range of areas in both the private sector and in government. In the past several years, ethical and other policy and governance questions around how and whether to use AI for various tasks have beco...
Prediction of non emergent acute care utilization and cost among patients receiving Medicaid
Переглядів 1066 місяців тому
Title: Prediction of non emergent acute care utilization and cost among patients receiving Medicaid Presenter: Sadiq Patel, Data Science Team Lead, Waymark; Adjunct Professor, University of Pennsylvania Abstract: Patients receiving Medicaid often experience social risk factors for poor health and limited access to primary care, leading to high utilization of emergency departments and hospitals ...
Temporal Relation Extraction from EMR Clinical Text (and Beyond) (Geurgana Savova)
Переглядів 1256 місяців тому
Title: Temporal Relation Extraction from EMR Clinical Text (and Beyond) Presenter: Geurgana Savova, Professor and Patricia F. Brennan Chair in the Computational Health Informatics Program (CHIP) at Boston Children’s Hospital and Harvard Medical School Abstract: This talk will focus on the definition of the task of temporal relation extraction from the clinical narrative and computational method...
Who's Tool is it Anyway: Engaging Clinicians and Patients as Experts when Creating Novel ...
Переглядів 897 місяців тому
Title: Who's Tool is it Anyway: Engaging Clinicians and Patients as Experts when Creating Novel Healthcare Technologies Presenter: Megan Hofmann, PhD, Assistant Professor, Khoury College of Computer Sciences, Northeastern University Abstract: As more complex computing tools work their way into healthcare practice, clinicians and patients become reliant on black boxes that determine their outcom...
Straying from the Path of Totality: Issues Requiring Illumination for Health AI’s Next Phase
Переглядів 6137 місяців тому
Title: Straying from the Path of Totality: Issues Requiring Illumination for Health AI’s Next Phase Presenter: Laurie Lovett Novak, Associate Professor, Department of Biomedical Informatics, Vanderbilt University Medical Center Abstract: On the day of the 2024 eclipse, Dr. Novak will discuss societal challenges presented by Health AI and its rapid pace of progress. Public engagement, the role o...
Integrating the healthcare ecosystem with algorithm development for accelerated impact
Переглядів 9 тис.7 місяців тому
Title: Integrating the healthcare ecosystem with algorithm development for accelerated impact Presenter: Gustavo Stolovitzky, PhD, Adjunct Associate Professor of Systems Biology and Biomedical Informatics Abstract: The breakneck speed at which biology and AI are advancing stands in stark contrast to the sluggish adoption of algorithms into healthcare practices. In this presentation I will argue...
Understanding the Disability Community’s Needs for Communicating with Social and Caregiving Networks
Переглядів 317 місяців тому
Title: Understanding the Disability Community’s Needs for Communicating with Social and Caregiving Networks Presenter: Rupa S. Valdez, PhD, MS, Professor, Public Health Sciences & Systems and Information Engineering, University of Virginia Abstract: Two empirical studies conducted in partnership with the disability community will be presented. Both studies present an in depth needs assessment t...
How Do We Get There?: Toward Intelligent Behavior Intervention
Переглядів 5157 місяців тому
Title: How Do We Get There?: Toward Intelligent Behavior Intervention Speaker: Xuhai "Orson" Xu, Postdoctoral Associate, Massachusetts Institute of Technology Abstract: As the intelligence of everyday smart devices continues to evolve, they can already monitor basic health behaviors such as physical activities and heart rates. The vision of an intelligent behavior change intervention pipeline f...
Causal Health Equity - Toward a Taxonomy (Drago Plecko)
Переглядів 8418 місяців тому
Title: Causal Health Equity - Toward a Taxonomy Presenter: Drago Plecko, Computer Science Department, Columbia University Abstract: The widespread adoption of electronic health records (EHRs) has allowed healthcare providers and health researchers to measure and analyze health disparities across demographic groups, which are known to cause both higher healthcare costs and losses to welfare. A n...
Enhancing Healthcare Decision-Making with AI: Towards Clinically Applicable ... (Shengpu Tang)
Переглядів 4,9 тис.8 місяців тому
Title: Enhancing Healthcare Decision-Making with AI: Towards Clinically Applicable Reinforcement Learning Presenter: Shengpu Tang, PhD candidate in Computer Science and Engineering, University of Michigan Abstract: Decisions are everywhere in healthcare, ranging from diagnosis to treatments, to care coordination and resource allocation - and these decisions directly impact patient care and outc...
Learning to Assess Disease and Health In Your Home (Yuzhe Yang)
Переглядів 3588 місяців тому
Title: Learning to Assess Disease and Health In Your Home Speaker: Yuzhe Yang, PhD Candidate, MIT Abstract: Today’s clinical systems frequently exhibit delayed diagnoses, sporadic patient visits, and unequal access to care. Can we identify chronic diseases earlier, potentially before they manifest clinically? Furthermore, can we bring comprehensive medical assessments into patient’s own homes t...
The Burden of Burden Measurement: Evaluation & Real-World Clinical Application of ... (Elise Ruan)
Переглядів 678 місяців тому
The Burden of Burden Measurement: Evaluation & Real-World Clinical Application of ... (Elise Ruan)
On Being an Outlier in a World that Worships Optimization (Rua Williams)
Переглядів 1868 місяців тому
On Being an Outlier in a World that Worships Optimization (Rua Williams)
Helping physicians make sense of medical evidence with LLMs (Byron Wallace)
Переглядів 14 тис.9 місяців тому
Helping physicians make sense of medical evidence with LLMs (Byron Wallace)
Translating FDA regulated Software as a Medical Device from Research to Practice (David Vidal)
Переглядів 3819 місяців тому
Translating FDA regulated Software as a Medical Device from Research to Practice (David Vidal)
Understanding how Neural Networks learn patterns from data (Dec. 4, Adit Radhakrishnan)
Переглядів 1,2 тис.11 місяців тому
Understanding how Neural Networks learn patterns from data (Dec. 4, Adit Radhakrishnan)
Healthcare Transformation Using AI/ML - a Use Case in Malnutrition AI Screening Tool (MAST) (H. Cao)
Переглядів 16711 місяців тому
Healthcare Transformation Using AI/ML - a Use Case in Malnutrition AI Screening Tool (MAST) (H. Cao)
Image-based Primary Open-angle Glaucoma Diagnosis and Prognosis (Yifan Peng)
Переглядів 3411 місяців тому
Image-based Primary Open-angle Glaucoma Diagnosis and Prognosis (Yifan Peng)
Improving Prediction of 30-day Hospital Readmission for Patients with Heart Failure ... (Joyce Ho)
Переглядів 98Рік тому
Improving Prediction of 30-day Hospital Readmission for Patients with Heart Failure ... (Joyce Ho)
Applications of Human-Centered Design to Create Inclusive Health Informatics Interventions
Переглядів 35Рік тому
Applications of Human-Centered Design to Create Inclusive Health Informatics Interventions
Radiomics, Radiogenomics, and AI: The Emerging Role of Imaging Biomarkers in Precision Cancer Care
Переглядів 1 тис.Рік тому
Radiomics, Radiogenomics, and AI: The Emerging Role of Imaging Biomarkers in Precision Cancer Care
Machine Learning in Healthcare: standing on, or looking over, the shoulders of clinicians?
Переглядів 63Рік тому
Machine Learning in Healthcare: standing on, or looking over, the shoulders of clinicians?
Justice, Equity, Fairness, and Anti-Bias (JustEFAB) ... (Sept. 18, Melissa McCradden)
Переглядів 159Рік тому
Justice, Equity, Fairness, and Anti-Bias (JustEFAB) ... (Sept. 18, Melissa McCradden)
#DATABACK: Indigenous Genomic Data Justice for Indigenous Peoples (May 1, Krystal Tsosie/Keolu Fox)
Переглядів 1,1 тис.Рік тому
#DATABACK: Indigenous Genomic Data Justice for Indigenous Peoples (May 1, Krystal Tsosie/Keolu Fox)
Getting Started with AI for Medical Imaging: Exploring CXR Foundation from Google Health AI
Переглядів 101Рік тому
Getting Started with AI for Medical Imaging: Exploring CXR Foundation from Google Health AI
Very nice talk. Field of defining phenotypes is still understudied and using self-supervised and unsupervised deep learning is the way forward.
she is attractive and very sexy young woman! I can't hardly stop and stare. what a site for sore eyes 👀, have a great day!!!! 0:00
this girl is a keeper ! Looks good to me! and no stretch marks! ha! ha!
@24:44
It seems these top universities are obsessed with Data science when they themselves are writing that, these models/algorithms won't succeed fully unless take other social-technical/political/behavioral contexts into consideration.
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May I ask how to apply Clinical Data Analysis and Reporting System (CDARS) of HA in HK
Nice thanks for sharing !!! Best YT views provider -> *PromoSM*!!
You are amazing 😻
🎉
Why are this two women wearing masks? Will they get contaminated in the room they are?
Great analysis and suggestions. Thank you!
Nut.
There are some that say that moments of catharsis emerge from a low point in one's life, "THE ABYSS" in the Hero's Journey™ if you will. I would argue differently, a true catharsis is the result of understanding oneself, irrespective to one's state of life. A famous ancient Chinese philosopher once said "To lose, is to improve". The depth in this simple sentence cannot be understated. His underlying thesis is clear, that the moment of catharsis is defined by the state of understanding that there is a higher attainable state ahead of you. This journey is not a mere march towards a theoretical pinnacle - it is an endless joyful excursion into the unknown of one's own humanity. I attained this nirvana while listening to Tony Sun describe his research. Similar to the way that a Ferrero Roche's bitter hazelnut core is enveloped by layers of crunchy delectable nuts and smooth velvety chocolate, the intricacies of this researcher's ideas cannot be described with mere words. Peals of wisdom ooze from those lusciously juicy lips, caressing my inner heartstrings and synaptic connections with a blanket of jubilant ecstacy. His soothing voice tenderly graces my ears, coating them with cocoa butter kisses and mind-blowing facts about diagnostic patterns in the American Healthcare system. Just like a baby resting in his mother's amniotic fluid, I am immersed and engrossed in being birthed anew. Please do yourself a favor and listen to this video more than once. More than a dozen times. Each time you will delight yourself with new revelations that emerge from Tony's thoughtful words. Become a better version of the person you once hoped to become. Childhood fantasies merge with the present, and once forgotten foolish wishes become a reality. You will attain everything you thought was impossible if you watch this video enough times. P.S. Please subscribe to twitch.tv/lilrich96.
One of the best researchers I know!
Wow, this research seems great. Excited to see where it goes!
what a spiffy young lad
Agreed
Good luck Dr. ELIAS 👍
good content
Excellent! You have a new subscriber. You should use promosm! It will help you get your videos higher in the search results!
There are abundant imporant ideas in this talk. We need to be aware of the cognitive work physicians do. We can de-implement unneeded documentation. We can stop documenting normal findings. Our histories can become terse and meaningful. Our exams short and to the point. Time is wasted creating note bloat, time is wasted reading note bloat. We can and must do better.
I especially liked the "Myth of the Superhero", also the concept of "shallow" v.s. "deep work". "Project Joy" and "Click Busters" sound fantastic. Comments about "Covid rules" freeing up physicians to do physician level work is an important empirical experiment in simplifying documentation and coding requirements.
Nice job Ginny
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