Blog
Our work has been presented at national conferences and published in peer-reviewed journals. We are also frequently posting about our new projects.
Mount Sinai Ranks as No. 1 Health Care Institution according to Nature AI Index
Mount Sinai Health System has been ranked No. 1 on the new Nature AI Index 2024 of leading healthcare institutions. Here's full press release from Mount Sinai Communications: Mount Sinai Health System is No. 1 on the new Nature AI Index 2024 list of “Leading 10 Healthcare Institutions,” and among the world’s “Leading 200 Institutions” for AI across all fields, published by the prestigious journal Nature. The list ranks institutions by the number of affiliated researchers who contributed...
Mount Sinai ranked among the top 10 smart hospitals in the world
Mount Sinai has once again been ranked among the top 10 smart hospitals in the world by Newsweek: This recognition is a direct result of our collective commitment to advancing healthcare through digital, AI, data, and informatics. Robbie Freeman,Vice President of Clinical Innovation and Chief Nursing Informatics Officer at the Mount Sinai Health System The full rankings can be found at https://www.newsweek.com/rankings/worlds-best-smart-hospitals-2025.
Real-Time Machine Learning Alerts to Prevent Escalation of Care: A Nonrandomized Clustered Pragmatic Clinical Trial*
The "Real-Time Machine Learning Alerts to Prevent Escalation of Care: A Nonrandomized Clustered Pragmatic Clinical Trial*" paper, which was authored by several members of the Clinical Data Science team, has been published in the Critical Care Medicine journal, July 2024 - Volume 52 - Issue 7 edition. The full publication can be found at https://journals.lww.com/ccmjournal/fulltext/2024/07000/real_time_machine_learning_alerts_to_prevent.3.aspx. The accompanying editorial, "Moving From In Silico...
Mount Sinai Health System named 2024 Hearst Health Prize winner
The Clinical Data Science team at Mount Sinai Hospital was named the winner of the 2024 Hearst Health Prize. The team was declared the winner for its malnutrition identification machine learning engine, which is used to better identify patients with malnutrition throughout the hospital system. The full press release can be found at https://www.prnewswire.com/news-releases/mount-sinai-health-system-named-2024-hearst-health-prize-winner-302166047.html.
Mount Sinai’s CDS team nominated for the Hearst Health Prize
Mount Sinai's Clinical Data Science team has been nominated for the Hearst Health Prize! "The Hearst Health Prize showcases data science programs making an impact on human health. The judges evaluated submissions using several criteria, including demonstrated health impact or outcomes, data science approach, and scalability." The full press release can be found at...
Next-Generation Health Care: AI-Driven Patient Risk Profiling for Pressure Injuries in The Mount Sinai Hospital
CDS' Hospital Acquired Pressure Injury Prevention project has been featured in Mount Sinai's Spring 2024 nursing newsletter, Magnet NEWS. The full article can be found on pages 20-21 (https://mshs.co/3UN8hIS).
“Real-Time Machine Learning Alerts to Prevent Escalation of Care: A Nonrandomized Clustered Pragmatic Clinical Trial” study published in Critical Care Medicine journal
The study titled "Real-Time Machine Learning Alerts to Prevent Escalation of Care: A Nonrandomized Clustered Pragmatic Clinical Trial", authored by Dr. Levin and several members of the Clinical Data Science team, has been published in the Critical Care Medicine journal: https://journals.lww.com/ccmjournal/abstract/9900/real_time_machine_learning_alerts_to_prevent.296.aspx Abstract Objectives: Machine learning algorithms can outperform older methods in predicting clinical deterioration, but...
CDS presentation in the 2024 Critical Care Congress
The Clinical Data Science team, represented by our data scientist Kim-Anh-Nhi Nguyen, MSc, presented the paper "A Deep Learning Model using Chest X-Rays to Predict Re-Intubation Risk in Critical Care" at the 2024 Critical Care Congress in Phoenix, Arizona. More details of presentation can be found below: 2024 Critical Care Congress Category: Future of Critical Care Title: A Deep Learning Model using Chest X-Rays to Predict Re-Intubation Risk in Critical Care ...
A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation
A new paper written by the Clinical Data Science team has been published in JMIR (https://formative.jmir.org/2023/1/e46905). Background: Early prediction of the need for invasive mechanical ventilation (IMV) in patients hospitalized with COVID-19 symptoms can help in the allocation of resources appropriately and improve patient outcomes by appropriately monitoring and treating patients at the greatest risk of respiratory failure. To help with the complexity of deciding whether a patient needs...