HPI∙MS | Hasso Plattner Institute for Digital Health at Mount Sinai

Shaping the future of digital health

We are a global research institute rapidly developing digital health solutions that empower patients and healthcare providers.

A collaboration between

About Us

Turning the promise of digital health into a reality

Science has helped humanity to improve and extend life. Today, technological advances have allowed us to understand and analyze information in ways never before possible.

The Hasso Plattner Institute for Digital Health at Mount Sinai (HPI･MS) propels these possibilities through an extraordinary international academic collaboration between the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany, and the Mount Sinai Health System in New York City, USA.

Learn more about us

Why HPI•MS

A new era of digital health

Combining an innovative health care system with world class engineering expertise to offer unprecedented opportunities for improved global well-being and medical discovery.

Expanding possibilities in medicine

Advancements in data science and engineering are rapidly translated into medical discovery in partnership with clinical experts.

Improving access to critical data

Data science powered by 7.6 million clinical records is connected to diverse genomic sequencing, comprehensive medical imaging, and biometric cardiology data, alongside innovative clinical research.

Optimizing human health and well-being

By linking comprehensive patient data from hospital visits, genetic testing, and remote monitoring devices, patients and their physicians will be able to monitor, diagnose and inform personalized treatment plans.

Advancing the state of the art

Leveraging the latest technologies, creating novel methodologies, and bringing together interdisciplinary expertise, HPI⋅MS is leading the digital health revolution.

Research

Leading the digital health revolution

AIR.MS - AI Ready Mount Sinai

Leveraging expertise in computer science and engineering, we've built a platform where researchers can access the vast datasets held at Mount Sinai for data science and machine learning endeavors.

View full project

Digital Discovery Program

The DDP comprehensive digital health research program of patient-centric health studies utilizing wearable, mobile and sensor technologies serves as a scalable platform for digital trials in order to better understand complex diseases and optimize health and wellbeing.

View full project

Explore all research initiatives

Publications

World class research with a global impact

January, 2021

Physiological Data from a Wearable Device Identifies SARS-CoV-2 Infection and Symptoms and Predicts COVID-19 Diagnosis: Observational Study

Journal

Journal of Medical Internet Research

HPI·MS Authors

.author__profile-picture.gbi-2018879802-bCmYwY6N6QdRUZ1rC19Yie:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/6f1o7zPRyWXQ4KMqD8ztKn/c253edc1332bf12b9e4c4db103f913ce/Image_from_iOS__1_.jpg?w=24&q=100'); }

Robert Hirten

.author__profile-picture.gbi--1969690618-3EkUw8HFqcNBgDQwFyZXSp:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/70OBBRibe7LMvDg9nfoFs0/0f80292ffb70a2f785406c0543c1c617/Matteo-Danieletto.png?w=24&q=100'); }

Matteo Danieletto

.author__profile-picture.gbi-17046154-rwRdkrtvfv6tcJjwAhpuj9:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/6BvuqbykydFjvuOn5HJ7ao/39d9a6f796e99928265d147db5f4b9c0/Micol-Zweig.png?w=24&q=100'); }

Micol Zweig

.author__profile-picture.gbi--1434296477-RYfL99bNwnSkF15XGanoc:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/2FbsW2sXAKdc3czRH8tRPf/af1a84a93b6cf5357bca619a75521132/eddye_headshot.jpg?w=24&q=100'); }

Eddye Golden

.author__profile-picture.gbi-944584562-ryvKEUGeaKEGtCi17CT9vM:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/63O9uTFob0dP0UWrZtcxc4/fe3ace28cda7ba99387087054ebd044b/sparshdeep-kaur.png?w=24&q=100'); }

Sparshdeep (Shelly) Kaur

.author__profile-picture.gbi-1854195918-mkaCAdXrYLz7q7xzz67wEu:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/3hEWbF5T0Y79sYLLsLslAB/ce19591f01acd3536bb4dd0e133dfa67/Alex_Charney.png?w=24&q=100'); }

Alex Charney

.author__profile-picture.gbi--183137074-pE5EGw5aUQPx1LTATjXTH1:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/84hEOwyQQXKh9ASxFJJAx/712a07b375d695ca32e742d1a58a09b8/Riccardo_Miotto.png?w=24&q=100'); }

Riccardo Miotto

.author__profile-picture.gbi--111367730-qR64uqGJLEVvRKrJNnhJvJ:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/5Hy8JqLpZ5AKug1PuY6CLO/b4838b545e7f0fe90f45b19bacf045d5/Ben-Glicksberg.png?w=24&q=100'); }

Benjamin Glicksberg

.author__profile-picture.gbi--172148350-snY9zrA8U4FLQqDSYhyZ55:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/19qKAMLjqjuCigL5nC3yMO/d7c2dad6c2d4ad947ce9b2ea49fa7746/Erwin-Bottinger_web.jpg?w=24&q=100'); }

Erwin Bottinger

.author__profile-picture.gbi--748886666-wDrNazhhKv1G1iYgRgVNK:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/2TVnDzADjRqgSryXlvWYqT/558c5c5d8e3c7b5139417bf21129df69/Girish-Nadkarni.png?w=24&q=100'); }

Girish Nadkarni

.author__profile-picture.gbi--1423126066-56HTbdFtWT8H4ujwrrhum8:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/2QywYUc22Tw3W4WzkbwPJa/3e6bd4cb020a8f5a68b6530fc215c57c/Zahi-Fayad.png?w=24&q=100'); }

Zahi Fayad

11 HPI•MS authors

Full list of authors

Robert P Hirten; Matteo Danieletto; Lewis Tomalin; Katie Hyewon Choi; Micol Zweig; Eddye Golden; Sparshdeep Kaur; Drew Helmus; Anthony Biello; Renata Pyzik; Alexander Charney; Riccardo Miotto; Benjamin S Glicksberg; Matthew Levin; Ismail Nabeel; Judith Aberg; David Reich; Dennis Charney; Erwin P Bottinger; Laurie Keefer; Mayte Suarez-Farinas; Girish N Nadkarni; Zahi A Fayad

Journal

Journal of Medical Internet Research

Open Publication

November, 2020

Deep Learning for Biomedical Applications

Journal

Machine Learning in Cardiovascular Medicine, Elsevier

HPI·MS Authors

.author__profile-picture.gbi-221404342-7mcTN9XhV4gYcMwZtRV8SV:before { opacity: 1; background-image: url('/static/06e843d62425f8b6e8fd6107468dc5a3/e9c03/profile-picture-placeholder.png'); }

Jessica De Freitas

.author__profile-picture.gbi--111367730-okD15DnALp26iXsDShsz2n:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/5Hy8JqLpZ5AKug1PuY6CLO/b4838b545e7f0fe90f45b19bacf045d5/Ben-Glicksberg.png?w=24&q=100'); }

Benjamin Glicksberg

.author__profile-picture.gbi-221404342-ac3X2XPFx4FATGeXwjCYoS:before { opacity: 1; background-image: url('/static/06e843d62425f8b6e8fd6107468dc5a3/e9c03/profile-picture-placeholder.png'); }

Kipp Johnson

.author__profile-picture.gbi--183137074-qeGAHWaLT73RHXmQyfRneB:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/84hEOwyQQXKh9ASxFJJAx/712a07b375d695ca32e742d1a58a09b8/Riccardo_Miotto.png?w=24&q=100'); }

Riccardo Miotto

4 HPI•MS authors

Full list of authors

Jessica De Freitas, Benjamin S. Glicksberg, Kipp W. Johnson, Riccardo Miotto

Deep Learning

Book Chapter

Machine Learning in Cardiovascular Medicine, Elsevier

Open Publication

November, 2020

Phe2vec: Automated Disease Phenotyping based on Unsupervised Embeddings from Electronic Health Records

Journal

medRxiv

HPI·MS Authors

.author__profile-picture.gbi-221404342-gEZgrNr2GxnrQ2JVEESZbo:before { opacity: 1; background-image: url('/static/06e843d62425f8b6e8fd6107468dc5a3/e9c03/profile-picture-placeholder.png'); }

Jessica De Freitas

.author__profile-picture.gbi-221404342-aixzqChuQLBUkp9ky89aM6:before { opacity: 1; background-image: url('/static/06e843d62425f8b6e8fd6107468dc5a3/e9c03/profile-picture-placeholder.png'); }

Kipp Johnson

.author__profile-picture.gbi--1434296477-wjD2bPkHtGAhyau4DBweYX:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/2FbsW2sXAKdc3czRH8tRPf/af1a84a93b6cf5357bca619a75521132/eddye_headshot.jpg?w=24&q=100'); }

Eddye Golden

.author__profile-picture.gbi--748886666-qiXifs4ogUa1gd9y8EJaZM:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/2TVnDzADjRqgSryXlvWYqT/558c5c5d8e3c7b5139417bf21129df69/Girish-Nadkarni.png?w=24&q=100'); }

Girish Nadkarni

.author__profile-picture.gbi--172148350-oThfqdcqyXw9AVKSCCW43p:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/19qKAMLjqjuCigL5nC3yMO/d7c2dad6c2d4ad947ce9b2ea49fa7746/Erwin-Bottinger_web.jpg?w=24&q=100'); }

Erwin Bottinger

.author__profile-picture.gbi--111367730-jLuzSZkLsF8eaTneW4qVpv:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/5Hy8JqLpZ5AKug1PuY6CLO/b4838b545e7f0fe90f45b19bacf045d5/Ben-Glicksberg.png?w=24&q=100'); }

Benjamin Glicksberg

.author__profile-picture.gbi--183137074-jTcKCHFrdBkXwMP6ZPVr2X:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/84hEOwyQQXKh9ASxFJJAx/712a07b375d695ca32e742d1a58a09b8/Riccardo_Miotto.png?w=24&q=100'); }

Riccardo Miotto

7 HPI•MS authors

Full list of authors

Jessica K De Freitas, Kipp W Johnson, Eddye Golden, Girish N Nadkarni, Joel T Dudley, Erwin P Bottinger, Benjamin S Glicksberg, Riccardo Miotto

Journal

medRxiv

Open Publication

November, 2020

Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation

Journal

JMIR Medical Informatics

HPI·MS Authors

.author__profile-picture.gbi--714597874-8F8m9dj228vF4wfyyfbQ2X:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/2EpIOxIrCjA8ECAqfcn0gK/13ab77bc2f498ef753bdd80260312cce/Akhil_Vaid.png?w=24&q=100'); }

Akhil Vaid

.author__profile-picture.gbi-221404342-hYZLaDurrorfroqhAQY8v:before { opacity: 1; background-image: url('/static/06e843d62425f8b6e8fd6107468dc5a3/e9c03/profile-picture-placeholder.png'); }

Sulaiman Somani

.author__profile-picture.gbi-221404342-wwNXd2PLtrwAxWJQyFHGSF:before { opacity: 1; background-image: url('/static/06e843d62425f8b6e8fd6107468dc5a3/e9c03/profile-picture-placeholder.png'); }

Adam Russak

.author__profile-picture.gbi-221404342-ikmm9up1zMMHpT94NfuXCL:before { opacity: 1; background-image: url('/static/06e843d62425f8b6e8fd6107468dc5a3/e9c03/profile-picture-placeholder.png'); }

Jessica De Freitas

.author__profile-picture.gbi-221404342-uf7yfXkJSsjy62P4gQp8V7:before { opacity: 1; background-image: url('/static/06e843d62425f8b6e8fd6107468dc5a3/e9c03/profile-picture-placeholder.png'); }

Ishan Paranjpe

.author__profile-picture.gbi-221404342-oUnVPhLPi1VHkKVhURd3Wp:before { opacity: 1; background-image: url('/static/06e843d62425f8b6e8fd6107468dc5a3/e9c03/profile-picture-placeholder.png'); }

Kipp Johnson

.author__profile-picture.gbi--183137074-qVAyNcW5em1fLiipFenn4R:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/84hEOwyQQXKh9ASxFJJAx/712a07b375d695ca32e742d1a58a09b8/Riccardo_Miotto.png?w=24&q=100'); }

Riccardo Miotto

.author__profile-picture.gbi-221404342-fmWR2cJkcYPLeNjWLbDnjN:before { opacity: 1; background-image: url('/static/06e843d62425f8b6e8fd6107468dc5a3/e9c03/profile-picture-placeholder.png'); }

Shan Zhao

.author__profile-picture.gbi-221404342-5uVwQDhUCfH895r88V7cPo:before { opacity: 1; background-image: url('/static/06e843d62425f8b6e8fd6107468dc5a3/e9c03/profile-picture-placeholder.png'); }

Nidhi Naik

.author__profile-picture.gbi--2127668978-PGHdKk4HoKGAddxCoXans:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/5dd5hoJmYYnlGM1vjDjgE6/1e20f710905fa13ffbe9970f2f94bc71/Patricia-Glowe_.png?w=24&q=100'); }

Patricia (Savi) Glowe

.author__profile-picture.gbi--1434296477-vSuUZFgDLjU42rgk9FTnbU:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/2FbsW2sXAKdc3czRH8tRPf/af1a84a93b6cf5357bca619a75521132/eddye_headshot.jpg?w=24&q=100'); }

Eddye Golden

.author__profile-picture.gbi--1969690618-o4GNNYEXGv2h6WZywUgmm:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/70OBBRibe7LMvDg9nfoFs0/0f80292ffb70a2f785406c0543c1c617/Matteo-Danieletto.png?w=24&q=100'); }

Matteo Danieletto

.author__profile-picture.gbi-838165518-vWhZhDBsZtboRsQjoKnpr2:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/77oWUIHOu1xSVA81qb4gt8/038d7149a88ea89f15149984c1891d43/Manbir_Singh.png?w=24&q=100'); }

Manbir Singh

.author__profile-picture.gbi--172148350-ep5iqyzhU8AQQNLCS2qfmd:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/19qKAMLjqjuCigL5nC3yMO/d7c2dad6c2d4ad947ce9b2ea49fa7746/Erwin-Bottinger_web.jpg?w=24&q=100'); }

Erwin Bottinger

.author__profile-picture.gbi--1711466464-waf1WmjxXDqwibwiMg6Qun:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/3neUWYYLFQ0jO5iiRUrUva/84ac4c0ac5a439e68b41cb44db6bb29e/Jagat_Narula.jpg?w=24&q=100'); }

Jagat Narula

.author__profile-picture.gbi--1423126066-oe2VJQBW9jVLLSSXfr7ocP:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/2QywYUc22Tw3W4WzkbwPJa/3e6bd4cb020a8f5a68b6530fc215c57c/Zahi-Fayad.png?w=24&q=100'); }

Zahi Fayad

.author__profile-picture.gbi-1854195918-t4bNFT8pMh66428No2sF9w:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/3hEWbF5T0Y79sYLLsLslAB/ce19591f01acd3536bb4dd0e133dfa67/Alex_Charney.png?w=24&q=100'); }

Alex Charney

.author__profile-picture.gbi--111367730-8izTRxxikKoFo5jFZDryan:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/5Hy8JqLpZ5AKug1PuY6CLO/b4838b545e7f0fe90f45b19bacf045d5/Ben-Glicksberg.png?w=24&q=100'); }

Benjamin Glicksberg

.author__profile-picture.gbi--748886666-4tjk5boJHRoCoXQ7TXFSZc:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/2TVnDzADjRqgSryXlvWYqT/558c5c5d8e3c7b5139417bf21129df69/Girish-Nadkarni.png?w=24&q=100'); }

Girish Nadkarni

19 HPI•MS authors

Full list of authors

Akhil Vaid, Sulaiman Somani, Adam J Russak, Jessica K De Freitas, Fayzan F Chaudhry, Ishan Paranjpe, Kipp W Johnson, Samuel J Lee, Riccardo Miotto, Shan Zhao, Noam Beckmann, Nidhi Naik, Kodi Arfer, Arash Kia, Prem Timsina, Anuradha Lala, Manish Paranjpe, Patricia Glowe, Eddye Golden, Matteo Danieletto, Manbir Singh, Dara Meyer, Paul F O'Reilly, Laura H Huckins, Patricia Kovatch, Joseph Finkelstein, Robert M Freeman, Edgar Argulian, Andrew Kasarskis, Bethany Percha, Judith A Aberg, Emilia Bagiella, Carol R Horowitz, Barbara Murphy, Eric J Nestler, Eric E Schadt, Judy H Cho, Carlos Cordon-Cardo, Valentin Fuster, Dennis S Charney, David L Reich, Erwin P Bottinger, Matthew A Levin, Jagat Narula, Zahi A Fayad, Allan Just, Alexander W Charney, Girish N Nadkarni, Benjamin S Glicksberg

COVID-19 Machine Learning Electronic Health Records

Journal

JMIR Medical Informatics

Open Publication

See all publications

Press

HPI·MS in the news

News Breezer

Mount Sinai Study Shows Wearable Devices Can Detect COVID-19 Symptoms and Predict the Diagnosis

News Medical

Using 'federated learning' to enhance predictions of COVID-19 outcomes

Press Release

Mount Sinai Develops Machine Learning Models to Predict Critical Illness and Mortality in COVID-19 Patients

Our Last Event

HPI - Mount Sinai Digital Health Forum

2019 | Potsdam, Germany

We hosted a successful two-day conference on November 21 and November 22, 2019 at the Hasso Plattner Institute in Potsdam. The Forum introduced visions and strategies for driving the digital transformation of health care.

With more than 400 attendees and livestream viewers from around the world, the conference facilitated transatlantic co-innovation among international experts in medical and biological sciences, engineering, computer science, and research, from world-leading organizations.

Shaping the future of digital health

Turning the promise of digital health into a reality

A new era of digital health

Leading the digital health revolution

AIR.MS - AI Ready Mount Sinai

Digital Discovery Program

World class research with a global impact

Physiological Data from a Wearable Device Identifies SARS-CoV-2 Infection and Symptoms and Predicts COVID-19 Diagnosis: Observational Study

Deep Learning for Biomedical Applications

Phe2vec: Automated Disease Phenotyping based on Unsupervised Embeddings from Electronic Health Records

Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation

HPI·MS in the news

Mount Sinai receives Microsoft ai for health grant to support center dedicated to data science discovery for COVID-19

Software provides non-invasive hemoglobin measurement with a smart phone

Mount Sinai to open new Institute for Digital Health

Mount Sinai Study Shows Wearable Devices Can Detect COVID-19 Symptoms and Predict the Diagnosis

Using 'federated learning' to enhance predictions of COVID-19 outcomes

Mount Sinai Develops Machine Learning Models to Predict Critical Illness and Mortality in COVID-19 Patients

ICD-10 codes don't accurately capture COVID-19 symptoms, study shows

Is Oura’s $299 ‘Smart Ring’ Just a Fancy Thermometer?

Mount Sinai receives Microsoft ai for health grant to support center dedicated to data science discovery for COVID-19

Software provides non-invasive hemoglobin measurement with a smart phone

Mount Sinai to open new Institute for Digital Health

Mount Sinai Study Shows Wearable Devices Can Detect COVID-19 Symptoms and Predict the Diagnosis

Using 'federated learning' to enhance predictions of COVID-19 outcomes

Mount Sinai Develops Machine Learning Models to Predict Critical Illness and Mortality in COVID-19 Patients

ICD-10 codes don't accurately capture COVID-19 symptoms, study shows

Is Oura’s $299 ‘Smart Ring’ Just a Fancy Thermometer?

Mount Sinai receives Microsoft ai for health grant to support center dedicated to data science discovery for COVID-19

Software provides non-invasive hemoglobin measurement with a smart phone

Mount Sinai to open new Institute for Digital Health

HPI - Mount Sinai Digital Health Forum