Using AI to Improve Clinical Outcomes
Clinical Data Science
We are a team of data scientists and engineers that leverage the power of ML and AI at The Mount Sinai Hospital.
Clinical Conditions
Data science, machine learning (ML) and artificial intelligence (AI) have the potential to transform the delivery of clinical care and improve early recognition and diagnosis of clinical conditions
Hospital Operations
AI allows us to gain new insights and continuously improve over time as the platform learns from our patients and clinicians to enhance hospital operations
Awareness and Intervention
We have built a powerful real-time streaming clinical data platform that brings the power of ML and AI to improve provider awareness and timely intervention
Impact of a Real-Time Ventilator Management Dashboard with Alerts: Sustained Hospital-Wide Improvement in Lung Protective Ventilation
The following is a summary of a presentation conducted by our team for the American Thoracic Society 2022 International Conference, which took place May 13-18, 2022. For more information, visit...
CDI Alert System for Metastatic Disease
The Metastatic streaming engine was developed to identify inpatient encounters with high risk of metastatic disease (cancer that spreads from where it started to a distant part of the body) and optimize the capture rate. It was deployed at The Mount Sinai Hospital on...
Fusion of Chest Radiographs and Electronic Medical Records using Deep Learning to Predict Intubation among Hospitalized Patients with COVID-19
The following is a summary of a presentation conducted by our team for the AMIA 2022 Informatics Summit which took place on March 21-24, 2022. For more information, visit...
Strategies for Use of Training, Mentoring, and Sponsoring for Increasing Women in Biostatistics and Data Science Workforce
The following is a summary of a conference presentation conducted by our team at the Women in Statistics and Data Science Conference, which took place October 6-8, 2021. More information can be found at...
Deployment, Adoption, and Clinical Impact of a Real-Time Ventilator Management Dashboard
The following is a summary of a thematic poster presentation conducted by our team for the American Thoracic Society 2021 International Conference, which took place May 14-19, 2021. For more information, visit...
Predicting Readiness to Liberate from Mechanical Ventilation Using Machine Learning: Development and Retrospective Validation
The following is a summary of a thematic poster presentation conducted by our team for the American Thoracic Society 2021 International Conference, which took place May 14-19, 2021. For more information, visit...
Oncology Prognosis Tool
The Oncology Prognosis Tool is a classifier used to determine the eligibility of patients for serious illness conversation. It was deployed at the Mount Sinai Oncology Outpatient unit in March 2021 and Inpatient unit in August 2022. Challenges Majority of patients(~...
Patient Experience Project
The objective of the Patient Experience project is to predict patients who are most likely to report a sub-optimal inpatient experience in order to initiate service recovery, or other engagement strategies of the patient experience team, prior to discharge. The...
Development and validation of a machine learning-based prediction model for near-term in-hospital mortality among patients with COVID-19
This paper was published in BMJ Supportive and Palliative Care 2022;12:e424-e431. Parchure P, Joshi H, Dharmarajan K, et al. Development and validation of a machine learning-based prediction model for near-term in-hospital mortality among patients with COVID-19. BMJ...