Research | Icahn School of Medicine

The Center performs interdisciplinary research and seeks to improve human health by studying and facilitating effective use of diverse biomedical data. Our focus is on the exchange of health information, standards and terminologies, information access and sharing, data aggregation and harmonization, digital knowledge generation and management, problem-solving, and decision-making.

Our research is focused on precision health and improving personalized care delivery using big data analytics, predictive modeling, and deep phenotyping. We employ multidisciplinary approaches from basic sciences, biomedical informatics, engineering, computer and data sciences, medicine and dentistry, and system biology. By integrating these sources of information, we can develop innovative solutions for personalized health care.

The research projects at the Center for Biomedical and Population Health Informatics are aligned with the major domains of biomedical informatics. These include research informatics, translational informatics, health care informatics, and public health informatics. We describe each of these areas below.

Research informatics projects provide approaches, methodology, and architecture supporting pre-clinical, clinical, and translational research. The projects promote patient engagement in clinical trials, data collection tools, and management of clinical research data. In addition, these projects involve integrative informatics approaches for harmonized aggregation, visualization, and sharing of heterogeneous biological and health care data streams.

Translational informatics projects include big data analytics; predictive modeling; novel techniques for integrative analysis of biological and clinical data; precision medicine; deep phenotyping; computerized analysis of genetic variation, proteomics, metabolome, microbiome, and exposome; and examination of links between personalized health and systemic conditions.

Health care informatics projects encompass innovations in electronic health records, clinical decision support, quality improvement, patient safety, telemedicine, telerehabilitation, digital and mobile health, consumer informatics, personalized care delivery, real-time asset management, value-based care systems, and learning health care systems.

Population health informatics projects include development and evaluation of innovative approaches for population health literacy, health decision aids, health outcomes research, risk stratification, and population surveillance.

Presentations

Using Machine Learning to Identify No-Show Telemedicine Encounters
Wanting Cui, Joseph Finkelstein
ICIMTH 2022
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Identifying Determinants of Disparities in Lung Cancer Survival Rates from Electronic Health Record Data
Wanting Cui, Joseph Finkelstein
MIE 2022
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Comparative Analyses of Patient Distress in Opioid Treatment Programs Using Natural Language Processing
Shah-Mohammadi F, Cui W, Bachi K, Hurd Y, Finkelstein
HEALTHINFO 2022

Machine Learning Approaches for Early Prostate Cancer Prediction Based on Healthcare Utilization Patterns
Finkelstein J, Cui W, Martin TC, Parsons R,
ICIMTH 2021

Intelligent Integrative Platform for Sharing Heterogenuous Stem Cell Research Data
Borziak K, Parvanova I, Finkelstein J
EFMI STC 2021
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A comprehensive platform for induced pluripotent stem cell research data
Borziak K, Parvanova I, Finkelstein J
AMIA 2021
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A Platform for Integrating and Sharing Cancer Stem Cell Data
Parvanova I, Borziak K, Guarino J, Finkelstein J
EMBC 2021
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Characteristics of Electronic Informed Consent Platforms for Consenting Patients to Research Studies: A Scoping Review
Guarino J, Parvanova I, Finkelstein J
MEDINFO 2021
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Core Outcomes Sets Development in COVID-19 Clinical Trials
Parvanova I, Finkelstein J
AMIA 2021
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Cognitive Testing of an Electronic Consent Platform: Researcher Perspectives
Parvanova I, Robins D, Liu J, Finkelstein J
Nursing Informatics 2021
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Telerehabilitation for patients with cancer: A Scoping Review
Rocco P, Finkelstein J
MEDINFO 2021

Use of Artificial Intelligence for Predicting COVID-19 Outcomes: A Scoping Review
Lyu J, Cui W, Finkelstein J
ICIMTH 2021

Regenerative Medicine Data Repository (ReMeDy) for Harmonized Aggregation and Sharing of Stem Cell Research Data
Borziak K, Evangelista JE, Clarke D, Ma'ayan A, Finkelstein J
AMIA 2021
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Utilizing Shared Big Data to Identify Liver Cancer Dedifferentiation Markers
Borziak K, Finkelstein J
ICIMTH 2021
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Comparative Analysis of Telemedicine Services Before and During Pandemic
Cui W, Finkelstein J
AMIA 2021

Using EHR Data to Identify Social Determinants of Health Affecting Disparities in Cancer Survival
Cui W, Finkelstein J
MEDINFO 2021
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Using Big Data to Identify Risk Factors for Opioid Abuse Among Patients with Opioid Prescriptions
Huo X, Pradhan A, Shaya F, Finkelstein J
AMIA 2021
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NLP -Assisted Pipeline for COVID-19 Core Outcome Set Identification Using ClinicalTrials.gov
Shah-Mohammadi F, Parvanova I, Finkelstein J
NLP 2021

Latent COVID-19 Clusters in Patients with Opioid Misuse
Shah-Mohammadi F, Cui W, Bachi K, Hurd Y, Finkelstein J
ICIMTH 2021

Comparison of ACM and CLAMP for Entity Extraction in Clinical Notes
Shah-Mohammadi F, Cui W, Finkelstein J,
EMBC 2021
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Entity Extraction for Clinical Notes, a Comparison Between MetaMap and Amazon Comprehend Medical
Shah-Mohammadi F, Cui W, Finkelstein J
MIE 2021
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Comparative Analysis of Public Data Sets to Identify Stemness Markers that Differentiate Liver Cancer Stem Cells
Borziak K, Finkelstein J
MIE 2021
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Introducing a platform for integrating and sharing stem cell research data
Borziak K, Parvanova I, Finkelstein J
MIE 2021
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Impact of COVID-19 Pandemic on Use of Telemedicine Services in an Academic Medical Center
Cui W, Finkelstein J
MIE 2021
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Identifying Common Outcome Sets in Covid-19 Clinical Trials Using ClinicalTrials.gov
Parvanova I, Finkelstein J
MIE 2021
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