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How do we use data and technology to improve health and healthcare delivery? 
 
A central theme of my research is concerned with developing health informatics and data science tools as data-driven decision support for health and healthcare delivery. I am interested in outcome prediction, process optimization, and health communication. My research is funded by the NIH, AHRQ, and US-DOT. 

Aside from research, as the Informatics Director of Clinical Decision Support at NewYork-Presbyterian Hospital, I also work on the implementation of prediction models into the electronic health record systems.

An emerging interest of mine is the health impact of social media.

I am fluent in English, Japanese, and Chinese. I welcome global research collaborations.

Follow me on LinkedIn and Google Scholar for recent news and updates! ​

March 2024

  • Going to AMIA Informatics Summit to present in AMIA AI Showcase!

  • Paper on our AI implementation effectiveness paper is accepted at JAMIA!

  • Honored to participate in the AIM-AHEAD expert program as an AI expert

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POSITIONS

Academic

Associate Professor

Department of Population Health Sciences

Department of Emergency Medicine

Cornell Systems Engineering

Cornell University

Clinical

Informatics Director of Clinical Decision Support

NewYork-Presbyterian Hospitals 

Professional Engagement

NIH Study Section reviewers (ad hoc)

NSF Study Section reviewers (ad hoc)

JAMIA Open Guest Editor (Women's Health special issue)

SELECTED RESEARCH PROJECTS

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MACHINE LEARNING ALGORITHM DEVELOPMENT

The application of AI in health requires a deep knowledge about the methodology and the domain. We develop customized algorithms for data-driven clinical decision support.

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PROCESS OPTIMIZATION

The electronic health record captures a wealth of data on clinician decisions and activities. We study these logs to propose optimization in the clinical workflow.

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SPECIAL (SUPPORTING PREGNANCY CARE USING ARTIFICIAL INTELLIGENCE)

Maternal health remains an understudied area in Health Informatics. We aim to improve care process and outcomes using AI.

Development and validation of a machine learning algorithm for predicting the risk of postpartum depression among pregnant women

This model is currently implemented into Epic EHR. For more details, please see our upcoming publication in JAMIA.

+ see the white paper for model description, and docker container instruction for downloading the codes:

docker pull ichiyoz/ppd

GET IN TOUCH

I am looking for a research assistant to join my team!


Weill Cornell Medicine, Department of Healthcare Policy and Research, Division of Health Informatics

425 E 61st St
New York, New York County 10065
USA

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