Fei Wang is currently a tenured Professor of Health Informatics in Department of Population Health Sciences at Weill Cornell Medicine (WCM), where he also holds a secondary appointment as a Professor in Department of Emergency Medicine. Dr. Wang is the Founding Director of the WCM Institute of AI for Digital Health (AIDH) and an Adjunct Scientist at Hospital for Special Surgery (HSS). His research interest is machine learning and artificial intelligence in biomedicine. Dr. Wang has published over 350 papers on the major venues of AI and biomedicine, which have received more than 30K citations to date. His H-index is 83. Dr. Wang is an elected fellow of American Medical Informatics Association (AMIA), American College of Medical Informatics (ACMI) and International Academy of Health Sciences and Informatics (IAHSI), and a distinguished member of Association for Computing Machinery (ACM).
Overview and Courses
As data in healthcare grows more complex, AI is increasingly being used to enhance diagnoses, treatment, patient engagement, and administrative tasks. Brought to you by expert faculty from Weill Cornell Medicine, this certificate program focuses on the transformative power of data science and artificial intelligence in the healthcare space.
You will begin by developing a foundation in machine learning techniques, emphasizing data preparation and predictive analytics to improve patient care. You will build on this by focusing on efficient data management, exploring relational databases, and honing skills in data querying for insightful healthcare decisions. You will then be introduced to natural language processing (NLP), exploring methods to extract insights from medical texts as well as ways to apply conscientious practices in data handling. Finally, you will examine the human factors influencing AI design and implementation, advocating for user-centered solutions that adhere to ethical principles.
Throughout the courses, you will have the opportunity to apply these teachings in hands-on exercises as you gain practice addressing real-world healthcare problems. Together, these courses will equip you with the skills to leverage technology for improved healthcare outcomes, ensuring data integrity, patient privacy, and the creation of equitable AI-driven solutions.
You must have intermediate proficiency in Python programming and machine learning to be successful in this certificate program.
The courses in this certificate program are required to be completed in the order that they appear.
Course list
In the healthcare sector, patient data is abundant. Machine learning can transform this data into a powerful tool for prediction and analysis. In this course, you will explore supervised and unsupervised learning, two key machine learning approaches that can help you maximize your data's potential. Before addressing healthcare challenges with machine learning, it's essential to begin with high-quality data. You'll examine and practice the key steps to clean and prepare raw data, ensuring it's ready for effective machine analysis.
Once you've mastered these data preparation processes, you'll be ready to apply machine learning to healthcare analysis. You'll use supervised learning techniques to predict whether a patient is likely to experience sepsis. You'll also leverage unsupervised learning methods to identify similar subtypes within a large group of patients. By the end of the course, you'll realize how machine learning can improve efficiency for medical professionals and personalize patient care.
Students must have intermediate proficiency in Python programming and machine learning to succeed in this course.
Data is critical to the diagnosis and treatment of patients. In this course, you will examine the efficient storage, management, and processing of healthcare data, with a focus on implementing relational data models that emphasize structure, integrity, and manipulation. You'll also explore programming languages essential for querying information from relational and non-relational databases, and you'll gain proficiency in leveraging these languages to extract valuable insights from healthcare datasets through practical exercises. You'll also develop the skills to adapt to diverse data management challenges within healthcare systems.
By mastering these competencies, you will be well prepared to navigate the complex landscape of healthcare data management while ensuring compliance with regulatory standards and optimizing data-driven decision-making processes.
You are required to have completed the following courses or have equivalent experience before taking this course:
- Machine Learning in Healthcare
Clinical notes and patient records contain vast amounts of data, but this data is not always in a format machines can interpret. In this course, you will discover how natural language processing (NLP) can help you transform free text into structured data for extracting insights. You'll start by reviewing NLP methods to prepare raw text for machine analysis. Using the Python package spaCy, you'll perform NLP tasks like sentence splitting, tokenization, part-of-speech tagging, and parsing.
You will then explore key NLP applications. Using the scikit-learn and scispaCy Python packages, you'll apply text classification and named entity recognition (NER) to gain insights from medical texts. Finally, you'll advance to deep learning models, examining their application for healthcare tasks such as the de-identification of patient data. You will also consider the ethical implications of using such models, focusing on patient security and privacy. By the end of this course, you'll gain hands-on experience using NLP techniques to extract insights from healthcare data while also considering how to apply these methods ethically and responsibly.
Students must have intermediate proficiency in Python programming and machine learning to succeed in this course.
You are required to have completed the following courses or have equivalent experience before taking this course:
- Machine Learning in Healthcare
- Data Management in Healthcare
Artificial intelligence is influenced by various human factors. In this course, you will delve into the human elements that affect the design, implementation, and evaluation of tools in the healthcare industry. You'll examine how to apply user-centered and participatory design theory to potential solutions in healthcare, including the FAVES principles: fairness, appropriateness, validity, effectiveness, and safety. You'll discover how to create and assess a design based on a real-life problem, equipping you with the skills to translate theory into practice. You'll also explore how human factors impact different aspects of artificial intelligence and discover how to incorporate these considerations when designing a healthcare product or service related to digital health.
You are required to have completed the following courses or have equivalent experience before taking this course:
- Machine Learning in Healthcare
- Data Management in Healthcare
- Natural Language Processing in Healthcare
How It Works
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Faculty Authors
Yiye Zhang is an Associate Professor in the Division of Health Informatics and Faculty Director of the M.S. Program in Health Informatics at the Department of Population Health Sciences, Weill Cornell Medicine, Cornell University. She also has a secondary appointment in the Emergency Department at Weill Cornell Medicine. As a health informaticist, Dr. Zhang has years of research experience in developing tools to analyze and visualize healthcare information including electronic health records. As a principal investigator, Dr. Zhang’s research has been funded by multiple federal agencies including the National Library of Medicine of National Institute of Health, Agency for Healthcare Research and Quality, and the U.S. Department of Transportation Tier 1 University Transportation Center. Dr. Zhang received her Ph.D. degree in information systems and management at Carnegie Mellon University. She has a B.A. in mathematics from Washington University in St. Louis and an M.S. in biostatistics from Columbia University.
Yifan Peng, PhD, is an Assistant Professor in the Division of Health Sciences Department of Population Health Sciences at Weill Cornell Medicine. His main research interests include BioNLP and medical image analysis. He has published in major AI and healthcare informatics venues, including ACL, CVPR, MICCAI, and ICHI, as well as medical venues, including Nature Medicine, Nucleic Acids Research, npj Digital Medicine, and JAMIA. His research has been funded by federal agencies, including NIH and NSF, as well as by industries such as Amazon and Google. He is an Editorial Board Member for the Journal of Biomedical Informatics. He received the AMIA New Investigator Award in 2023.
Jose Florez-Arango is an educator, clinician, and researcher at the Weill Cornell Medical College. His work includes an emphasis in health informatics and emergency, disasters and pre-hospital care. Dr. Florez-Arango is focused on user-centered design of decision-support systems. He has extensive experience with the adaptation of low cost technologies to be used in healthcare. Dr. Florez-Arango holds an MD in Medicine and an MS in Biomedical Sciences from the University of Antioquia, and a PhD in Health Information and Medical Records Administration from the University of Texas Health Science Center at Houston.
Fei Wang is currently a tenured Professor of Health Informatics in Department of Population Health Sciences at Weill Cornell Medicine (WCM), where he also holds a secondary appointment as a Professor in Department of Emergency Medicine. Dr. Wang is the Founding Director of the WCM Institute of AI for Digital Health (AIDH) and an Adjunct Scientist at Hospital for Special Surgery (HSS). His research interest is machine learning and artificial intelligence in biomedicine. Dr. Wang has published over 350 papers on the major venues of AI and biomedicine, which have received more than 30K citations to date. His H-index is 83. Dr. Wang is an elected fellow of American Medical Informatics Association (AMIA), American College of Medical Informatics (ACMI) and International Academy of Health Sciences and Informatics (IAHSI), and a distinguished member of Association for Computing Machinery (ACM).
Yiye Zhang is an Associate Professor in the Division of Health Informatics and Faculty Director of the M.S. Program in Health Informatics at the Department of Population Health Sciences, Weill Cornell Medicine, Cornell University. She also has a secondary appointment in the Emergency Department at Weill Cornell Medicine. As a health informaticist, Dr. Zhang has years of research experience in developing tools to analyze and visualize healthcare information including electronic health records. As a principal investigator, Dr. Zhang’s research has been funded by multiple federal agencies including the National Library of Medicine of National Institute of Health, Agency for Healthcare Research and Quality, and the U.S. Department of Transportation Tier 1 University Transportation Center. Dr. Zhang received her Ph.D. degree in information systems and management at Carnegie Mellon University. She has a B.A. in mathematics from Washington University in St. Louis and an M.S. in biostatistics from Columbia University.
Yifan Peng, PhD, is an Assistant Professor in the Division of Health Sciences Department of Population Health Sciences at Weill Cornell Medicine. His main research interests include BioNLP and medical image analysis. He has published in major AI and healthcare informatics venues, including ACL, CVPR, MICCAI, and ICHI, as well as medical venues, including Nature Medicine, Nucleic Acids Research, npj Digital Medicine, and JAMIA. His research has been funded by federal agencies, including NIH and NSF, as well as by industries such as Amazon and Google. He is an Editorial Board Member for the Journal of Biomedical Informatics. He received the AMIA New Investigator Award in 2023.
Jose Florez-Arango is an educator, clinician, and researcher at the Weill Cornell Medical College. His work includes an emphasis in health informatics and emergency, disasters and pre-hospital care. Dr. Florez-Arango is focused on user-centered design of decision-support systems. He has extensive experience with the adaptation of low cost technologies to be used in healthcare. Dr. Florez-Arango holds an MD in Medicine and an MS in Biomedical Sciences from the University of Antioquia, and a PhD in Health Information and Medical Records Administration from the University of Texas Health Science Center at Houston.
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Key Course Takeaways
- Design, implement and evaluate machine learning models in healthcare
- Manage, process, and analyze data to prepare for AI integration in healthcare
- Process and analyze text data to prepare for AI integrations in healthcare
- Apply natural language processing and data management models to daily workflows in healthcare
Download a Brochure
Not ready to enroll but want to learn more? Download the certificate brochure to review program details.
What You'll Earn
- AI in Healthcare Certificate from Weill Cornell Graduate School of Medical Sciences
- 56 Professional Development Hours (5.6 CEUs)
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Who Should Enroll
- Data scientists
- Medical and health services managers
- Database and IT data architects
- Data engineers
- Digital transformation managers
- Clinicians with experience in informatics
- Biomedical and clinical informatics fellows
- Aspiring medical database managers or administrators
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AI in Healthcare
| Select Payment Method | Cost |
|---|---|
| $3,750 | |