Lesson Learned annotating training data for healthcare NLP projects
In lessons learned annotating training data for healthcare NLP projects, will explain the best strategies for getting the training data you need, the mistakes you should be able to avoid that may lower the accuracy of the model that will be trained and how to get a high IAA which will be a great way to achieve an accurate model!
The lessons learned in data annotation align with the growing applications of generative AI in healthcare, enhancing model accuracy and efficiency in various medical tasks.
Dr. Rebecca Leung graduated with a Bachelor’s degree in Medicine & Surgery from the Chinese University of Hong Kong in 2004. She has 12 years of experience working as a physician specializing in Anaesthesiology in Hong Kong. Dr. Leung recently graduated with a PhD in Health Education & Promotion from the University of Alabama and she has presented her research at the American Public Health Association conference in 2019.
She is a healthcare data annotator at John Snow Labs with a specific interest in the use of AI in health care settings.
Marianne Mak is a Healthcare Data Researcher/annotator at John Snow Labs; she is a Clinical Pharmacist with a rich background in medical research, family medicine, immunizations and business administration.
She graduated from the University of Pharmaceutical sciences with honor with a degree in Clinical Pharmacy, she has had ten years' experience in the field of research and as a community pharmacist and as a supervisor of the healthcare centers at the ministry of health helping enabling prisoners, foreigners and refugees get treated for multiple diseases, also worked in conjunction with WHO in immunization campaigns targeting a large number of people,she is currently working as a Healthcare Data researcher and annotator on different projects at John Snow Labs.
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