Скачать или смотреть Improvement and Innovation Showcase 37: Natural Language Processing

Samuel Gluck, Medical Administration Registrar, Northern Adelaide Local Health Network

The ability to predict when a patient is likely to be discharged will help clinical teams to ensure the patient is ready. Sam's solution uses RAH data to derive an NLP algorithm, validated in both the RAH and QE, that predicts the likelihood of discharge within 48 hours. Sam's team is conducting an implementation study at RAH and QEH to assist with identifying patients who will be ready for discharge at the weekend.
Sam Gluck is a duel RACMA and CICM trainee. He grew up in Wales and trained in Cambridge, undertaking anaesthetic training in the NHS prior to emigrating to Australia in 2013. He has just completed a PhD in the use of passive smartphone data in the measurement of patient outcomes. Sam is well published in machine learning and natural language processing and when not working he can be found restoring a small part of the Adelaide Hills to native bushland with his wife and 2 young sons.