Reut Apel

Bridging the Gap: Extracting Patient Life Events from Medical Chats to Empower Healthcare Providers
Reut Apel


Reut Apel is a data scientist at K Health, dedicated to advancing AI and NLP technologies to revolutionize healthcare. Reut holds a PhD in Data Science from the Technion, where she conducted pioneering research at the intersection of game theory, psychology, and NLP, and published papers in both AI and NLP journals. She was one of the first three students and the first female student of the Technion Data Science and Engineering BSc program. Prior to joining K Health, Reut worked as a data scientist at Intel and as an NLP researcher at Microsoft. At K Health, Reut works on the company’s next-generation AI chatbot and develops NLP-driven solutions to empower healthcare providers.


Have you ever thought about how life events, such as changes in family status or work, can impact your health and whether your doctor is aware of them and takes them into consideration in your treatment? In the realm of healthcare, patient-reported information often holds crucial insights into individual health experiences and needs. However, a significant challenge arises when such information, particularly non-medical life events, remains confined within the conversational context between the patient and the doctor without being documented in official medical records. This lack of documentation creates a gap in the continuity of care, hindering effective decision-making and personalized interventions.

This lecture will discuss how we addressed this challenge at K Health by leveraging Large Language Models (LLMs) to extract patient life events from unstructured chat data. Our approach involves structuring the output to include both structured data and relevant free text, capturing the nuances of patient-reported information. However, evaluating such models poses unique challenges. One key challenge is the lack of labeled data, which complicates the evaluation process. Additionally, we face the task of structuring the data while preserving the richness of patient-reported information. Furthermore, there is a need to balance between extracting correct life events without overwhelming doctors with irrelevant information, while ensuring that all relevant events are captured to provide a comprehensive view of the patient’s health history.

Overcoming these challenges is crucial in empowering healthcare providers with comprehensive patient insights beyond conventional medical records, thereby enhancing continuity of care and supporting informed decision-making in clinical practice.


8:45 Reception
9:30 Opening remarks by WiDS TLV ambassadors Noah Eyal Altman, Or Basson, and Nitzan Gado
9:45 Dr. Aya Soffer, IBM: "Putting Generative AI to Work: What Have We Learned So Far?"
10:15 Prof. Reut Tsarfaty, Bar-llan University: "Will Hebrew Speakers Be Able to Use Generative AI in Their Native Tongue?"
10:45 Poster Pitches
10:55 Break
11:10 Lightning talks
12:30 Lunch & poster session
13:30 Roundtable session & poster session
14:15 Roundtable closing
14:30 Break
14:40 Naomi Ken Korem, Lightricks: "Mastering the Art of Generative Models: Training and Controlling Text-to-Video Models"
15:00 Dr. Yael Mathov, Intuit: "Surviving the AI-pocalypse: Your Guide to LLM Security"
15:20 Closing remarks
15:30 The end