Faiza Khan Khattak


Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support
Stephen Obadinma | Faiza Khan Khattak | Shirley Wang | Tania Sidhorn | Elaine Lau | Sean Robertson | Jingcheng Niu | Winnie Au | Alif Munim | Karthik Raja Kalaiselvi Bhaskar
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track

Building Agent Assistants that can help improve customer service support requires inputs from industry users and their customers, as well as knowledge about state-of-the-art Natural Language Processing (NLP) technology. We combine expertise from academia and industry to bridge the gap and build task/domain-specific Neural Agent Assistants (NAA) with three high-level components for: (1) Intent Identification, (2) Context Retrieval, and (3) Response Generation. In this paper, we outline the pipeline of the NAA’s core system and also present three case studies in which three industry partners successfully adapt the framework to find solutions to their unique challenges. Our findings suggest that a collaborative process is instrumental in spurring the development of emerging NLP models for Conversational AI tasks in industry. The full reference implementation code and results are available at https://github.com/VectorInstitute/NAA.


Predicting ICU transfers using text messages between nurses and doctors
Faiza Khan Khattak | Chloé Pou-Prom | Robert Wu | Frank Rudzicz
Proceedings of the 2nd Clinical Natural Language Processing Workshop

We explore the use of real-time clinical information, i.e., text messages sent between nurses and doctors regarding patient conditions in order to predict transfer to the intensive care unit(ICU). Preliminary results, in data from five hospitals, indicate that, despite being short and full of noise, text messages can augment other visit information to improve the performance of ICU transfer prediction.

Extracting relevant information from physician-patient dialogues for automated clinical note taking
Serena Jeblee | Faiza Khan Khattak | Noah Crampton | Muhammad Mamdani | Frank Rudzicz
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)

We present a system for automatically extracting pertinent medical information from dialogues between clinicians and patients. The system parses each dialogue and extracts entities such as medications and symptoms, using context to predict which entities are relevant. We also classify the primary diagnosis for each conversation. In addition, we extract topic information and identify relevant utterances. This serves as a baseline for a system that extracts information from dialogues and automatically generates a patient note, which can be reviewed and edited by the clinician.