Faiza Khan Khattak


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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.

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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.