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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Predicting ICU transfers using text messages between nurses and doctors
%A Khan Khattak, Faiza
%A Pou-Prom, Chloé
%A Wu, Robert
%A Rudzicz, Frank
%Y Rumshisky, Anna
%Y Roberts, Kirk
%Y Bethard, Steven
%Y Naumann, Tristan
%S Proceedings of the 2nd Clinical Natural Language Processing Workshop
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F khan-khattak-etal-2019-predicting
%X 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.
%R 10.18653/v1/W19-1911
%U https://aclanthology.org/W19-1911
%U https://doi.org/10.18653/v1/W19-1911
%P 89-94

Markdown (Informal)

[Predicting ICU transfers using text messages between nurses and doctors](https://aclanthology.org/W19-1911) (Khan Khattak et al., ClinicalNLP 2019)

• Predicting ICU transfers using text messages between nurses and doctors (Khan Khattak et al., ClinicalNLP 2019)

ACL

• Faiza Khan Khattak, Chloé Pou-Prom, Robert Wu, and Frank Rudzicz. 2019. Predicting ICU transfers using text messages between nurses and doctors. In Proceedings of the 2nd Clinical Natural Language Processing Workshop, pages 89–94, Minneapolis, Minnesota, USA. Association for Computational Linguistics.