@inproceedings{d17af395fbba4e29bf35c50c09fcdfc0,
title = "Treatment Prediction in the ICU Setting Using a Partitioned, Sequential Deep Time Series Analysis",
abstract = "We developed a neural network architecture to evaluate the patient's state using temporal data, patient's demographics and comorbidities. We examined the model's ability to predict both a binary medication-treatment decision and its specific dose in three common scenarios: hypokalemia, hypoglycemia and hypotension. We partition the common 12-hours horizon window into three sub-windows, examining how patterns of treatment evolve following a key clinical event or state. This partitioned analysis also helps in alleviating the problem of small data sets, by utilizing previous sub-windows' data as additional training data. We also propose a solution to the problem of the relative inability of dose-prediction models to output a 'no treatment' classification, through the use of sequential prediction.",
keywords = "Decision support, ICU, Temporal deep learning",
author = "Michael Shapiro and Yuval Shahar",
note = "Publisher Copyright: {\textcopyright} 2022 The authors and IOS Press.",
year = "2022",
month = jan,
day = "1",
doi = "10.3233/SHTI220738",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "360--361",
editor = "John Mantas and Parisis Gallos and Emmanouil Zoulias and Arie Hasman and Househ, \{Mowafa S.\} and Marianna Diomidous and Joseph Liaskos and Martha Charalampidou",
booktitle = "Advances in Informatics, Management and Technology in Healthcare",
address = "Netherlands",
}