Easily Created Prediction Model Using Automated Artificial Intelligence Framework (Prediction One, Sony Network Communications Inc., Tokyo, Japan) for Subarachnoid Hemorrhage Outcomes Treated by Coiling and Delayed Cerebral Ischemia
- PMID: 34277282
- PMCID: PMC8281789
- DOI: 10.7759/cureus.15695
Easily Created Prediction Model Using Automated Artificial Intelligence Framework (Prediction One, Sony Network Communications Inc., Tokyo, Japan) for Subarachnoid Hemorrhage Outcomes Treated by Coiling and Delayed Cerebral Ischemia
Abstract
Introduction Reliable prediction models of subarachnoid hemorrhage (SAH) outcomes and delayed cerebral ischemia (DCI) are needed to decide the treatment strategy. Automated artificial intelligence (AutoAI) is attractive, but there are few reports on AutoAI-based models for SAH functional outcomes and DCI. We herein made models using an AutoAI framework, Prediction One (Sony Network Communications Inc., Tokyo, Japan), and compared it to other previous statistical prediction scores. Methods We used an open dataset of 298 SAH patients, who were with non-severe neurological grade and treated by coiling. Modified Rankin Scale 0-3 at six months was defined as a favorable functional outcome and DCI occurrence as another outcome. We randomly divided them into a 248-patient training dataset and a 50-patient test dataset. Prediction One made the model using training dataset with 5-fold cross-validation. We evaluated the model using the test dataset and compared the area under the curves (AUCs) of the created models. Those of the modified SAFIRE score and the Fisher computed tomography (CT) scale to predict the outcomes. Results The AUCs of the AutoAI-based models for functional outcome in the training and test dataset were 0.994 and 0.801, and those for the DCI occurrence were 0.969 and 0.650. AUCs for functional outcome calculated using modified SAFIRE score were 0.844 and 0.892. Those for the DCI occurrence calculated using the Fisher CT scale were 0.577 and 0.544. Conclusions We easily and quickly made AutoAI-based prediction models. The models' AUCs were not inferior to the previous prediction models despite the easiness.
Keywords: automated artificial intelligence (autoai); deep learning (dl); delayed cerebral ischemia (dci); machine learning (ml); subarachnoid hemorrhage (sah).
Copyright © 2021, Katsuki et al.
Conflict of interest statement
The authors have declared that no competing interests exist.
References
-
- Clinical prediction models for aneurysmal subarachnoid hemorrhage: a systematic review. Jaja BN, Cusimano MD, Etminan N, et al. Neurocrit Care. 2013;18:143–153. - PubMed
-
- Mortality after spontaneous subarachnoid hemorrhage: causality and validation of a prediction model. Abulhasan YB, Alabdulraheem N, Simoneau G, Angle MR, Teitelbaum J. World Neurosurg. 2018;112:0–811. - PubMed
-
- Predicting outcome in subarachnoid hemorrhage (SAH) utilizing the Full Outline of UnResponsiveness (FOUR) Score. Zeiler FA, Lo BWY, Akoth E, Silvaggio J, Kaufmann AM, Teitelbaum J, West M. Neurocrit Care. 2017;27:381–389. - PubMed
-
- Validation and optimization of barrow neurological institute score in prediction of adverse events and functional outcome after subarachnoid hemorrhage—Creation of the HATCH (Hemorrhage, Age, Treatment, Clinical State, Hydrocephalus) score. Hostettler IC, Sebök M, Ambler G, et al. Neurosurgery. 2020;88:96–105. - PubMed
LinkOut - more resources
Full Text Sources