Prediction of Life-Threatening Intracranial Hypertension during the Acute Phase of Traumatic Brain Injury Using Machine Learning

Hack Jin Lee, Hakseung Kim, Tak Kim, Kanghee Won, Marek Czosnyka, Dong Joo Kim

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


Intracranial hypertension (IH) following acute phase traumatic brain injury (TBI) is associated with high mortality. Objective: This study proposes a novel parameter that may identify a potentially life-threatening IH (LTH) event and designs a machine learning model to predict LTH. Continuous recordings of intracranial pressure (ICP) and arterial blood pressure (ABP) from 273 TBI patients were used as the development dataset. The pressure-time dose (PTD) and pressure reactivity index (PRx) were calculated for each IH event, and an IH event with PRx > 0 and PTD > 5 was considered an LTH event. The association between the LTH parameters accumulated over five days and mortality was analyzed. A categorical boosting (CatBoost) model was employed to predict the occurrence of a future LTH event from the onset of IH using the ABP- and ICP-related parameters. Training and validation were performed on a total of 5,938 IH events. External performance evaluation was performed in 307 IH events included in the Cerebral Haemodynamic Autoregulatory Information System (CHARIS) database. The performance of the proposed model was evaluated through the area under the receiver operating characteristic curve (AUROC). The LTH parameters were able to distinguish between the deceased and surviving patients (AUROC > 0.7, p < 0.001). The CatBoost model predicted LTH with an AUROC = 0.7 on the external test dataset. This study demonstrated that the proposed LTH prediction model has a reasonable predictive capacity for mortality. The CatBoost model anticipates whether an IH event will develop into an LTH event. The findings of this study support the usefulness of ICP monitoring.

Original languageEnglish
Pages (from-to)3967-3976
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Issue number10
Publication statusPublished - 2021 Oct 1

Bibliographical note

Funding Information:
Manuscript received July 26, 2020; revised October 27, 2020, February 24, 2021, and April 9, 2021; accepted May 27, 2021. Date of publication June 1, 2021; date of current version October 5, 2021. This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science and ICT, MSIT) under Grants 2019R1A2C1003399 and 2020R1C1C1006773, and in part by a Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) funded by the Ministry of Health & Welfare, Republic of Korea under Grant HI17C1561; and a Korea University Grant. (Corresponding author: Dong-Joo Kim.) Hack-Jin Lee is with the Department of Brain and Cognitive Engineering, Korea University, 02841, South Korea, and also with the R&D Team, DoAI Inc., 13449, South Korea (e-mail:

Publisher Copyright:
© 2013 IEEE.


  • Clinical outcome
  • Intracranial hypertension
  • Machine learning
  • Neuromonitoring
  • Traumatic brain injury

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics
  • Electrical and Electronic Engineering
  • Health Information Management


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