Autorenverzeichnis

MEd,; Gregor, Olaf; Köhler, Wolfgang; Näher-Noé, Martina; Rege, Susan; Seelig, Daniela; nat, med. Dr. rer.; Lezaic, Vesna; Schönherr, Sabine; med.,

doi:10.1016/b978-3-437-23191-9.09991-7

Cited by 1 publication

(2 citation statements)

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“…GCS median (IQR) 3 [3][4] mean imputation. For machine learning models that support missing data natively, like decision trees or tree ensembles, missing data imputation/normalization was not performed.…”

Section: Multi-scale Historymentioning

confidence: 99%

“…All experimental result tables report these 4 metrics as well as areas under the ROC/PR curves. 95% standard-errorbased confidence intervals of performance metrics, which are used in all figures/tables, were constructed by drawing 10 randomized train/validation/test splits (proportion 40:20:40%) with respect to complete recording Energy in frequency bands [0,1], [1,2], [2,3], [3,6] Training, validation and test sets contained 49, 25, and 49 segments, respectively. Splits were stratified, such that the label prevalence difference between training, validation, test sets does not exceed 0.015 in each split.…”

Section: Experimental Designmentioning

confidence: 99%

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Forecasting intracranial hypertension using multi-scale waveform metrics

et al. 2020

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Objective: Acute intracranial hypertension is an important risk factor of secondary brain damage after traumatic brain injury. Hypertensive episodes are often diagnosed reactively, leading to late detection and lost time for intervention planning. A pro-active approach that predicts critical events several hours ahead of time could assist in directing attention to patients at risk. Approach: We developed a prediction framework that forecasts onsets of acute intracranial hypertension in the next 8 hours. It jointly uses cerebral auto-regulation indices, spectral energies and morphological pulse metrics to describe the neurological state of the patient. One-minute base windows were compressed by computing signal metrics, and then stored in a multi-scale history, from which physiological features were derived. Main results: Our model predicted events up to 8 hours in advance with alarm recall rates of 90% at a precision of 30.3% in the MIMIC-III waveform database, improving upon two baselines from the literature. We found that features derived from high-frequency waveforms substantially improved the prediction performance over simple statistical summaries of low-frequency time series, and each of the three feature classes contributed to the performance gain. The inclusion of long-term history up to 8 hours was especially important. Significance: Our results highlight the importance of information contained in high-frequency waveforms in the neurological intensive care unit. They could motivate future studies on pre-hypertensive patterns and the design of new alarm algorithms for critical events in the injured brain.

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Section: Multi-scale Historymentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%