Computer Science > Machine Learning
[Submitted on 8 Mar 2023 (v1), last revised 1 Aug 2023 (this version, v3)]
Title:Mining the contribution of intensive care clinical course to outcome after traumatic brain injury
View PDFAbstract:Existing methods to characterise the evolving condition of traumatic brain injury (TBI) patients in the intensive care unit (ICU) do not capture the context necessary for individualising treatment. Here, we integrate all heterogenous data stored in medical records (1,166 pre-ICU and ICU variables) to model the individualised contribution of clinical course to six-month functional outcome on the Glasgow Outcome Scale - Extended (GOSE). On a prospective cohort (n=1,550, 65 centres) of TBI patients, we train recurrent neural network models to map a token-embedded time series representation of all variables (including missing values) to an ordinal GOSE prognosis every two hours. The full range of variables explains up to 52% (95% CI: 50%-54%) of the ordinal variance in functional outcome. Up to 91% (95% CI: 90%-91%) of this explanation is derived from pre-ICU and admission information (i.e., static variables). Information collected in the ICU (i.e., dynamic variables) increases explanation (by up to 5% [95% CI: 4%-6%]), though not enough to counter poorer overall performance in longer-stay (>5.75 days) patients. Highest-contributing variables include physician-based prognoses, CT features, and markers of neurological function. Whilst static information currently accounts for the majority of functional outcome explanation after TBI, data-driven analysis highlights investigative avenues to improve dynamic characterisation of longer-stay patients. Moreover, our modelling strategy proves useful for converting large patient records into interpretable time series with missing data integration and minimal processing.
Submission history
From: Shubhayu Bhattacharyay [view email][v1] Wed, 8 Mar 2023 14:48:30 UTC (10,548 KB)
[v2] Fri, 2 Jun 2023 22:25:26 UTC (15,199 KB)
[v3] Tue, 1 Aug 2023 21:58:45 UTC (15,102 KB)
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