Predicting Outcome in Patients with Brain Injury: Differences between Machine Learning versus Conventional Statistics

Abstract

Defining reliable tools for early prediction of outcome is the main target for physicians to guide care decisions in patients with brain injury. The application of machine learning (ML) is rapidly increasing in this field of study, but with a poor translation to clinical practice. This is basically dependent on the uncertainty about the advantages of this novel technique with respect to traditional approaches. In this review we address the main differences between ML techniques and traditional statistics (such as logistic regression, LR) applied for predicting outcome in patients with stroke and traumatic brain injury (TBI). Thirteen papers directly addressing the different performance among ML and LR methods were included in this review. Basically, ML algorithms do not outperform traditional regression approaches for outcome prediction in brain injury. Better performance of specific ML algorithms (such as Artificial neural networks) was mainly described in the stroke domain, but the high heterogeneity in features extracted from low-dimensional clinical data reduces the enthusiasm for applying this powerful method in clinical practice. To better capture and predict the dynamic changes in patients with brain injury during intensive care courses ML algorithms should be extended to high-dimensional data extracted from neuroimaging (structural and fMRI), EEG and genetics.

Modeling outcome trajectories in patients with acquired brain injury using a non-linear dynamic evolution approach

“…The only two patients for whom it was not feasible to establish the possible outcome had missing values at T1. The performance reported is in line with several previous studies using machine learning algorithms to predict outcomes in acquired brain injury 8 . In a previous study (Bruschetta et al 2022) different ML approaches were compared with the classic Linear Model (LM) to predict the final evolution of TBI patients according to 2 and 4 GOS classes based on the clinical assessment at T0.…”

“…linear regression models) 7 . In fact, a recent narrative review showed that ML algorithms do not outperform traditional regression approaches for outcome prediction in brain injury 8 .…”

Modeling the outcome trajectories in patients with acquired brain injury: a non-linear dynamic evolution approach

“…The only two patients for whom it was not feasible to establish the possible outcome had missing values at T1. The reported performance is in line with several previous studies using machine learning algorithms to predict outcomes in acquired brain injury (Cerasa et al, 2022). In a previous study (Bruschetta et al, 2022) different ML approaches were compared with the classic Linear Model (LM) to predict the nal evolution of TBI patients according to 2 and 4 GOS classes based on the clinical assessment at T0.…”

“…This is basically dependent on the uncertainty about the intrinsically ''black-box'' nature of this approach and the lack of clear advantages with respect to traditional approaches (i.e linear regression models) (Quinn et al, 2022). Indeed, a recent narrative review showed that ML algorithms do not outperform traditional regression approaches for outcome prediction in brain injury (Cerasa et al, 2022).…”

“…) and clinical factors (i.e, initial severity of disability, radiological markers; comorbidities) as well as the extent of improvement observed within the rst days/weeks post-injury are considered the main indicators of the outcome at six-months(Kwakkel & Kollen, 2013), although their magnitude in in uencing predictive models change as a function of statistical approach(Cerasa et al, 2022). In our study, we demonstrated that the ensemble of feeding modality, RLAS, ERBI_A, Tracheostomy, CRS-r and ERBI-B is able to de ne the time course of clinical evolution in ABI patients who will be discharged with a Positive or Negative outcome, reaching the best classi cation of the subjects and a high accuracy rate starting from the 86th days post-injury.…”