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

Evidence and unknowns for the relevancy of applying current parenteral nutrition support recommendations among infants born less than 750 g or younger than 25 weeks' gestation: A narrative review

With advancements in neonatal care, the viability of extremely low-birth-weight (ELBW) infants, especially those born extremely preterm, is increasing. However, specific recommendations for managing parenteral nutrition (PN) support in nanopreterm infants (<750 g or <25 weeks' gestation) are lacking. We aim to evaluate current recommendations and highlight considerations for applying them to nanopreterm infants. The author team used English-language articles related to nutrition in ELBW with emphasis on nanopreterm infants, along with studies on fetal growth and metabolism. Current PN support recommendations for ELBW infants may not suit nanopreterm infants due to physiological and developmental differences. Key considerations for nanopreterm infants include: Carbohydrate: They require immediate dextrose provision with low glucose infusion rates because of limited glycogen stores, immature gluconeogenesis, and impaired glucose intolerance. Lipids: Although essential for energy storage and cell membrane integrity, the ability to metabolize them may be limited, requiring careful consideration of lipid injectable emulsion provision and dosing. Protein: Protein is crucial for growth and development. However, achieving euglycemia is essential for proper amino acid utilization, requiring a delicate balance of dextrose and protein provision. Energy: Because of lower muscle and tissue mass and immature metabolic capabilities, existing recommendations may overestimate their energy needs. Micronutrients: Exact micronutrient requirements are unknown during this specific period of fetal development. This review highlights the limitations of available PN support recommendations for nanopreterm infants. Further research is needed to establish precise guidelines that optimally meet their nutrition needs.

MetaRehabVerse: The Great Opportunity to Put the Person's Functioning and Participation at the Center of Healthcare

BACKGROUND AND OBJECTIVE

The metaverse refers to a digital realm accessible via internet connections using virtual reality and augmented reality glasses for promoting a new era of social rehabilitation. It represents the next-generation mobile computing platform expected to see widespread utilization in the future. In the context of rehabilitation, the metaverse is envisioned as a novel approach to enhance the treatment of human functioning exploiting the "synchronized brains" potential exacerbated by social interactions in virtual scenarios.

RESULTS

The metaverse emerges as an ideal domain for adapting the principles of the-International Classification of Functioning. Its intrinsic capacity to simulate interactions within virtual environments shared by multi-users, while providing a profound sense of presence and comprehensive perception, should facilitate learning and experiential understanding. Technical and conceptual aspects are currently under definition, including the interplay with artificial intelligence, definition of social metrics performance, and the utilization of blockchain technology for economic purposes.

CONCLUSION

Building upon these foundations, this paper explores potential areas of metaverse applications in rehabilitation and examines how they may facilitate the pillars outlined in the World Health Organization's Rehabilitation 2030 call for action.

Use of machine learning algorithms to construct models of symptom burden cluster risk in breast cancer patients undergoing chemotherapy

PURPOSE

To develop models using different machine learning algorithms to predict high-risk symptom burden clusters in breast cancer patients undergoing chemotherapy, and to determine an optimal model.

METHODS

Data from 647 breast cancer patients were analyzed to develop a model predicting high-risk symptom burden clusters. Five machine learning algorithms, including an artificial neural network (ANN), a decision tree (DT), a support vector machine (SVM), a random forest (RF), and extreme gradient boosting (XGBoost), were tested, as was traditional logistic regression. Performance was evaluated by deriving the predictive accuracy, precision, discriminatory capacity, calibration, and clinical utility, and an optimal model was identified.

RESULTS

A model based on the RF algorithm exhibited better accuracy, precision, and discriminatory capacity than the other models. The area under the receiver operator curve was 0.91, the sensitivity was 65.8%, the specificity was 93.5%, the positive predictive value was 98.02%, and the false positive rate was only 0.91%.

CONCLUSION

The model created using the RF algorithm was excellent in terms of predictive accuracy and precision, and can be used for early identification of the risk of self-reported symptom burden clusters in breast cancer patients undergoing chemotherapy.

Development and validation of a model to predict cognitive impairment in traumatic brain injury patients: a prospective observational study

Background

Traumatic brain injury (TBI) is a significant public health issue worldwide that affects millions of people every year. Cognitive impairment is one of the most common long-term consequences of TBI, seriously affect the quality of life. We aimed to develop and validate a predictive model for cognitive impairment in TBI patients, with the goal of early identification and support for those at risk of developing cognitive impairment at the time of hospital admission.

Methods

The training cohort included 234 TBI patients, all of whom were admitted to the Department of Neurosurgery at the Third Affiliated Hospital of Soochow University from May 2017 to April 2020. These patients were selected from our previously published studies. Baseline characteristics, medical history, clinical TBI characteristics, treatment details, and vital signs during hospitalization were screened via least absolute shrinkage and selection operator (LASSO) and logistic regression to construct a predictive net risk score. The derived score represents an estimate of the risk of developing cognitive impairment in patients with TBI. A nomogram was constructed, and its accuracy and predictive performance were evaluated with the area under the receiver operating characteristic curve (AUC), calibration curves, and clinical decision curves. For the validation cohort, data were prospectively collected from TBI patients admitted to the Department of Neurosurgery at the Third Affiliated Hospital of Soochow University from March 1, 2024 to August 30, 2024, according to the inclusion and exclusion criteria. This study is registered with the Chinese Clinical Trial Registry (ChiCTR) at http://www.chictr.org.cn/ (registration number: ChiCTR2400083495).

Findings

The training cohort included 234 patients. The mean (standard deviation, SD) age of the patients in the cohort was 47.74 (17.89) years, and 184 patients (78.63%) were men. The validation cohort included 84 patients with a mean (SD) age of 48.44 (14.42) years, and 68 patients (80.95%) were men. Among the 48 potential predictors, the following 6 variables were significant independent predictive factors and were included in the net risk score: age (odds ratio (OR) = 1.06, 95% confidence interval (CI): 1.03-1.08, P = 0.00), years of education (OR = 0.80, 95% CI: 0.70-0.93, P = 0.00), pulmonary infection status (OR = 4.64, 95% CI: 1.41-15.27, P = 0.01), epilepsy status (OR = 4.79, 95% CI: 1.09-21.13, P = 0.04), cerebrospinal fluid leakage status (OR = 5.57, 95% CI: 1.08-28.75, P = 0.04), and the Helsinki score (OR = 1.53, 95% CI: 1.28-1.83, P = 0.00). The AUC in the training cohort was 0.90, and the cut-off value was 0.71. The AUC in the validation cohort was 0.87, and the cut-off value was 0.63. The score was translated into an online risk calculator that is freely available to the public (https://yuanxiaofang.shinyapps.io/Predict_cognitive_impairment_in_TBI/).

Interpretation

This model for predicting post-TBI cognitive impairment has potential value for facilitating early predictions by clinicians, aiding the early initiation of preventative interventions for cognitive impairment.

Funding

This research was supported by Science and Technology Development Plan Project of ChangZhou (CJ20229036); Science and Technology Project of Changzhou Health Commission (QN202113).

Comparison of Machine Learning Models in Predicting Mental Health Sequelae Following Concussion in Youth

Youth who experience concussions may be at greater risk for subsequent mental health challenges, making early detection crucial for timely intervention. This study utilized Bidirectional Long Short-Term Memory (BiLSTM) networks to predict mental health outcomes following concussion in youth and compared its performance to traditional models. We also examined whether incorporating social determinants of health (SDoH) improved predictive power, given the disproportionate impact of concussions and mental health issues on disadvantaged populations. We evaluated the models using accuracy, area under the curve (AUC) of the receiver operating characteristic (ROC), and other performance metrics. Our BiLSTM model with SDoH data achieved the highest accuracy (0.883) and AUC-ROC score (0.892). Unlike traditional models, our approach provided real-time predictions at each visit within 12 months of the index concussion, aiding clinicians in making timely, visit-specific referrals for further treatment and interventions.

Predicting upper limb motor recovery in subacute stroke patients via fNIRS-measured cerebral functional responses induced by robotic training

BACKGROUND

Neural activation induced by upper extremity robot-assisted training (UE-RAT) helps characterize adaptive changes in the brains of poststroke patients, revealing differences in recovery potential among patients. However, it remains unclear whether these task-related neural activities can effectively predict rehabilitation outcomes. In this study, we utilized functional near-infrared spectroscopy (fNIRS) to measure participants' neural activity profiles during resting and UE-RAT tasks and developed models via machine learning to verify whether task-related functional brain responses can predict the recovery of upper limb motor function.

METHODS

Cortical activation and brain network functional connectivity (FC) in brain regions such as the superior frontal cortex, premotor cortex, and primary motor cortex were measured using fNIRS in 82 subacute stroke patients in the resting state and during UE-RAT. The Fugl-Meyer Upper Extremity Assessment Scale (FMA-UE) was chosen as the index for assessing upper extremity motor function, and clinical information such as demographic and neurophysiological data was also collected. Robust features were screened in 100 randomly divided training sets using the least absolute shrinkage and selection operator (LASSO) method. Based on the selected robust features, machine learning algorithms were used to develop clinical models, fNIRS models, and combined models that integrated both clinical and fNIRS features. Finally, Shapley Additive Explanations (SHAP) was applied to interpret the prediction process and analyze key predictive factors.

RESULTS

Compared to the resting state, task-related FC is a more robust feature for modeling, with screening frequencies above 90%. The combined models built using artificial neural networks (ANNs) and support vector machines (SVMs) significantly outperformed the other algorithms, with an average AUC of 0.861 (± 0.087) for the ANN and an average correlation coefficient (r) of 0.860 (± 0.069) for the SVM. Furthermore, predictive factor analysis of the models revealed that FC measured during tasks is the most important factor for predicting upper limb motor function.

CONCLUSION

This study confirmed that UE-RAT-induced FC can serve as an important predictor of rehabilitation, especially when combined with clinical information, further enhancing the accuracy of model predictions. These findings provide new insights for the early prediction of patients' recovery potential, which may contribute to personalized rehabilitation decisions.

The Role of Psychological Variables in Predicting Rehabilitation Outcomes After Spinal Cord Injury: An Artificial Neural Networks Study

Background: Accurate prediction of neurorehabilitation outcomes following Spinal Cord Injury (SCI) is crucial for optimizing healthcare resource allocation and improving rehabilitation strategies. Artificial Neural Networks (ANNs) may identify complex prognostic factors in patients with SCI. However, the influence of psychological variables on rehabilitation outcomes remains underexplored despite their potential impact on recovery success. Methods: A cohort of 303 patients with SCI was analyzed with an ANN model that employed 17 input variables, structured into two hidden layers and a single output node. Clinical and psychological data were integrated to predict functional outcomes, which were measured by the Spinal Cord Independence Measure (SCIM) at discharge. Paired Wilcoxon tests were used to evaluate pre-post differences and linear regression was used to assess correlations, with Pearson's coefficient and the Root Mean Square Error calculated. Results: Significant improvements in SCIM scores were observed (21.8 ± 15.8 at admission vs. 57.4 ± 22.5 at discharge, p < 0.001). The model assigned the highest predictive weight to SCIM at admission (10.3%), while psychological factors accounted for 36.3%, increasing to 40.9% in traumatic SCI cases. Anxiety and depression were the most influential psychological predictors. The correlation between the predicted and actual SCIM scores was R = 0.794 for the entire sample and R = 0.940 for traumatic cases. Conclusions: The ANN model demonstrated the strong impact, especially for traumatic SCI, of psychological factors on functional outcomes. Anxiety and depression emerged as dominant negative predictors. Conversely, self-esteem and emotional regulation functioned as protective factors increasing functional outcomes. These findings support the integration of psychological assessments into predictive models to enhance accuracy in SCI rehabilitation outcomes.

Life-Course Multidisciplinary Psychosocial Predictors of Dementia Among Older Adults: Results From the Health and Retirement Study

Background and Objectives

Identifying predictors of dementia may help improve risk assessments, increase awareness for risk reduction, and identify potential targets for interventions. We use a life-course psychosocial multidisciplinary modeling framework to examine leading predictors of dementia incidence.

Research Design and Methods

We use data from the Health and Retirement Study to measure 57 psychosocial factors across 7 different domains: (i) demographics, (ii) childhood experiences, (iii) socioeconomic conditions, (iv) health behaviors, (v) social connections, (vi) psychological characteristics, and (vii) adverse adulthood experiences. Our outcome is dementia incidence (over 8 years) operationalized using Langa-Weir classification for adults aged 65+ years who meet criteria for normal cognition at the baseline when all psychosocial factors are measured (N = 1 784 in training set and N = 1 611 in testing set). We compare the standard statistical method (Logistic regression) with machine learning (ML) method (Random Forest) in identifying predictors across the disciplines of interest.

Results

Standard and ML methods identified predictors that spanned multiple disciplines. The standard statistical methods identified lower education and childhood financial duress as among the leading predictors of dementia incidence. The ML method differed in their identification of predictors.

Discussion and Implications

The findings emphasize the importance of upstream risk and protective factors and the long-reaching impact of childhood experiences on cognitive health. The ML approach highlights the importance of life-course multidisciplinary frameworks for improving evidence-based interventions for dementia. Further investigations are needed to identify how complex interactions of life-course factors can be addressed through interventions.

Classifying Residual Stroke Severity Using Robotics-Assisted Stroke Rehabilitation: Machine Learning Approach

BACKGROUND

Stroke therapy is essential to reduce impairments and improve motor movements by engaging autogenous neuroplasticity. Traditionally, stroke rehabilitation occurs in inpatient and outpatient rehabilitation facilities. However, recent literature increasingly explores moving the recovery process into the home and integrating technology-based interventions. This study advances this goal by promoting in-home, autonomous recovery for patients who experienced a stroke through robotics-assisted rehabilitation and classifying stroke residual severity using machine learning methods.

OBJECTIVE

Our main objective is to use kinematics data collected during in-home, self-guided therapy sessions to develop supervised machine learning methods, to address a clinician's autonomous classification of stroke residual severity-labeled data toward improving in-home, robotics-assisted stroke rehabilitation.

METHODS

In total, 33 patients who experienced a stroke participated in in-home therapy sessions using Motus Nova robotics rehabilitation technology to capture upper and lower body motion. During each therapy session, the Motus Hand and Motus Foot devices collected movement data, assistance data, and activity-specific data. We then synthesized, processed, and summarized these data. Next, the therapy session data were paired with clinician-informed, discrete stroke residual severity labels: "no range of motion (ROM)," "low ROM," and "high ROM." Afterward, an 80%:20% split was performed to divide the dataset into a training set and a holdout test set. We used 4 machine learning algorithms to classify stroke residual severity: light gradient boosting (LGB), extra trees classifier, deep feed-forward neural network, and classical logistic regression. We selected models based on 10-fold cross-validation and measured their performance on a holdout test dataset using F1-score to identify which model maximizes stroke residual severity classification accuracy.

RESULTS

We demonstrated that the LGB method provides the most reliable autonomous detection of stroke severity. The trained model is a consensus model that consists of 139 decision trees with up to 115 leaves each. This LGB model boasts a 96.70% F1-score compared to logistic regression (55.82%), extra trees classifier (94.81%), and deep feed-forward neural network (70.11%).

CONCLUSIONS

We showed how objectively measured rehabilitation training paired with machine learning methods can be used to identify the residual stroke severity class, with efforts to enhance in-home self-guided, individualized stroke rehabilitation. The model we trained relies only on session summary statistics, meaning it can potentially be integrated into similar settings for real-time classification, such as outpatient rehabilitation facilities.

Evaluation of Rehabilitation Outcomes in Patients with Chronic Neurological Health Conditions Using a Machine Learning Approach

Background: Over one billion people worldwide suffer from neurological conditions that cause mobility impairments, often persisting despite rehabilitation. Chronic neurological disease (CND) patients who lack access to continuous rehabilitation face gradual functional decline. The International Classification of Functioning, Disability, and Health (ICF) provides a comprehensive framework for assessing these patients. Objective: This study aims to evaluate the outcomes of a non-hospitalized neuromotor rehabilitation project for CND patients in Italy using the Barthel Index (BI) as the primary outcome measure. The rehabilitation was administered through an Individual Rehabilitation Plan (IRP), tailored by a multidisciplinary team and coordinated by a physiatrist. The IRP involved an initial comprehensive assessment, individualized therapy administered five days a week, and continuous adjustments based on patient progress. The secondary objectives include assessing mental status and sensory and communication functions, and identifying predictive factors for BI improvement using an artificial neural network (ANN). Methods: A retrospective observational study of 128 CND patients undergoing a rehabilitation program between 2018 and 2023 was conducted. Variables included demographic data, clinical assessments (BI, SPMSQ, and SVaMAsc), and ICF codes. Data were analyzed using descriptive statistics, linear regressions, and ANN to identify predictors of BI improvement. Results: Significant improvements in the mean BI score were observed from admission (40.28 ± 29.08) to discharge (42.53 ± 30.02, p < 0.001). Patients with severe mobility issues showed the most difficulty in transfers and walking, as indicated by the ICF E codes. Females, especially older women, experienced more cognitive decline, affecting rehabilitation outcomes. ANN achieved 86.4% accuracy in predicting BI improvement, with key factors including ICF mobility codes and the number of past rehabilitation projects. Conclusions: The ICF mobility codes are strong predictors of BI improvement in CND patients. More rehabilitation sessions and targeted support, especially for elderly women and patients with lower initial BI scores, can enhance outcomes and reduce complications. Continuous rehabilitation is essential for maintaining progress in CND patients.

Prediction of poststroke independent walking using machine learning: a retrospective study

BACKGROUND

Accurately predicting the walking independence of stroke patients is important. Our objective was to determine and compare the performance of logistic regression (LR) and three machine learning models (eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Random Forest (RF)) in predicting walking independence at discharge in stroke patients, as well as to explore the variables that predict prognosis.

METHODS

778 (80% for the training set and 20% for the test set) stroke patients admitted to China Rehabilitation Research Center between February 2020 and January 2023 were retrospectively included. The training set was used for training models. The test set was used to validate and compare the performance of the four models in terms of area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score.

RESULTS

Among the three ML models, the AUC of the XGBoost model is significantly higher than that of the SVM and RF models (P < 0.001, P = 0.024, respectively). There was no significant difference in the AUCs between the XGBoost model and the LR model (0.891 vs. 0.880, P = 0.560). The XGBoost model demonstrated superior accuracy (87.82% vs. 86.54%), sensitivity (50.00% vs. 39.39%), PPV (73.68% vs. 73.33%), NPV (89.78% vs. 87.94%), and F1 score (59.57% vs. 51.16%), with only slightly lower specificity (96.09% vs. 96.88%). Together, the XGBoost model and the stepwise LR model identified age, FMA-LE at admission, FAC at admission, and lower limb spasticity as key factors influencing independent walking.

CONCLUSION

Overall, the XGBoost model performed best in predicting independent walking after stroke. The XGBoost and LR models together confirm that age, admission FMA-LE, admission FAC, and lower extremity spasticity are the key factors influencing independent walking in stroke patients at hospital discharge.

TRIAL REGISTRATION

Not applicable.

Artificial neural network analysis of factors affecting functional independence recovery in patients with lumbar stenosis after neurosurgery treatment: An observational cohort study

Background and aim

Lumbar spinal stenosis (LSS) is a leading cause of low back pain and lower limbs pain often associated with functional impairment which entails the loss or the impairment of independence in older adults. Conservative treatment is effective in a small percentage of patients, while a significant percentage undergo surgery, even if often without a complete resolution of clinical symptoms and motor deficits. The aim of the study is to identify clinical and demographic prognostic factors characterising the patients who would benefit most from surgical treatment in relation to the functional independence recovery using an innovative approach based on an artificial neural network.

Methods

Adult patients with LSS and indication of neurosurgical treatment were enrolled in the study. Clinical evaluation was performed in the preoperative-phase (into the 48 h before surgery) and after two months. Clinical battery investigated the motor, functional, cognitive, behavioural, and pain status. Demographics and clinical characteristics were analysed via Artificial Neural Network (ANN) using 24 input variables, 2 hidden layers and a single final output layer to predict the outcome. ANN results were compared with those of a multiple linear regression.

Results

108 patients were included in the study and 90 of them [66.5 ± 12.8 years; 27.8 % F] were submitted to surgery treatment and completed longitudinal evaluation. Statistically significant improvement was recorded in all clinical scales comparing pre- and post-surgery. The ANN results showed a prediction ability up to 81 %. Disability, functional limitations, and pain concerning clinical assessment and stature, onset and age about demographic characteristics are the main variables impacting on surgical outcome.

Conclusions

ANN can support clinical decision making, using clinical and demographic characteristics of patients with LSS identifying the characteristics of those who might benefit more from the surgical treatment in terms of global functional recovery.

Machine learning prediction models for in-hospital postoperative functional outcome after moderate-to-severe traumatic brain injury

AIM

This study aims to utilize machine learning (ML) and logistic regression (LR) models to predict surgical outcomes among patients with traumatic brain injury (TBI) based on admission examination, assisting in making optimal surgical treatment decision for these patients.

METHOD

We conducted a retrospective review of patients hospitalized in our department for moderate-to-severe TBI. Patients admitted between October 2011 and October 2022 were assigned to the training set, while patients admitted between November 2022 and May 2023 were designated as the external validation set. Five ML algorithms and LR model were employed to predict the postoperative Glasgow Outcome Scale (GOS) status at discharge using clinical and routine blood data collected upon admission. The Shapley (SHAP) plot was utilized for interpreting the models.

RESULTS

A total of 416 patients were included in this study, and they were divided into the training set (n = 396) and the external validation set (n = 47). The ML models, using both clinical and routine blood data, were able to predict postoperative GOS outcomes with area under the curve (AUC) values ranging from 0.860 to 0.900 during the internal cross-validation and from 0.801 to 0.890 during the external validation. In contrast, the LR model had the lowest AUC values during the internal and external validation (0.844 and 0.567, respectively). When blood data was not available, the ML models achieved AUCs of 0.849 to 0.870 during the internal cross-validation and 0.714 to 0.861 during the external validation. Similarly, the LR model had the lowest AUC values (0.821 and 0.638, respectively). Through repeated cross-validation analysis, we found that routine blood data had a significant association with higher mean AUC values in all ML and LR models. The SHAP plot was used to visualize the contributions of all predictors and highlighted the significance of blood data in the lightGBM model.

CONCLUSION

The study concluded that ML models could provide rapid and accurate predictions for postoperative GOS outcomes at discharge following moderate-to-severe TBI. The study also highlighted the crucial role of routine blood tests in improving such predictions, and may contribute to the optimization of surgical treatment decision-making for patients with TBI.

Harnessing Artificial Neural Networks for Spinal Cord Injury Prognosis

Background: Prediction of neurorehabilitation outcomes after a Spinal Cord Injury (SCI) is crucial for healthcare resource management and improving prognosis and rehabilitation strategies. Artificial neural networks (ANNs) have emerged as a promising alternative to conventional statistical approaches for identifying complex prognostic factors in SCI patients. Materials: a database of 1256 SCI patients admitted for rehabilitation was analyzed. Clinical and demographic data and SCI characteristics were used to predict functional outcomes using both ANN and linear regression models. The former was structured with input, hidden, and output layers, while the linear regression identified significant variables affecting outcomes. Both approaches aimed to evaluate and compare their accuracy for rehabilitation outcomes measured by the Spinal Cord Independence Measure (SCIM) score. Results: Both ANN and linear regression models identified key predictors of functional outcomes, such as age, injury level, and initial SCIM scores (correlation with actual outcome: R = 0.75 and 0.73, respectively). When also alimented with parameters recorded during hospitalization, the ANN highlighted the importance of these additional factors, like motor completeness and complications during hospitalization, showing an improvement in its accuracy (R = 0.87). Conclusions: ANN seemed to be not widely superior to classical statistics in general, but, taking into account complex and non-linear relationships among variables, emphasized the impact of complications during the hospitalization on recovery, particularly respiratory issues, deep vein thrombosis, and urological complications. These results suggested that the management of complications is crucial for improving functional recovery in SCI patients.

Machine learning models for predicting in-hospital outcomes after non-surgical treatment among patients with moderate-to-severe traumatic brain injury

AIM

This study aims to develop prediction models for in-hospital outcomes after non-surgical treatment among patients with moderate-to-severe traumatic brain injury (TBI).

METHOD

We conducted a retrospective review of patients hospitalized for moderate-to-severe TBI in our department from 2011 to 2020. Five machine learning (ML) algorithms and the conventional logistic regression (LR) model were employed to predict in-hospital mortality and the Glasgow Outcome Scale (GOS) functional outcomes. These models utilized clinical and routine blood data collected upon admission.

RESULTS

This study included a total of 196 patients who received only non-surgical treatment after moderate-to-severe TBI. When predicting mortality, ML models achieved area under the curve (AUC) values of 0.921 to 0.994 using clinical and routine blood data, and 0.877 to 0.982 using only clinical data. In comparison, LR models yielded AUCs of 0.762 and 0.730 respectively. When predicting the GOS outcome, ML models achieved AUCs of 0.870 to 0.915 using clinical and routine blood data, and 0.858 to 0.927 using only clinical data. In comparison, the LR model yielded AUCs of 0.798 and 0.787 respectively. Repeated internal validation showed that the contributions of routine blood data for prediction models may depend on different prediction algorithms and different outcome measurements.

CONCLUSION

The study reported ML-based prediction models that provided rapid and accurate predictions on short-term outcomes after non-surgical treatment among patients with moderate-to-severe TBI. The study also highlighted the superiority of ML models over conventional LR models and proposed the complex contributions of routine blood data in such predictions.

Integrating EEG and Machine Learning to Analyze Brain Changes during the Rehabilitation of Broca's Aphasia

The fusion of electroencephalography (EEG) with machine learning is transforming rehabilitation. Our study introduces a neural network model proficient in distinguishing pre- and post-rehabilitation states in patients with Broca's aphasia, based on brain connectivity metrics derived from EEG recordings during verbal and spatial working memory tasks. The Granger causality (GC), phase-locking value (PLV), weighted phase-lag index (wPLI), mutual information (MI), and complex Pearson correlation coefficient (CPCC) across the delta, theta, and low- and high-gamma bands were used (excluding GC, which spanned the entire frequency spectrum). Across eight participants, employing leave-one-out validation for each, we evaluated the intersubject prediction accuracy across all connectivity methods and frequency bands. GC, MI theta, and PLV low-gamma emerged as the top performers, achieving 89.4%, 85.8%, and 82.7% accuracy in classifying verbal working memory task data. Intriguingly, measures designed to eliminate volume conduction exhibited the poorest performance in predicting rehabilitation-induced brain changes. This observation, coupled with variations in model performance across frequency bands, implies that different connectivity measures capture distinct brain processes involved in rehabilitation. The results of this paper contribute to current knowledge by presenting a clear strategy of utilizing limited data to achieve valid and meaningful results of machine learning on post-stroke rehabilitation EEG data, and they show that the differences in classification accuracy likely reflect distinct brain processes underlying rehabilitation after stroke.

Machine Learning-Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review

BACKGROUND

An early warning tool to predict attacks could enhance asthma management and reduce the likelihood of serious consequences. Electronic health records (EHRs) providing access to historical data about patients with asthma coupled with machine learning (ML) provide an opportunity to develop such a tool. Several studies have developed ML-based tools to predict asthma attacks.

OBJECTIVE

This study aims to critically evaluate ML-based models derived using EHRs for the prediction of asthma attacks.

METHODS

We systematically searched PubMed and Scopus (the search period was between January 1, 2012, and January 31, 2023) for papers meeting the following inclusion criteria: (1) used EHR data as the main data source, (2) used asthma attack as the outcome, and (3) compared ML-based prediction models' performance. We excluded non-English papers and nonresearch papers, such as commentary and systematic review papers. In addition, we also excluded papers that did not provide any details about the respective ML approach and its result, including protocol papers. The selected studies were then summarized across multiple dimensions including data preprocessing methods, ML algorithms, model validation, model explainability, and model implementation.

RESULTS

Overall, 17 papers were included at the end of the selection process. There was considerable heterogeneity in how asthma attacks were defined. Of the 17 studies, 8 (47%) studies used routinely collected data both from primary care and secondary care practices together. Extreme imbalanced data was a notable issue in most studies (13/17, 76%), but only 38% (5/13) of them explicitly dealt with it in their data preprocessing pipeline. The gradient boosting-based method was the best ML method in 59% (10/17) of the studies. Of the 17 studies, 14 (82%) studies used a model explanation method to identify the most important predictors. None of the studies followed the standard reporting guidelines, and none were prospectively validated.

CONCLUSIONS

Our review indicates that this research field is still underdeveloped, given the limited body of evidence, heterogeneity of methods, lack of external validation, and suboptimally reported models. We highlighted several technical challenges (class imbalance, external validation, model explanation, and adherence to reporting guidelines to aid reproducibility) that need to be addressed to make progress toward clinical adoption.

Predicting return to work after traumatic brain injury using machine learning and administrative data

BACKGROUND

Accurate patient-specific predictions on return-to-work after traumatic brain injury (TBI) can support both clinical practice and policymaking. The use of machine learning on large administrative data provides interesting opportunities to create such prognostic models.

AIM

The current study assesses whether return-to-work one year after TBI can be predicted accurately from administrative data. Additionally, this study explores how model performance and feature importance change depending on whether a distinction is made between mild and moderate-to-severe TBI.

METHODS

This study used a population-based dataset that combined discharge, claims and social security data of patients hospitalized with a TBI in Belgium during the year 2016. The prediction of TBI was attempted with three algorithms, elastic net logistic regression, random forest and gradient boosting and compared in their performance by their accuracy, sensitivity, specificity and area under the receiver operator curve (ROC AUC).

RESULTS

The distinct modelling algorithms resulted in similar results, with 83% accuracy (ROC AUC 85%) for a binary classification of employed vs. not employed and up to 76% (ROC AUC 82%) for a multiclass operationalization of employment outcome. Modelling mild and moderate-to-severe TBI separately did not result in considerable differences in model performance and feature importance. The features of main importance for return-to-work prediction were related to pre-injury employment.

DISCUSSION

While clearly offering some information beneficial for predicting return-to-work, administrative data needs to be supplemented with additional information to allow further improvement of patient-specific prognose.

State-of-the-Art on Brain-Computer Interface Technology

This paper provides a comprehensive overview of the state-of-the-art in brain-computer interfaces (BCI). It begins by providing an introduction to BCIs, describing their main operation principles and most widely used platforms. The paper then examines the various components of a BCI system, such as hardware, software, and signal processing algorithms. Finally, it looks at current trends in research related to BCI use for medical, educational, and other purposes, as well as potential future applications of this technology. The paper concludes by highlighting some key challenges that still need to be addressed before widespread adoption can occur. By presenting an up-to-date assessment of the state-of-the-art in BCI technology, this paper will provide valuable insight into where this field is heading in terms of progress and innovation.

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

This study describes a dynamic non-linear mathematical approach for modeling the course of disease in acquired brain injury (ABI) patients. Data from a multicentric study were used to evaluate the reliability of the Michaelis-Menten (MM) model applied to well-known clinical variables that assess the outcome of ABI patients. The sample consisted of 156 ABI patients admitted to eight neurorehabilitation subacute units and evaluated at baseline (T0), 4 months after the event (T1) and at discharge (T2). The MM model was used to characterize the trend of the first Principal Component Analysis (PCA) dimension (represented by the variables: feeding modality, RLAS, ERBI-A, Tracheostomy, CRS-r and ERBI-B) in order to predict the most plausible outcome, in terms of positive or negative Glasgow outcome score (GOS) at discharge. Exploring the evolution of the PCA dimension 1 over time, after day 86 the MM model better differentiated between the time course for individuals with a positive and negative GOS (accuracy: 85%; sensitivity: 90.6%; specificity: 62.5%). The non-linear dynamic mathematical model can be used to provide more comprehensive trajectories of the clinical evolution of ABI patients during the rehabilitation period. Our model can be used to address patients for interventions designed for a specific outcome trajectory.

Application of an Artificial Neural Network to Identify the Factors Influencing Neurorehabilitation Outcomes of Patients with Ischemic Stroke Treated with Thrombolysis

The administration of thrombolysis usually reduces the risk of death and the consequences of stroke in the acute phase. However, having received thrombolysis administration is not a prognostic factor for neurorehabilitation outcome in the subacute phase of stroke. It is conceivably due to the complex intertwining of many clinical factors. An artificial neural network (ANN) analysis could be helpful in identifying the prognostic factors of neurorehabilitation outcomes and assigning a weight to each of the factors considered. This study hypothesizes that the prognostic factors could be different between patients who received and those who did not receive thrombolytic treatment, even if thrombolysis is not a prognostic factor per se. In a sample of 862 patients with ischemic stroke, the tested ANN identified some common factors (such as disability at admission, age, unilateral spatial neglect), some factors with higher weight in patients who received thrombolysis (hypertension, epilepsy, aphasia, obesity), and some other factors with higher weight in the other patients (dysphagia, malnutrition, total arterial circulatory infarction). Despite the fact that thrombolysis is not an independent prognostic factor for neurorehabilitation, it seems to modify the relative importance of other clinical factors in predicting which patients will better respond to neurorehabilitation.