Prediction of tuberous sclerosis-associated neurocognitive disorders and seizures via machine learning of structural magnetic resonance imaging.

PURPOSE: Tuberous sclerosis complex (TSC) is a genetic disorder characterized by multiorgan hamartomas, including cerebral lesions, with seizures as a common presentation. Most TSC patients will also experience neurocognitive comorbidities. Our objective was to use machine learning techniques incorporating clinical and imaging data to predict the occurrence of major neurocognitive disorders and seizures in TSC patients. METHODS: A cohort of TSC patients were enrolled in this retrospective study. Clinical data included genetic, demographic, and seizure characteristics. Imaging parameters included the number, characteristics, and location of cortical tubers and the presence of subependymal nodules, SEGAs, and cerebellar tubers. A random forest machine learning scheme was used to predict seizures and neurodevelopmental delay or intellectual developmental disability. Prediction ability was assessed by the area-under-the-curve of receiver-operating-characteristics (AUC-ROC) of ten-fold cross-validation training set and an independent validation set. RESULTS: The study population included 77 patients, 55% male (17.1 ± 11.7 years old). The model achieved AUC-ROC of 0.72 ± 0.1 and 0.68 in the training and internal validation datasets, respectively, for predicting neurocognitive comorbidity. Performance was limited in predicting seizures (AUC-ROC of 0.54 ± 0.19 and 0.71 in the training and internal validation datasets, respectively). The integration of seizure characteristics into the model improved the prediction of neurocognitive comorbidity with AUC-ROC of 0.84 ± 0.07 and 0.75 in the training and internal validation datasets, respectively. CONCLUSIONS: This proof of concept study shows that it is possible to achieve a reasonable prediction of major neurocognitive morbidity in TSC patients using structural brain imaging and machine learning techniques. These tools can help clinicians identify subgroups of TSC patients with an increased risk of developing neurocognitive comorbidities.

Early diagnosis of bloodstream infections in the intensive care unit using machine-learning algorithms.

PURPOSE: We aimed to develop a machine-learning (ML) algorithm that can predict intensive care unit (ICU)-acquired bloodstream infections (BSI) among patients suspected of infection in the ICU. METHODS: The study was based on patients' electronic health records at Beth Israel Deaconess Medical Center (BIDMC) in Boston, Massachusetts, USA, and at Rambam Health Care Campus (RHCC), Haifa, Israel. We included adults from whom blood cultures were collected for suspected BSI at least 48 h after admission. Clinical data, including time-series variables and their interactions, were analyzed by an ML algorithm at each site. Prediction ability for ICU-acquired BSI was assessed by the area under the receiver operating characteristics (AUROC) of ten-fold cross-validation and validation sets with 95% confidence intervals. RESULTS: The datasets comprised 2351 patients from BIDMC (151 with BSI) and 1021 from RHCC (162 with BSI). The median (inter-quartile range) age was 62 (51-75) and 56 (38-69) years, respectively; the median Acute Physiology and Chronic Health Evaluation II scores were 26 (21-32) and 24 (20-29), respectively. The means of the cross-validation AUROCs were 0.87 ± 0.02 for BIDMC and 0.93 ± 0.03 for RHCC. AUROCs of 0.89 ± 0.01 and 0.92 ± 0.02 were maintained in both centers with internal validation, while external validation deteriorated. Valuable predictors were mainly the trends of time-series variables such as laboratory results and vital signs. CONCLUSION: An ML approach that uses temporal and site-specific data achieved high performance in recognizing BC samples with a high probability for ICU-acquired BSI.