scholarly journals Machine Learning Algorithms to Predict In-Hospital Mortality in Patients with Traumatic Brain Injury

2021 ◽  
Vol 11 (11) ◽  
pp. 1144
Author(s):  
Sheng-Der Hsu ◽  
En Chao ◽  
Sy-Jou Chen ◽  
Dueng-Yuan Hueng ◽  
Hsiang-Yun Lan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Emergency Department ◽  
Brain Injury ◽  
High Risk ◽  
Hospital Mortality ◽  
Prediction Models ◽  
Care Delivery ◽  
Machine Learning Algorithms ◽  
Clinical Measures

Traumatic brain injury (TBI) can lead to severe adverse clinical outcomes, including death and disability. Early detection of in-hospital mortality in high-risk populations may enable early treatment and potentially reduce mortality using machine learning. However, there is limited information on in-hospital mortality prediction models for TBI patients admitted to emergency departments. The aim of this study was to create a model that successfully predicts, from clinical measures and demographics, in-hospital mortality in a sample of TBI patients admitted to the emergency department. Of the 4881 TBI patients who were screened at the emergency department at a high-level first aid duty hospital in northern Taiwan, 3331 were assigned in triage to Level I or Level II using the Taiwan Triage and Acuity Scale from January 2008 to June 2018. The most significant predictors of in-hospital mortality in TBI patients were the scores on the Glasgow coma scale, the injury severity scale, and systolic blood pressure in the emergency department admission. This study demonstrated the effective cutoff values for clinical measures when using machine learning to predict in-hospital mortality of patients with TBI. The prediction model has the potential to further accelerate the development of innovative care-delivery protocols for high-risk patients.

scholarly journals Prediction of Cardiac Arrest in the Emergency Department Based on Machine Learning and Sequential Characteristics: Model Development and Retrospective Clinical Validation Study (Preprint)

2019 ◽  
Author(s):  
Sungjun Hong ◽  
Sungjoo Lee ◽  
Jeonghoon Lee ◽  
Won Chul Cha ◽  
Kyunga Kim
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Cardiac Arrest ◽  
Prediction Model ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Clinical Usefulness ◽  
Class Imbalance Problem ◽  
Data Set

BACKGROUND The development and application of clinical prediction models using machine learning in clinical decision support systems is attracting increasing attention. OBJECTIVE The aims of this study were to develop a prediction model for cardiac arrest in the emergency department (ED) using machine learning and sequential characteristics and to validate its clinical usefulness. METHODS This retrospective study was conducted with ED patients at a tertiary academic hospital who suffered cardiac arrest. To resolve the class imbalance problem, sampling was performed using propensity score matching. The data set was chronologically allocated to a development cohort (years 2013 to 2016) and a validation cohort (year 2017). We trained three machine learning algorithms with repeated 10-fold cross-validation. RESULTS The main performance parameters were the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). The random forest algorithm (AUROC 0.97; AUPRC 0.86) outperformed the recurrent neural network (AUROC 0.95; AUPRC 0.82) and the logistic regression algorithm (AUROC 0.92; AUPRC=0.72). The performance of the model was maintained over time, with the AUROC remaining at least 80% across the monitored time points during the 24 hours before event occurrence. CONCLUSIONS We developed a prediction model of cardiac arrest in the ED using machine learning and sequential characteristics. The model was validated for clinical usefulness by chronological visualization focused on clinical usability.

scholarly journals What Do Machines Tell us About Dementia? Machine Learning Applied to Aging, Dementia and Traumatic Brain Injury Study

2021 ◽  
Author(s):  
Denis Arthur Pinheiro Moura ◽  
Joao Ricardo Mendes de Oliveira
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Expression Profiles ◽  
Brain Regions ◽  
Machine Learning Algorithms ◽  
Differentially Expressed ◽  
Classification Model ◽  
Specific Gene

Abstract Dementia, a syndrome characterized by the progressive deterioration of memory and cognition, arises from different pathologies, with Alzheimer's Disease (AD) its most common cause. Patterns of gene expression during dementia of different etiologies may function as generalist biomarkers of the condition. We used RNA-Seq data from the Allen Dementia and Traumatic Brain Injury Study (ADTBI) to identify differentially expressed genes in brains with dementia. Machine Learning algorithms Decision Trees (DT) and Random Forest (RF) were used to create models to identify dementia samples based on their gene expression profile. Importance analyses were conducted to identify the most relevant genes in each classification model. A total of 1629 differentially expressed (DE) genes were found in brains with the condition. Gene PAN3-AS1 was the only DE gene across more than three brain regions. The artificial intelligence models were capable of identifying correctly up to 92.85% of dementia samples. Our analyses provide interesting insights regarding using brain-specific gene expression profiles as biomarkers of dementia, identifying genes possibly involved with dementia, and guiding future studies in prediction and early identification of the syndrome.

scholarly journals Prediction of Cardiac Arrest in the Emergency Department Based on Machine Learning and Sequential Characteristics: Model Development and Retrospective Clinical Validation Study

10.2196/15932 ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. e15932
Author(s):  
Sungjun Hong ◽  
Sungjoo Lee ◽  
Jeonghoon Lee ◽  
Won Chul Cha ◽  
Kyunga Kim
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Cardiac Arrest ◽  
Prediction Model ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Clinical Usefulness ◽  
Class Imbalance Problem ◽  
Data Set

Background The development and application of clinical prediction models using machine learning in clinical decision support systems is attracting increasing attention. Objective The aims of this study were to develop a prediction model for cardiac arrest in the emergency department (ED) using machine learning and sequential characteristics and to validate its clinical usefulness. Methods This retrospective study was conducted with ED patients at a tertiary academic hospital who suffered cardiac arrest. To resolve the class imbalance problem, sampling was performed using propensity score matching. The data set was chronologically allocated to a development cohort (years 2013 to 2016) and a validation cohort (year 2017). We trained three machine learning algorithms with repeated 10-fold cross-validation. Results The main performance parameters were the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). The random forest algorithm (AUROC 0.97; AUPRC 0.86) outperformed the recurrent neural network (AUROC 0.95; AUPRC 0.82) and the logistic regression algorithm (AUROC 0.92; AUPRC=0.72). The performance of the model was maintained over time, with the AUROC remaining at least 80% across the monitored time points during the 24 hours before event occurrence. Conclusions We developed a prediction model of cardiac arrest in the ED using machine learning and sequential characteristics. The model was validated for clinical usefulness by chronological visualization focused on clinical usability.

scholarly journals Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yu-Tai Lo ◽  
Jay Chie-hen Liao ◽  
Mei-Hua Chen ◽  
Chia-Ming Chang ◽  
Cheng-Te Li
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Prediction Models ◽  
Transitional Care ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Unplanned Readmission ◽  
Extreme Gradient Boosting ◽  
Readmission Risk

Abstract Background Early unplanned hospital readmissions are associated with increased harm to patients, increased medical costs, and negative hospital reputation. With the identification of at-risk patients, a crucial step toward improving care, appropriate interventions can be adopted to prevent readmission. This study aimed to build machine learning models to predict 14-day unplanned readmissions. Methods We conducted a retrospective cohort study on 37,091 consecutive hospitalized adult patients with 55,933 discharges between September 1, 2018, and August 31, 2019, in an 1193-bed university hospital. Patients who were aged < 20 years, were admitted for cancer-related treatment, participated in clinical trial, were discharged against medical advice, died during admission, or lived abroad were excluded. Predictors for analysis included 7 categories of variables extracted from hospital’s medical record dataset. In total, four machine learning algorithms, namely logistic regression, random forest, extreme gradient boosting, and categorical boosting, were used to build classifiers for prediction. The performance of prediction models for 14-day unplanned readmission risk was evaluated using precision, recall, F1-score, area under the receiver operating characteristic curve (AUROC), and area under the precision–recall curve (AUPRC). Results In total, 24,722 patients were included for the analysis. The mean age of the cohort was 57.34 ± 18.13 years. The 14-day unplanned readmission rate was 1.22%. Among the 4 machine learning algorithms selected, Catboost had the best average performance in fivefold cross-validation (precision: 0.9377, recall: 0.5333, F1-score: 0.6780, AUROC: 0.9903, and AUPRC: 0.7515). After incorporating 21 most influential features in the Catboost model, its performance improved (precision: 0.9470, recall: 0.5600, F1-score: 0.7010, AUROC: 0.9909, and AUPRC: 0.7711). Conclusions Our models reliably predicted 14-day unplanned readmissions and were explainable. They can be used to identify patients with a high risk of unplanned readmission based on influential features, particularly features related to diagnoses. The operation of the models with physiological indicators also corresponded to clinical experience and literature. Identifying patients at high risk with these models can enable early discharge planning and transitional care to prevent readmissions. Further studies should include additional features that may enable further sensitivity in identifying patients at a risk of early unplanned readmissions.

scholarly journals Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

2020 ◽  
Vol 122 ◽  
pp. 95-107 ◽  
Author(s):  
Benjamin Y. Gravesteijn ◽  
Daan Nieboer ◽  
Ari Ercole ◽  
Hester F. Lingsma ◽  
David Nelson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Regression Models ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Better Than

scholarly journals Machine Learning Augmented Causal Inference To Estimate The Treatment Effect of Tranexamic Acid In Traumatic Brain Injury

2021 ◽  
Author(s):  
Imke Meyer ◽  
Jean-Denis Moyer ◽  
Aliénor Dreyfus ◽  
Boutonnet Mathieu ◽  
Pierre-Julien Cungi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Missing Data ◽  
Head Injury ◽  
Hospital Mortality ◽  
Causal Inference ◽  
Observational Data ◽  
Level Of Evidence ◽  
Inference Techniques

Abstract Background: The CRASH-3 trial provides a high level of evidence on the question whether to administer Tranexamic Acid (TXA) for Traumatic brain injury (TBI). For numerous other research questions, the available evidence will not correspond to such a level of evidence and will rely on observational evidence only. The development of methodological alternatives to analyze observational data is necessary. The Crash-3trial provided the opportunity to explore the effect of TXA on TBI mortality with two distinct causal inference methods using incomplete observational data. Methods: Two causal inference techniques, inverse propensity weighting (IPW) and doubly robust method (DR), associated with machine learning method techniques to handle missing data, explored the effect of TXA administration on 30-day head injury related death expressed in registry data. The effect was expressed as Average Treatment Effect (ATE). TBI was defined as a head Abbreviated Injury Score >2. The hypothesis expected the results to concur with the results obtained with the CRASH-3 benchmark trial. Results: Between September 2010 and February 2019, from a total of 20037 registry cases 8269 corresponded to the definition of TBI. A total of 683 received TXA and 7565 did not. The observed head-injury related 30-day hospital mortality rate in the group TXA was 30% (205/683) compared to 15% in the group no-TXA (1102/7565, p<0.001). Causal inference with the IPW approach indicates an ATE with a higher mortality after TXA independently of the approach applied to manage missing data (ATE 0.10 (95% IC [0.06, 0.14]) or 0.09 (95% IC [0.03, 0.15])). ATE obtained with DR did not show any effect on mortality independently of the approach applied to missing data (ATE -0.01 (95% IC [-0.05, 0.03]) or -0.01 (95% IC [-0.07, 0.05])). No effect was observed in predefined subgroups. Conclusions: This study demonstrated the feasibility to apply causal inference techniques in incomplete observational data. DR based on a stronger theoretical background compared to IPW, did not show a significant association of TXA administration with in-hospital mortality. This result provides a strong incentive to explore augmented causal inference techniques on incomplete observational data coupled with techniques to handle missing values.

scholarly journals Prediction of in-hospital mortality in patients on mechanical ventilation post traumatic brain injury: machine learning approach

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ahmad Abujaber ◽  
Adam Fadlalla ◽  
Diala Gammoh ◽  
Husham Abdelrahman ◽  
Monira Mollazehi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Mechanical Ventilation ◽  
Brain Injury ◽  
Hospital Mortality ◽  
Clinical Decision Making ◽  
Injury Severity ◽  
Operating Characteristics ◽  
Thromboembolism Prophylaxis ◽  
Head Region

Abstract Background The study aimed to introduce a machine learning model that predicts in-hospital mortality in patients on mechanical ventilation (MV) following moderate to severe traumatic brain injury (TBI). Methods A retrospective analysis was conducted for all adult patients who sustained TBI and were hospitalized at the trauma center from January 2014 to February 2019 with an abbreviated injury severity score for head region (HAIS) ≥ 3. We used the demographic characteristics, injuries and CT findings as predictors. Logistic regression (LR) and Artificial neural networks (ANN) were used to predict the in-hospital mortality. Accuracy, area under the receiver operating characteristics curve (AUROC), precision, negative predictive value (NPV), sensitivity, specificity and F-score were used to compare the models` performance. Results Across the study duration; 785 patients met the inclusion criteria (581 survived and 204 deceased). The two models (LR and ANN) achieved good performance with an accuracy over 80% and AUROC over 87%. However, when taking the other performance measures into account, LR achieved higher overall performance than the ANN with an accuracy and AUROC of 87% and 90.5%, respectively compared to 80.9% and 87.5%, respectively. Venous thromboembolism prophylaxis, severity of TBI as measured by abbreviated injury score, TBI diagnosis, the need for blood transfusion, heart rate upon admission to the emergency room and patient age were found to be the significant predictors of in-hospital mortality for TBI patients on MV. Conclusions Machine learning based LR achieved good predictive performance for the prognosis in mechanically ventilated TBI patients. This study presents an opportunity to integrate machine learning methods in the trauma registry to provide instant clinical decision-making support.

scholarly journals Intracranial Densitometry-Augmented Machine Learning Enhances the Prognostic Value of Brain CT in Pediatric Patients With Traumatic Brain Injury: A Retrospective Pilot Study

2021 ◽  
Vol 9 ◽  
Author(s):  
Young-Tak Kim ◽  
Hakseung Kim ◽  
Choel-Hui Lee ◽  
Byung C. Yoon ◽  
Jung Bin Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Length Of Stay ◽  
Hospital Mortality ◽  
Outcome Measures ◽  
Prognostic Model ◽  
Pediatric Patients ◽  
Prognostic Models ◽  
Brain Ct

Background: The inter- and intrarater variability of conventional computed tomography (CT) classification systems for evaluating the extent of ischemic-edematous insult following traumatic brain injury (TBI) may hinder the robustness of TBI prognostic models.Objective: This study aimed to employ fully automated quantitative densitometric CT parameters and a cutting-edge machine learning algorithm to construct a robust prognostic model for pediatric TBI.Methods: Fifty-eight pediatric patients with TBI who underwent brain CT were retrospectively analyzed. Intracranial densitometric information was derived from the supratentorial region as a distribution representing the proportion of Hounsfield units. Furthermore, a machine learning-based prognostic model based on gradient boosting (i.e., CatBoost) was constructed with leave-one-out cross-validation. At discharge, the outcome was assessed dichotomously with the Glasgow Outcome Scale (favorability: 1–3 vs. 4–5). In-hospital mortality, length of stay (&gt;1 week), and need for surgery were further evaluated as alternative TBI outcome measures.Results: Densitometric parameters indicating reduced brain density due to subtle global ischemic changes were significantly different among the TBI outcome groups, except for need for surgery. The skewed intracranial densitometry of the unfavorable outcome became more distinguishable in the follow-up CT within 48 h. The prognostic model augmented by intracranial densitometric information achieved adequate AUCs for various outcome measures [favorability = 0.83 (95% CI: 0.72–0.94), in-hospital mortality = 0.91 (95% CI: 0.82–1.00), length of stay = 0.83 (95% CI: 0.72–0.94), and need for surgery = 0.71 (95% CI: 0.56–0.86)], and this model showed enhanced performance compared to the conventional CRASH-CT model.Conclusion: Densitometric parameters indicative of global ischemic changes during the acute phase of TBI are predictive of a worse outcome in pediatric patients. The robustness and predictive capacity of conventional TBI prognostic models might be significantly enhanced by incorporating densitometric parameters and machine learning techniques.

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