Machine Learning-Based Approaches for Prediction of Patients’ Functional Outcome and Mortality after Spontaneous Intracerebral Hemorrhage

Guo, Rui; Zhang, Renjie; Liu, Ran; Liu, Yi; Li, Hao; Ma, Lu; He, Min; You, Chao; Tian, Rui

doi:10.3390/jpm12010112

Cited by 17 publications

(9 citation statements)

References 28 publications

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“…12 ML has been applied to predict outcome in intracerebral hemorrhage. [8][9][10] In a study by Wang et al 8 with 333 patients, an overall accuracy of 83.9%, with a sensitivity and specificity of 72.5%, and 90.6% were achieved using the RF model in predicting 6 month outcome after intracerebral hemorrhage. In a retrospective study by Guo et al, 9 ML-based models slightly outperformed traditional statistical analysis and the ICH score in prediction of mortality.…”

Section: Results and Validationmentioning

confidence: 99%

“…[8][9][10] In a study by Wang et al 8 with 333 patients, an overall accuracy of 83.9%, with a sensitivity and specificity of 72.5%, and 90.6% were achieved using the RF model in predicting 6 month outcome after intracerebral hemorrhage. In a retrospective study by Guo et al, 9 ML-based models slightly outperformed traditional statistical analysis and the ICH score in prediction of mortality. Another RF model to predict outcome in elderly ICH patients by Trevisi et al 33 achieved an area under the curve (AUC) of 0.96, 0.89, and 0.93 for dead, poor, and good outcome classes, respectively, demonstrating high discriminative ability.…”

Section: Results and Validationmentioning

confidence: 99%

“…ML algorithms to predict outcome and mortality in ICH and predicting ICH outcome is still experimental with few documented reports. [8][9][10] These models predict mortality or dichotomized outcome and no attempt has been made to predict the 7-point modified Rankin Scale (mRS). The objective of this study was to develop an AI-based mRS prognostication tool for spontaneous intracerebral hemorrhage using data obtained from our in-hospital stroke database.…”

Section: Introductionmentioning

confidence: 99%

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Development of an Artificial Intelligence-Based Machine Tool to Predict Outcome in Intracerebral Hemorrhage

Menon

Gajula

et al. 2022

Journal of Stroke Medicine

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Introduction: Spontaneous intracerebral hemorrhage is the second most common type of stroke with high morbidity and mortality. Outcome prediction is very important in this disease, to enable us tailor treatment strategies especially in a low- and middle-income countries. Today, prediction is predominantly limited to few clinical factors and may not be very accurate. We explore the application of an artificial intelligence-based platform for outcome prediction with a combination of clinical, radiological, and biochemical parameters. Methods: Data from our prospectively maintained stroke register was cleaned and processed using the XGBoost machine learning (ML) algorithm to predict outcome at discharge and 90 days using the modified Rankin scale. A total of 1,000 patients were included in the study, 129 variables were pruned to 19 significant features during the phase of preprocessing. Results: The data set was split 9:1 with 900 cases being used for training and the remaining 100 for validation. The models were evaluated based on the mean absolute error (MAE). Model-1 trained for predicting “mRS_Discharge” had a MAE of 0.34 and model-2 trained for predicting “mRS_3months” had a MAE of 0.63. Conclusion: ML algorithms can be successfully applied for the prediction of outcome in intracerebral hemorrhage.

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Section: Results and Validationmentioning

confidence: 99%

Section: Results and Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of an Artificial Intelligence-Based Machine Tool to Predict Outcome in Intracerebral Hemorrhage

Menon

Gajula

et al. 2022

Journal of Stroke Medicine

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“…Machine learning is very useful in detecting large vessel occlusion (LVO) in the diagnosis of acute stroke [ 151 ]. It is also used for the prediction of functional outcome of treatment in hemorrhagic stroke [ 152 ].…”

Section: Discussionmentioning

confidence: 99%

Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review

Subudhi

Dash²,

Mohapatra

et al. 2022

Diagnostics

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Magnetic resonance imaging (MRI) is a standard tool for the diagnosis of stroke, but its manual interpretation by experts is arduous and time-consuming. Thus, there is a need for computer-aided-diagnosis (CAD) models for the automatic segmentation and classification of stroke on brain MRI. The heterogeneity of stroke pathogenesis, morphology, image acquisition modalities, sequences, and intralesional tissue signal intensity, as well as lesion-to-normal tissue contrast, pose significant challenges to the development of such systems. Machine learning (ML) is increasingly being used in predictive neuroimaging diagnosis and prognostication. This paper reviews image processing and machine learning techniques that have been applied to detect ischemic stroke on brain MRI, including details on image acquisition, pre-processing, techniques to segment, extraction of features, and classification into stroke types. The main objective of this work is to find the state-of-art machine learning techniques used to predict the ischemic stroke and their application in clinical set-up. The article selection is performed according to PRISMA guideline. The state-of-the-art on automated MRI stroke diagnosis, with a focus on machine learning, is discussed, along with its advantages and limitations. We found that the various machine learning models discussed in this article are able to detect the infarcts with an acceptable accuracy of 70–90%. However, no one has highlighted the time complexity to predict the stroke in the model developed, which is an important factor. The work concludes with proposals for future recommendations for building efficient and robust deep learning (DL) models for quantitative brain MRI analysis. In recent work, with the application of DL approaches, using large datasets to train the models has improved the detection accuracy and reduced computational complexity. We suggest that the design of a decision support system based on artificial intelligence (AI) and clinical data presenting symptoms is essential to support clinicians to accelerate diagnosis and timeous therapy in the emergency management of stroke.

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“…For example, in the field of intracerebral hemorrhage (ICH) mortality and prognosis prediction. Guo et al used logistic regression (LR), random forest (RF), support vector machine (SVM), and other methods to predict 90-day functional outcome of patients with ICH, and LR had the highest AUC of 0.89 [ 15 ]. Bacchi et al used four methods, including LR, RF, decision trees (DT), and artificial neural network (ANN), to predict in-hospital mortality of patients with stroke, and LR performed the best with an AUC of 0.90 [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Joint modeling strategy for using electronic medical records data to build machine learning models: an example of intracerebral hemorrhage

Tang

Wang

Wan

et al. 2022

BMC Med Inform Decis Mak

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Background Outliers and class imbalance in medical data could affect the accuracy of machine learning models. For physicians who want to apply predictive models, how to use the data at hand to build a model and what model to choose are very thorny problems. Therefore, it is necessary to consider outliers, imbalanced data, model selection, and parameter tuning when modeling. Methods This study used a joint modeling strategy consisting of: outlier detection and removal, data balancing, model fitting and prediction, performance evaluation. We collected medical record data for all ICH patients with admissions in 2017–2019 from Sichuan Province. Clinical and radiological variables were used to construct models to predict mortality outcomes 90 days after discharge. We used stacking ensemble learning to combine logistic regression (LR), random forest (RF), artificial neural network (ANN), support vector machine (SVM), and k-nearest neighbors (KNN) models. Accuracy, sensitivity, specificity, AUC, precision, and F1 score were used to evaluate model performance. Finally, we compared all 84 combinations of the joint modeling strategy, including training set with and without cross-validated committees filter (CVCF), five resampling techniques (random under-sampling (RUS), random over-sampling (ROS), adaptive synthetic sampling (ADASYN), Borderline synthetic minority oversampling technique (Borderline SMOTE), synthetic minority oversampling technique and edited nearest neighbor (SMOTEENN)) and no resampling, seven models (LR, RF, ANN, SVM, KNN, Stacking, AdaBoost). Results Among 4207 patients with ICH, 2909 (69.15%) survived 90 days after discharge, and 1298 (30.85%) died within 90 days after discharge. The performance of all models improved with removing outliers by CVCF except sensitivity. For data balancing processing, the performance of training set without resampling was better than that of training set with resampling in terms of accuracy, specificity, and precision. And the AUC of ROS was the best. For seven models, the average accuracy, specificity, AUC, and precision of RF were the highest. Stacking performed best in F1 score. Among all 84 combinations of joint modeling strategy, eight combinations performed best in terms of accuracy (0.816). For sensitivity, the best performance was SMOTEENN + Stacking (0.662). For specificity, the best performance was CVCF + KNN (0.987). Stacking and AdaBoost had the best performances in AUC (0.756) and F1 score (0.602), respectively. For precision, the best performance was CVCF + SVM (0.938). Conclusion This study proposed a joint modeling strategy including outlier detection and removal, data balancing, model fitting and prediction, performance evaluation, in order to provide a reference for physicians and researchers who want to build their own models. This study illustrated the importance of outlier detection and removal for machine learning and showed that ensemble learning might be a good modeling strategy. Due to the low imbalanced ratio (IR, the ratio of majority class and minority class) in this study, we did not find any improvement in models with resampling in terms of accuracy, specificity, and precision, while ROS performed best on AUC.

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Machine Learning-Based Approaches for Prediction of Patients’ Functional Outcome and Mortality after Spontaneous Intracerebral Hemorrhage

Cited by 17 publications

References 28 publications

Development of an Artificial Intelligence-Based Machine Tool to Predict Outcome in Intracerebral Hemorrhage

Development of an Artificial Intelligence-Based Machine Tool to Predict Outcome in Intracerebral Hemorrhage

Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review

Joint modeling strategy for using electronic medical records data to build machine learning models: an example of intracerebral hemorrhage

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