Development and validation of machine learning prediction model for post-rehabilitation functional outcome after intracerebral hemorrhage

Sonobe, Shinya; Ishikawa, Tsubasa; Niizuma, Kuniyasu; Kawakami, Eiryo; Ueda, Takuya; Takaya, Eichi; Miyauchi, Carlos Makoto; Iwazaki, Junya; Kochi, Ryuzaburo; Endo, Toshiki; Shastry, Arun; Vijayananda, J.; Seth, Ajay; Nakagawa, Atsuhiro; Yoshida, Mitsutoshi; Tominaga, Teiji

doi:10.1016/j.inat.2022.101560

Cited by 3 publications

(4 citation statements)

References 43 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%

“…ML-based models have also been use to predict hematoma expansion and outcome from CT images of patients with a high degree of accuracy. 10, 34, 35 The strength of our model is the prediction of individual patient mRs using unlike the discussed models which classifies outcome as good, poor, or dead. A recent meta-analysis has demonstrated that ML models have better predictive performance when compared to standard statistical analysis.…”

Section: Discussionmentioning

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: Discussionmentioning

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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“…Decisions can be made using the random forest (RF) method, which averages many nodes. It is possible to classify and segment images using a convolutional neural network (CNN), which has several layers and nodes [4]. Each of these algorithms has been previously detailed in technical detail [5], but no agreement has developed to guide the selection of specifc algorithms for clinical use in cardiovascular medicine.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning for Cardiovascular Disease Risk Prediction

Dalal

Goel

Onyema

et al. 2023

Computational Intelligence and Neuroscience

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Cardiovascular diseases (CVDs) are a common cause of heart failure globally. The need to explore possible ways to tackle the disease necessitated this study. The study designed a machine learning model for cardiovascular disease risk prediction in accordance with a dataset that contains 11 features which may be used to forecast the disease. The dataset from Kaggle on cardiovascular disease includes approximately 70,000 patient records that were used to determine the outcome. Compared to the UCI dataset, the Kaggle dataset has many more training and validation records. Models created using neural networks, random forests, Bayesian networks, C5.0, and QUEST were compared for this dataset. On training and testing data sets, the results acquired a high accuracy (99.1 percent), which is significantly superior to previous methods. Ahead-of-time detection and diagnosis of cardiac disease, as well as better treatment outcomes, are strong possibilities for the suggested prediction model. Additionally, it may help patients better manage their illness or life forms in order to increase their chances of recovery/survival. The result showed greater accuracy and promising signs that machine-learning algorithms can indeed assist in early identification of the disease and improvement of the treatment outcome.

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CT-based deep learning model for predicting hospital discharge outcome in spontaneous intracerebral hemorrhage

Zhao,

Zhou,

Luo

et al. 2023

Eur Radiol

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Development and validation of machine learning prediction model for post-rehabilitation functional outcome after intracerebral hemorrhage

Cited by 3 publications

References 43 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 for Cardiovascular Disease Risk Prediction

CT-based deep learning model for predicting hospital discharge outcome in spontaneous intracerebral hemorrhage

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