Random forest can predict 30‐day mortality of spontaneous intracerebral hemorrhage with remarkable discrimination

Peng, Shih-Kuei; Chuang, Ya-Ching; Kang, T. W.; Tseng, Kuo Hsin

doi:10.1111/j.1468-1331.2010.02955.x

Cited by 53 publications

(57 citation statements)

References 34 publications

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“…In predicting the 30-day mortality, the mean AUC of ICH scores in our study (0.882 using CAVA; 0.877 using ABC/2) are higher than Pengs' study (0.72) [9], Steins' study (0.736) [10], and Chuangs' study (0.74) [11], consistent with Clarkes' study (0.88) [20], and lower than Hemphrills' study (0.92%) [8]. Our study further reveals that the ICH score predicts the 30-day mortality superior to the hematoma shape (AUC = 0.692) and the hematoma size (AUC = 0.715 by ABC/2 and = 0.738 by CAVA) with significant differences of AUC ( P = 0.034 to 0.008).…”

Section: Discussioncontrasting

confidence: 82%

Hematoma Shape, Hematoma Size, Glasgow Coma Scale Score and ICH Score: Which Predicts the 30-Day Mortality Better for Intracerebral Hematoma?

et al. 2014

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PurposeTo investigate the performance of hematoma shape, hematoma size, Glasgow coma scale (GCS) score, and intracerebral hematoma (ICH) score in predicting the 30-day mortality for ICH patients. To examine the influence of the estimation error of hematoma size on the prediction of 30-day mortality.Materials and MethodsThis retrospective study, approved by a local institutional review board with written informed consent waived, recruited 106 patients diagnosed as ICH by non-enhanced computed tomography study. The hemorrhagic shape, hematoma size measured by computer-assisted volumetric analysis (CAVA) and estimated by ABC/2 formula, ICH score and GCS score was examined. The predicting performance of 30-day mortality of the aforementioned variables was evaluated. Statistical analysis was performed using Kolmogorov-Smirnov tests, paired t test, nonparametric test, linear regression analysis, and binary logistic regression. The receiver operating characteristics curves were plotted and areas under curve (AUC) were calculated for 30-day mortality. A P value less than 0.05 was considered as statistically significant.ResultsThe overall 30-day mortality rate was 15.1% of ICH patients. The hematoma shape, hematoma size, ICH score, and GCS score all significantly predict the 30-day mortality for ICH patients, with an AUC of 0.692 (P = 0.0018), 0.715 (P = 0.0008) (by ABC/2) to 0.738 (P = 0.0002) (by CAVA), 0.877 (P<0.0001) (by ABC/2) to 0.882 (P<0.0001) (by CAVA), and 0.912 (P<0.0001), respectively.ConclusionOur study shows that hematoma shape, hematoma size, ICH scores and GCS score all significantly predict the 30-day mortality in an increasing order of AUC. The effect of overestimation of hematoma size by ABC/2 formula in predicting the 30-day mortality could be remedied by using ICH score.

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Section: Discussioncontrasting

confidence: 82%

Hematoma Shape, Hematoma Size, Glasgow Coma Scale Score and ICH Score: Which Predicts the 30-Day Mortality Better for Intracerebral Hematoma?

et al. 2014

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“…In the meantime, a tuned RF model outperformed multinomial logistic regression in predicting 6-category COD by 17.2% higher prediction accuracy (8.7% absolute accuracy-difference). This finding supports that RF’s accuracy is similar to or better than support vector machines, artificial neural network and logistic regression in predicting various clinical outcomes,[9-11, 37] but contrasts to that its accuracy is inferior to that of logistic regression. [38] It is plausible, but needs additional validation, that RF could also be highly useful in predicting multi-category COD or outcomes of other diseases.…”

Section: Discussionsupporting

confidence: 57%

Predicting long-term multicategory cause of death in patients with prostate cancer: random forest versus multinomial model

Wang

Deng

Zeng

et al. 2020

Preprint

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Patients with prostate cancer more likely die of non-cancer cause of death (COD) than prostate cancer. It is thus important to accurately predict multi-category COD in these patients. Random forest (RF), a popular machine learning model, has been shown useful for predicting binary cancer-specific deaths. However, its accuracy for predicting multi-category COD in cancer patients is unclear. We included patients in Surveillance, Epidemiology, and End Results-18 cancer registry-program with prostate cancer diagnosed in 2004 (followed-up through 2016). They were randomly divided into training and testing sets with equal sizes. We evaluated prediction accuracies of RF and conventional-statistical/multinomial models for 6-category COD by data-encoding types using the 2-fold cross-validation approach. Among 49,864 prostate cancer patients, 29,611 (59.4%) were alive at the end of follow-up, and 5,448 (10.9%) died of cardiovascular disease, 4,607 (9.2%) of prostate cancer, 3,681 (7.4%) of Non-Prostate cancer, 717 (1.4%) of infection, and 5,800 (11.6%) of other causes. We predicted 6-category COD among these patients with a mean accuracy of 59.1% (n=240, 95% CI, 58.7%-59.4%) in RF models with one-hot encoding, and 50.4% (95% CI, 49.7%-51.0%) in multinomial models. Tumor characteristics, prostate-specific antigen level, and diagnosis confirmation-method were important in RF and multinomial models. In RF models, no statistical differences were found between the accuracies of development versus cross validation phases, and those of categorical versus one-hot encoding. We here report a RF model that has an accuracy of 59.1% in predicting long-term 6-category COD among prostate cancer patients. It outperforms multinomial logistic models (absolute prediction-accuracy difference, 8.7%).

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“…Kim et al (2019) recently published a deep-learning model that uses clinical parameters to predict survival of oral cancer patients with high concordance with reality [12]. Similarly, random forest-based models have been created to predict 30-day mortality of spontaneous intracerebral hemorrhage [13] and overall mortality of patients with acute kidney injury or in renal transplant recipients [14,15].…”

Section: Introductionmentioning

confidence: 99%

Improved Personalized Survival Prediction of Patients With Diffuse Large B-cell Lymphoma Using Gene Expression Profiling

Orgueira

Arias

López

et al. 2020

Preprint

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Abstract Background30-40% of patients with Diffuse Large B-cell Lymphoma (DLBCL) have an adverse clinical evolution. The increased understanding of DLBCL biology has shed light on the clinical evolution of this pathology, leading to the discovery of prognostic factors based on gene expression data, genomic rearrangements and mutational subgroups. Nevertheless, additional efforts are needed in order to enable survival predictions at the patient level. This study investigated new machine learning models of survival based on transcriptomic and clinical data.MethodsGene expression profiling (GEP) in 2 different publicly available retrospective cohorts were analyzed. Cox regression and unsupervised clustering were performed in order to identify probes associated with overall survival on the largest cohort. Random forests were created to model survival using combinations of GEP data, COO classification and clinical information. Cross-validation was used to compare model results in the training set, and Harrel’s concordance index (c-index) was used to assess model’s predictability. Results were validated in an independent test set. Results233 and 64 patients were included in the training and test set, respectively. Initially we derived and validated a 4-gene expression clusterization that was independently associated with lower survival in 20% of patients. These genes were TNFRSF9, BIRC3, BCL2L1 and G3BP2. Thereafter, we applied machine-learning models to predict survival. A set of 102 genes was highly predictive of disease outcome, outperforming available clinical information and COO classification. The final best model integrated clinical information, COO classification, 4-gene-based clusterization and 50 gene expression data (training set c-index, 0.8404, test set c-index, 0.7942). ConclusionThis study indicates that modelling DLBCL survival with transcriptomic-based machine learning algorithms can largely outperform other important prognostic variables such as disease stage and COO.

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Random forest can predict 30‐day mortality of spontaneous intracerebral hemorrhage with remarkable discrimination

Abstract: The RF provided the best predictive performance amongst all of the tested models. We believe that the RF is a suitable tool for clinicians to use in predicting the 30-day mortality of patients after SICH.

Cited by 53 publications

References 34 publications

Hematoma Shape, Hematoma Size, Glasgow Coma Scale Score and ICH Score: Which Predicts the 30-Day Mortality Better for Intracerebral Hematoma?

Hematoma Shape, Hematoma Size, Glasgow Coma Scale Score and ICH Score: Which Predicts the 30-Day Mortality Better for Intracerebral Hematoma?

Predicting long-term multicategory cause of death in patients with prostate cancer: random forest versus multinomial model

Improved Personalized Survival Prediction of Patients With Diffuse Large B-cell Lymphoma Using Gene Expression Profiling

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