Predicting Intracerebral Hemorrhage Patients' Length-of-Stay Probability Distribution Based on Demographic, Clinical, Admission Diagnosis, and Surgery Information

Fusion-Based Deep Learning with Nature-Inspired Algorithm for Intracerebral Haemorrhage Diagnosis

Natural computing refers to computational processes observed in nature and human-designed computing inspired by nature. In recent times, data fusion in the healthcare sector becomes a challenging issue, and it needs to be resolved. At the same time, intracerebral haemorrhage (ICH) is the injury of blood vessels on the brain cells, which is mainly liable for stroke. X-rays and computed tomography (CT) scans are widely applied for locating the haemorrhage position and size. Since manual segmentation of the CT scans by planimetry by the use of radiologists is a time-consuming process, deep learning (DL) is used to attain effective ICH diagnosis performance. This paper presents an automated intracerebral haemorrhage diagnosis using fusion-based deep learning with swarm intelligence (AICH-FDLSI) algorithm. The AICH-FDLSI model operates on four major stages namely preprocessing, image segmentation, feature extraction, and classification. To begin with, the input image is preprocessed using the median filtering (MF) technique to remove the noise present in the image. Next, the seagull optimization algorithm (SOA) with Otsu multilevel thresholding is employed for image segmentation. In addition, the fusion-based feature extraction model using the Capsule Network (CapsNet) and EfficientNet is applied to extract a useful set of features. Moreover, deer hunting optimization (DHO) algorithm is utilized for the hyperparameter optimization of the CapsNet and DenseNet models. Finally, a fuzzy support vector machine (FSVM) is applied as a classification technique to identify the different classes of ICH. A set of simulations takes place to determine the diagnostic performance of the AICH-FDLSI model using the benchmark intracranial haemorrhage data set. The experimental outcome stated that the AICH-FDLSI model has reached a proficient performance over the compared methods in a significant way.

A machine learning approach to model for end-stage liver disease score in cardiac surgery

OBJECTIVE

Model for end-stage liver disease (MELD) likely has nonlinear effects on operative outcomes. We use machine learning to evaluate the nonlinear (dependent variable may not correlate one to one with an increased risk in the outcome) relationship between MELD and outcomes of cardiac surgery.

METHODS

Society of Thoracic Surgery indexed elective cardiac operations between 2011 and 2018 were included. MELD was retrospectively calculated. Logistic regression models and an imbalanced random forest classifier were created on operative mortality. Cox regression models and random forest survival models evaluated survival. Variable importance analysis (VIMP) ranked variables by predictive power. Linear and machine-learned models were compared with receiver operator characteristic (ROC) and Brier score.

RESULTS

We included 3872 patients. Operative mortality was 1.7% and 5-year survival was 82.1%. MELD was the fourth largest positive predictor on VIMP analysis for operative long-term survival and the strongest negative predictor for operative mortality. MELD was not a significant predictor for operative mortality or long-term survival in the logistic or Cox regressions. The logistic model ROC area was 0.762, compared to the random forest classifier ROC of 0.674. The Brier score of the random forest survival model was larger than the Cox regression starting at 2 years and continuing throughout the study period. Bootstrap estimation on linear regression demonstrated machine-learned models were superior.

CONCLUSIONS

MELD and mortality are nonlinear. MELD was insignificant in the Cox multivariable regression but was strongly important in the random forest survival model and when using bootstrapping, the superior utility was demonstrated of the machine-learned models.

Early Determinants of Neurocritical Care Unit Length of Stay in Patients with Spontaneous Intracerebral Hemorrhage

BACKGROUND

The present study considers patients with spontaneous intracerebral hemorrhage (ICH) admitted to the neurocritical care unit (NCCU) through the Emergency Department (ED). It aims to identify patient-specific clinical variables that can be assessed on presentation and that are associated with prolonged NCCU length of stay (LOS).

METHODS

A cross-sectional, single-center, retrospective analysis of ICH patients directly admitted from the ED to the NCCU over an 8-year period was performed. Patients' demographics, clinical exam characteristics, serum laboratory values, intubation status, and neurosurgical procedures at presentation were recorded. Head computed tomography scans obtained on presentation were reviewed. LOS was calculated based on the number of midnights spent in the NCCU. Prolonged LOS was determined using a change point analysis, adopting the method of Taylor which utilizes CUMSUM charts and bootstrap analysis. A decision tree model was trained and validated to identify reliable variables associated with prolonged LOS.

RESULTS

Two hundred and five patients with ICH were analyzed. Prolonged LOS was calculated to be a stay that exceeds 8 days; 68 patients (33%) had a prolonged LOS in NCCU. Median LOS did not differ between survivors and patients who died in hospital. Clinical variables explored through the decision tree model were intubation status, neurosurgical intervention (EVD, decompression or evacuation within 24 h from presentation), and components of the ICH score: age, GCS, hematoma volume, the presence of intraventricular hemorrhage (IVH), and infratentorial location. The model accuracy was 0.8 and AUC was 0.83 (95% CI 0.78-0.89).

CONCLUSION

We propose an ICH-LOS model based on neurosurgical intervention, intubation status and GCS at presentation to predict prolonged LOS in the NCCU in patients with ICH. This simple clinical tool, if prospectively validated, could help with medical planning, contribute to patient care-directed conversations, assist in optimizing hospital resource utilization, and, more importantly, motivating patient-specific interventions aimed at optimizing outcomes and decreasing LOS.