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Prognosis

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Random Forest Prognostication of Survival and 6-Month Outcome in Pediatric Patients Following Decompressive Craniectomy for Traumatic Brain Injury.

World neurosurgery
BACKGROUND: There is a dearth of literature regarding prognostic and predictive factors for outcome following pediatric decompressive craniectomy (DC) performed after traumatic brain injury (TBI). The aim of this study was to develop a random forest ...

Machine learning for predicting in-hospital mortality in elderly patients with heart failure combined with hypertension: a multicenter retrospective study.

Cardiovascular diabetology
BACKGROUND: Heart failure combined with hypertension is a major contributor for elderly patients (≥ 65 years) to in-hospital mortality. However, there are very few models to predict in-hospital mortality in such elderly patients. We aimed to develop ...

Machine learning-based model for predicting the occurrence and mortality of nonpulmonary sepsis-associated ARDS.

Scientific reports
OBJECTIVE: The objective was to establish a machine learning-based model for predicting the occurrence and mortality of nonpulmonary sepsis-associated ARDS.

Use of machine learning to identify prognostic variables for outcomes in chronic low back pain treatment: a retrospective analysis.

The Journal of manual & manipulative therapy
OBJECTIVES: Most patients seen in physical therapy (PT) clinics for low back pain (LBP) are treated for chronic low back pain (CLBP), yet PT interventions suggest minimal effectiveness. The Cochrane Back Review Group proposed 'Holy Grail' questions, ...

Improved prognostication of overall survival after radiotherapy in lung cancer patients by an interpretable machine learning model integrating lung and tumor radiomics and clinical parameters.

La Radiologia medica
BACKGROUND: Accurate prognostication of overall survival (OS) for non-small cell lung cancer (NSCLC) patients receiving definitive radiotherapy (RT) is crucial for developing personalized treatment strategies. This study aims to construct an interpre...

Prognostic Significance and Associations of Neural Network-Derived Electrocardiographic Features.

Circulation. Cardiovascular quality and outcomes
BACKGROUND: Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to conventional ECG parameters and morphology. ...

Deep Learning to Detect Pulmonary Hypertension from the Chest X-Ray Images of Patients with Systemic Sclerosis.

International heart journal
Pulmonary hypertension (PH) is a serious prognostic complication in patients with systemic sclerosis (SSc). Deep learning models can be applied to detect PH in the chest X-ray images of these patients. The aim of the study was to investigate the perf...

Machine learning-driven estimation of mutational burden highlights DNAH5 as a prognostic marker in colorectal cancer.

Biology direct
BACKGROUND: Tumor Mutational Burden (TMB) have emerged as pivotal predictive biomarkers in determining prognosis and response to immunotherapy in colorectal cancer (CRC) patients. While Whole Exome Sequencing (WES) stands as the gold standard for TMB...

Prediction of 12-month recurrence of pancreatic cancer using machine learning and prognostic factors.

BMC medical informatics and decision making
BACKGROUND AND AIM: Pancreatic cancer is lethal and prevalent among other cancer types. The recurrence of this tumor is high, especially in patients who did not receive adjuvant therapies. Early prediction of PC recurrence has a significant role in e...

AI tool for predicting MGMT methylation in glioblastoma for clinical decision support in resource limited settings.

Scientific reports
Glioblastoma is an aggressive brain cancer with a poor prognosis. The O6-methylguanine-DNA methyltransferase (MGMT) gene methylation status is crucial for treatment stratification, yet economic constraints often limit access. This study aims to devel...