AIMC Topic: Prognosis

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Construction of a Risk Prediction Model for Hospital-Acquired Pulmonary Embolism in Hospitalized Patients.

Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
The purpose of this study is to establish a novel pulmonary embolism (PE) risk prediction model based on machine learning (ML) methods and to evaluate the predictive performance of the model and the contribution of variables to the predictive perform...

Differentiating Small-Cell Lung Cancer From Non-Small-Cell Lung Cancer Brain Metastases Based on MRI Using Efficientnet and Transfer Learning Approach.

Technology in cancer research & treatment
Differentiation between small-cell lung cancer (SCLC) from non-small-cell lung cancer (NSCLC) brain metastases is crucial due to the different clinical behaviors of the two tumor types. We propose the use of a deep learning and transfer learning appr...

Prediction of Radiation Pneumonitis With Machine Learning in Stage III Lung Cancer: A Pilot Study.

Technology in cancer research & treatment
BACKGROUND: Radiation pneumonitis (RP) is a dose-limiting toxicity in lung cancer radiotherapy (RT). As risk factors in the development of RP, patient and tumor characteristics, dosimetric parameters, and treatment features are intertwined, and it is...

Temporal changes of quantitative CT findings from 102 patients with COVID-19 in Wuhan, China: A longitudinal study.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Computed tomography (CT) imaging combined with artificial intelligence is important in the diagnosis and prognosis of lung diseases.

Treatment of individual predictors with neural network algorithms improves Global Registry of Acute Coronary Events score discrimination.

Archivos de cardiologia de Mexico
OBJECTIVE: The aim of this study was to develop, train, and test different neural network (NN) algorithm-based models to improve the Global Registry of Acute Coronary Events (GRACE) score performance to predict in-hospital mortality after an acute co...

Development and Clinical Validation of a Seven-Gene Prognostic Signature Based on Multiple Machine Learning Algorithms in Kidney Cancer.

Cell transplantation
About a third of patients with kidney cancer experience recurrence or cancer-related progression. Clinically, kidney cancer prognoses may be quite different, even in patients with kidney cancer at the same clinical stage. Therefore, there is an urgen...

Using machine learning method to identify as a novel marker to predict biochemical recurrence in prostate cancer.

Biomarkers in medicine
This study aims to identify novel marker to predict biochemical recurrence (BCR) in prostate cancer patients after radical prostatectomy with negative surgical margin. The Cancer Genome Atlas database, Gene Expression Omnibus database and Cancer Ce...

Development of Machine Learning Models for Predicting Postoperative Delayed Remission in Patients With Cushing's Disease.

The Journal of clinical endocrinology and metabolism
CONTEXT: Postoperative hypercortisolemia mandates further therapy in patients with Cushing's disease (CD). Delayed remission (DR) is defined as not achieving postoperative immediate remission (IR), but having spontaneous remission during long-term fo...

Phenotypic clustering of heart failure with preserved ejection fraction reveals different rates of hospitalization.

Journal of cardiovascular medicine (Hagerstown, Md.)
AIMS: Approximately 50% of patients with heart failure have preserved (≥50%) ejection fraction (HFpEF). Improved understanding of the phenotypic heterogeneity of HFpEF might facilitate development of targeted therapies and interventions.