Machine Learning-Based Model for Prediction of Post-Stroke Cognitive Impairment in Acute Ischemic Stroke: A Cross-Sectional Study.

Background And Objective: Early identification of post-stroke cognitive impairment (PSCI) is an important challenge for clinicians. In this study, we aimed to build a machine learning-based prediction model for PSCI and uncover potential risk factors to support clinical decision-making.

Materials And Methods: We collected features of 96 patients with acute ischemic stroke and measured cognitive impairment using the Mini-Mental State Examination.

Diagnostic value of HSP90α and related markers in lung cancer.

Purpose: To investigate the expression of heat shock protein 90α (HSP90α) in patients with lung cancer (LC) and the clinical value of HSP90α and other related markers in the diagnosis of LC.

Methods: Of 335 patients enrolled in the study cohort, 175 were screened for LC and 160 were healthy (HC). The plasma levels of HSP90α and related markers (CEA, NSE, CYFRA21-1 and ProGRP) were detected in all individuals in the cohort by enzyme-linked immunosorbent assay (ELISA).

Prediction of prognosis in immunoglobulin a nephropathy patients with focal crescent by machine learning.

Background And Objectives: Immunoglobulin a nephropathy (IgAN) is the most common primary glomerular disease in the world, with different clinical manifestations, varying severity of pathological changes, common complications of crescent formation in different proportions, and great individual heterogeneous in clinical outcomes. Therefore, we aim to develop a machine learning (ML) based predictive model for predicting the prognosis of IgAN with focal crescent formation and without obvious chronic renal lesions (glomerulosclerosis <25%).

Materials: We retrospectively reviewed biopsy-proven IgAN patients in our hospital and cooperative hospital from 2005 to 2017.

An application of machine learning based on real-world data: Mining features of fibrinogen in clinical stages of lung cancer between sexes.

Background: Lung cancer is the most threatening malignant tumor to human health and life. Using a variety of machine learning algorithms and statistical analyses, this paper explores, discovers and demonstrates new indicators for the early diagnosis of lung cancer and their diagnostic performance from large samples of clinical data in the real world.

Methods: By applying machine learning methods, including minimum description length (MDL), naive Bayesian (NB), K-means (KM), nonnegative matrix factorization (NMF), and decision tree (DT), based on large sample data of 2,502 patients, we built a classification model and systematically explored differences in fibrinogen levels in different clinical stages of lung cancer between the sexes.

Predicting the postoperative blood coagulation state of children with congenital heart disease by machine learning based on real-world data.

Background: Postoperative blood coagulation assessment of children with congenital heart disease (CHD) has been developed using a conventional statistical approach. In this study, the machine learning (ML) was used to predict postoperative blood coagulation function of children with CHD, and assess an array of ML models.

Methods: This was a retrospective and data mining study.

Machine learning for the prediction of severe pneumonia during posttransplant hospitalization in recipients of a deceased-donor kidney transplant.

Background: Pneumonia accounts for the majority of infection-related deaths after kidney transplantation. We aimed to build a predictive model based on machine learning for severe pneumonia in recipients of deceased-donor transplants within the perioperative period after surgery.

Methods: We collected the features of kidney transplant recipients and used a tree-based ensemble classification algorithm (Random Forest or AdaBoost) and a nonensemble classifier (support vector machine, Naïve Bayes, or logistic regression) to build the predictive models.