Development and Validation of Nomograms to Predict the Overall Survival and Progression-Free Survival in Patients with Advanced Unresectable Intrahepatic Cholangiocarcinoma.

Purpose: This study aimed to develop and validate clinical nomograms for predicting progression-free survival (PFS) and overall survival (OS) in unresectable ICC patients.

Patients And Methods: Patients with ICC between 1 January 2018 and 31 May 2023 were selected and randomized into a training set and an internal validation set as a 7:3 ratio. Data analysis and modeling were conducted through R software.

Developing a Machine Learning Model to Predict 180-day Readmission for Elderly Patients with Angina.

Background: Readmission of elderly angina patients has become a serious problem, with a dearth of available prediction tools for readmission assessment. The objective of this study was to develop a machine learning (ML) model that can predict 180-day all-cause readmission for elderly angina patients.

Methods: The clinical data for elderly angina patients was retrospectively collected.

Established machine learning models to predict readmission for elderly patients with ischemic heart disease.

Background: The contribution of clinical features associated with 30-day or 1-year readmission in elderly patients with ischemic heart disease (IHD) and whether these features can be used to predict the readmission risk of patients has not been studied.

Aims: The study aimed to develop 30-day and 1-year readmission prediction models for elderly IHD patients using combined machine learning features routinely collected at the time of hospital discharge, and to investigate their prognostic impact.

Methods: Eight machine learning algorithms were used to develop prediction models.

Predicting 1 year readmission for heart failure: A comparative study of machine learning and the LACE index.

Aims: There is a lack of tools for accurately identifying the risk of readmission for heart failure in elderly patients with arrhythmia. The aim of this study was to establish and compare the performance of the LACE [length of stay ('L'), acute (emergent) admission ('A'), Charlson comorbidity index ('C') and visits to the emergency department during the previous 6 months ('E')] index and machine learning in predicting 1 year readmission for heart failure in elderly patients with arrhythmia.

Methods: Elderly patients with arrhythmia who were hospitalized at Sichuan Provincial People's Hospital between 1 June 2018 and 31 May 2020 were enrolled.

Research trends and hotspots in exercise rehabilitation for coronary heart disease: A bibliometric analysis.

Exercise rehabilitation can improve the prognosis of patients with coronary heart disease. However, a bibliometric analysis of the global exercise rehabilitation for coronary heart disease (CHD) research topic is lacking. This study investigated the development trends and research hotspots in the field of coronary heart disease and exercise rehabilitation.

Predicting 7-day unplanned readmission in elderly patients with coronary heart disease using machine learning.

Background: Short-term unplanned readmission is always neglected, especially for elderly patients with coronary heart disease (CHD). However, tools to predict unplanned readmission are lacking. This study aimed to establish the most effective predictive model for the unplanned 7-day readmission in elderly CHD patients using machine learning (ML) algorithms.

Develop an ADR prediction system of Chinese herbal injections containing Panax notoginseng saponin: a nested case-control study using machine learning.

Objective: This study aimed to develop an adverse drug reactions (ADR) antecedent prediction system using machine learning algorithms to provide the reference for security usage of Chinese herbal injections containing Panax notoginseng saponin in clinical practice.

Design: A nested case-control study.

Setting: National Center for ADR Monitoring and the Electronic Medical Record (EMR) system.

Compare the performance of multiple machine learning models in predicting tacrolimus concentration for infant patients with living donor liver transplantation.

Background: This study aims to establish multiple ML models and compare their performance in predicting tacrolimus concentration for infant patients who received LDLT within 3 months after transplantation.

Methods: Retrospectively collected basic information and relevant biochemical indicators of included infant patients. CMIA was used to determine tacrolimus C .