Meanwhile, in order to be applicable to real-time applications while overcoming model uncertainty caused by parameter variability common in IMMPSs models, we subsequently develop a data ...

The statistical associating fluid theory (SAFT) equations of state have been extensively applied in diverse forms across various industrial fields. Within this investigation, based on a benchmark ...

Models that use postoperative data, thus unsuited for baseline prediction, were excluded. Four models were further dropped from this work as their required variables (e.g. normalized atrial area) are ...

Prediction model is used to forecast or predict value from dataset. But one of the most common problems in training prediction model is there are missing values in datasets. Problem is usually managed ...

The data in this paper were obtained from elderly participants in several communities in Beijing from June 2021 to May 2022, including 161 (27.6%) males and 423 (72.4%) females, 248 (42.47%) with ...

The Cleveland Clinic and startup Piramidal are developing an AI model trained on brain wave data to monitor intensive care patients.

Kidney stones are common in elderly patients, and their high recurrence rate after surgery is a significant clinical issue. This single-centre retrospective study aimed to assess the impact of the ...

Extracting experimentally measured heterogeneous catalysis data from the text of research articles into structured databases would facilitate the rapid screening of catalysts with target properties ...

Therefore, our objective was to develop and validate a predictive model for AF recurrence after RFCA using multiple ML algorithms. This model incorporates demographic characteristics, imaging data, ...