Representative graphic displays of pyrexia scores by algorithm 5.0 of all analyzed patients over time; First data set, used for optimization of the pyrexia score algorithm. by Hannah Schaefer (13410637)

The structure of genetic algorithm (GA). by Ali Akbar Moosavi (17769033)

“…Then, radial basis functions (RBFNNs), multilayer perceptron (MLPNNs), hybrid genetic algorithm (GA-NNs), and particle swarm optimization (PSO-NNs) neural networks were utilized to develop PTFs and compared their accuracy with the traditional regression model (MLR) using statistical indices. …”

RT laboratory setup. by Ahmed M. Ewias (17262848)

S1 Data - by Alexander Eckers (19106390)

“…Patients were randomized into three groups receiving either a 10% HES 130/0.42 solution, a 6% HES 130/0.42 solution or a crystalloid following a goal-directed hemodynamic algorithm. Endpoints were parameters of standard and viscoelastic coagulation laboratory, blood loss and transfusion requirements at baseline, at the end of surgery (EOS) and the first postoperative day (POD 1).…”

Basic characteristics and intraoperative data. by Alexander Eckers (19106390)

“…Patients were randomized into three groups receiving either a 10% HES 130/0.42 solution, a 6% HES 130/0.42 solution or a crystalloid following a goal-directed hemodynamic algorithm. Endpoints were parameters of standard and viscoelastic coagulation laboratory, blood loss and transfusion requirements at baseline, at the end of surgery (EOS) and the first postoperative day (POD 1).…”

All tables and figures. by Michael Bonert (3751348)

“…Specimens were classified by neuroanatomical location, diagnostic category, and diagnosis with a hierarchical free text string-matching algorithm. All reports classified as probable metastasis per algorithm were reviewed by a pathologist. …”

Von Willebrand parameters over time. by Alexander Eckers (19106390)

“…Patients were randomized into three groups receiving either a 10% HES 130/0.42 solution, a 6% HES 130/0.42 solution or a crystalloid following a goal-directed hemodynamic algorithm. Endpoints were parameters of standard and viscoelastic coagulation laboratory, blood loss and transfusion requirements at baseline, at the end of surgery (EOS) and the first postoperative day (POD 1).…”

Standard coagulation parameters. by Alexander Eckers (19106390)

“…Patients were randomized into three groups receiving either a 10% HES 130/0.42 solution, a 6% HES 130/0.42 solution or a crystalloid following a goal-directed hemodynamic algorithm. Endpoints were parameters of standard and viscoelastic coagulation laboratory, blood loss and transfusion requirements at baseline, at the end of surgery (EOS) and the first postoperative day (POD 1).…”

Blood loss and transfusion requirements. by Alexander Eckers (19106390)

“…Patients were randomized into three groups receiving either a 10% HES 130/0.42 solution, a 6% HES 130/0.42 solution or a crystalloid following a goal-directed hemodynamic algorithm. Endpoints were parameters of standard and viscoelastic coagulation laboratory, blood loss and transfusion requirements at baseline, at the end of surgery (EOS) and the first postoperative day (POD 1).…”

Standard coagulation parameters over time. by Alexander Eckers (19106390)

“…Patients were randomized into three groups receiving either a 10% HES 130/0.42 solution, a 6% HES 130/0.42 solution or a crystalloid following a goal-directed hemodynamic algorithm. Endpoints were parameters of standard and viscoelastic coagulation laboratory, blood loss and transfusion requirements at baseline, at the end of surgery (EOS) and the first postoperative day (POD 1).…”

Von Willebrand parameters. by Alexander Eckers (19106390)

“…Patients were randomized into three groups receiving either a 10% HES 130/0.42 solution, a 6% HES 130/0.42 solution or a crystalloid following a goal-directed hemodynamic algorithm. Endpoints were parameters of standard and viscoelastic coagulation laboratory, blood loss and transfusion requirements at baseline, at the end of surgery (EOS) and the first postoperative day (POD 1).…”

S1 Data - by Ali Akbar Moosavi (17769033)

“…Then, radial basis functions (RBFNNs), multilayer perceptron (MLPNNs), hybrid genetic algorithm (GA-NNs), and particle swarm optimization (PSO-NNs) neural networks were utilized to develop PTFs and compared their accuracy with the traditional regression model (MLR) using statistical indices. …”

Personal details that are used as predictors. by Jari Turkia (17912475)

“…Considering the limited research on the reactions of ESRD patients, we collected dietary intake data and corresponding laboratory analyses from a cohort of 37 patients. …”

Nutrient predictors of the model. by Jari Turkia (17912475)

“…Considering the limited research on the reactions of ESRD patients, we collected dietary intake data and corresponding laboratory analyses from a cohort of 37 patients. …”

Posterior predictive check (PPC) of the final nutritional effect model. by Jari Turkia (17912475)

Description of the dataset. by Davide Ferrari (163517)

“…Our findings reveal that traditional ML models exhibit subpar performance across all training scenarios. In contrast, MOSR, specifically configured to minimize false negatives by optimizing also for the F1-Score, outperforms other ML algorithms and consistently delivers reliable results, irrespective of the training set balance with F1-Score.22 and.28 higher than any other alternative. …”

Anonymized data. by Michael Bonert (3751348)

“…Specimens were classified by neuroanatomical location, diagnostic category, and diagnosis with a hierarchical free text string-matching algorithm. All reports classified as probable metastasis per algorithm were reviewed by a pathologist. …”

Supporting data for “Digital immunochromatography: A bead-based bilateral zigzag flow immuno-assay with adaptive differential noise correction for rapid and precise protein detecti... by Zhiting Peng (9722516)

S1 Data set - by Ming-Song Zhao (757598)

“…Characteristic bands were selected from each type of spectra by the competitive adaptive reweighted sampling (CARS) algorithm, respectively. Thirdly, SOM prediction models were established based on random forest (RF), support vector regression (SVR), deep neural networks (DNN) and partial least squares regression (PLSR) methods using optimal spectral indexes, denoted here as SI-based models. …”

Biomarkers and neuroanatomical sites. by Michael Bonert (3751348)

“…Specimens were classified by neuroanatomical location, diagnostic category, and diagnosis with a hierarchical free text string-matching algorithm. All reports classified as probable metastasis per algorithm were reviewed by a pathologist. …”