Age feature importance. by Nicola Lazzarini (3528197)

The scenario used in Unity environment. Both the agent and target are spawned randomly at the start of the game anywhere in the game environment for each episode. by Enoch Solomon (21416703)

Study case/patient distribution algorithm. by Navid Behzadi Koochnai (18554931)

Proposed reinforcement learning architecture. by Enoch Solomon (21416703)

“…By using raw audio with visual cues, our objective is to enrich the decision-making process of the agent at each stage. Experimental evaluation were conducted employing Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) algorithms within ViZDoom and Unity reinforcement learning environments. …”

Table_1_An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: A two-stage prediction approach.pdf by Chung-Feng Liu (9369911)

Table_3_An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: A two-stage prediction approach.pdf by Chung-Feng Liu (9369911)

Table_2_An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: A two-stage prediction approach.pdf by Chung-Feng Liu (9369911)

Table_1_Comparison and analysis of multiple machine learning models for discriminating benign and malignant testicular lesions based on magnetic resonance imaging radiomics.DOCX by Yanhui Feng (1484833)

Data_Sheet_1_Construction and Interpretation of Prediction Model of Teicoplanin Trough Concentration via Machine Learning.docx by Pan Ma (400070)

Overview of the cohort definition process. by Nicola Lazzarini (3528197)

Data_Sheet_1_Comparison of Radiomics-Based Machine-Learning Classifiers in Diagnosis of Glioblastoma From Primary Central Nervous System Lymphoma.PDF by Chaoyue Chen (7298480)

“…<p>Purpose: The purpose of the current study was to evaluate the ability of magnetic resonance (MR) radiomics-based machine-learning algorithms in differentiating glioblastoma (GBM) from primary central nervous system lymphoma (PCNSL).…”

Data_Sheet_1_A Machine-Learning Approach for Dynamic Prediction of Sepsis-Induced Coagulopathy in Critically Ill Patients With Sepsis.docx by Qin-Yu Zhao (10014626)

Data_Sheet_2_A Machine-Learning Approach for Dynamic Prediction of Sepsis-Induced Coagulopathy in Critically Ill Patients With Sepsis.xlsx by Qin-Yu Zhao (10014626)

Historical forest aboveground biomass carbon storage (HFCS) dataset by Jinlong Chen (20554607)

“…By continuously adjusting the velocity and position of particles using the Particle Swarm Optimization algorithm, we obtained the optimal RF model with optimal model parameters as follows: a number of decision trees of 195, a maximum depth of 10 for each decision tree, a number of features of 12 to consider when looking for the best split, minimum number of samples of 4 required to split an internal node, and a minimum number of samples of 3 required to be at a leaf node.…”

Datasheet1_An explainable machine learning approach using contemporary UNOS data to identify patients who fail to bridge to heart transplantation.pdf by Mamoun T. Mardini (14672933)

“…We used the eXtreme Gradient Boosting (XGBoost) algorithm to build and validate ML models. We developed two models: (1) a comprehensive model that included all patients in our cohort and (2) separate models designed for each of the 11 UNOS regions.…”

CSPP instance by peixiang wang (19499344)

“…</b></p><p dir="ltr">Its primary function is to create structured datasets that simulate container terminal operations, which can then be used for developing, testing, and benchmarking optimization algorithms (e.g., for yard stacking strategies, vessel stowage planning).…”

Image_3_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF by Joshua A. Krachman (11660266)

“…We excluded encounters with gaps in recorded clinical data, encounters in which MV treatment occurred prior to HFNC, and cases electively intubated in the operating room. The primary study outcome was discriminatory capacity of generated machine learning algorithms to predict HFNC flow rate escalations as compared to each other and ROX indices using area under the receiver operating characteristic (AUROC) analyses. …”

Image_5_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF by Joshua A. Krachman (11660266)

“…We excluded encounters with gaps in recorded clinical data, encounters in which MV treatment occurred prior to HFNC, and cases electively intubated in the operating room. The primary study outcome was discriminatory capacity of generated machine learning algorithms to predict HFNC flow rate escalations as compared to each other and ROX indices using area under the receiver operating characteristic (AUROC) analyses. …”

Table_3_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.docx by Joshua A. Krachman (11660266)

“…We excluded encounters with gaps in recorded clinical data, encounters in which MV treatment occurred prior to HFNC, and cases electively intubated in the operating room. The primary study outcome was discriminatory capacity of generated machine learning algorithms to predict HFNC flow rate escalations as compared to each other and ROX indices using area under the receiver operating characteristic (AUROC) analyses. …”

Table_1_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.docx by Joshua A. Krachman (11660266)

“…We excluded encounters with gaps in recorded clinical data, encounters in which MV treatment occurred prior to HFNC, and cases electively intubated in the operating room. The primary study outcome was discriminatory capacity of generated machine learning algorithms to predict HFNC flow rate escalations as compared to each other and ROX indices using area under the receiver operating characteristic (AUROC) analyses. …”