Confusion matrix. by Shaik Ahamed Fayaz (19859670)

“…In this retrospective study, a total of 1236 PTB patients who were given treatment under a randomized controlled clinical trial at the ICMR-National Institute for Research in Tuberculosis, Chennai, India were considered for data analysis. The multiple ML models were developed and tested to identify the best algorithm to predict the sputum culture conversion of TB patients during the treatment period. …”

Baseline characteristics of TB patients. by Shaik Ahamed Fayaz (19859670)

“…In this retrospective study, a total of 1236 PTB patients who were given treatment under a randomized controlled clinical trial at the ICMR-National Institute for Research in Tuberculosis, Chennai, India were considered for data analysis. The multiple ML models were developed and tested to identify the best algorithm to predict the sputum culture conversion of TB patients during the treatment period. …”

This is a minimal dataset for this analysis. by Shaik Ahamed Fayaz (19859670)

“…In this retrospective study, a total of 1236 PTB patients who were given treatment under a randomized controlled clinical trial at the ICMR-National Institute for Research in Tuberculosis, Chennai, India were considered for data analysis. The multiple ML models were developed and tested to identify the best algorithm to predict the sputum culture conversion of TB patients during the treatment period. …”

Examples of cases in which the model failed to classify the parasites correctly, leading to misclassification and a drop in performance. by Hamid Cheraghi (22057476)

A Radiograph Dataset for the Classification, Localization, and Segmentation of Primary Bone Tumors by Shunhan Yao (20296506)

Symptom-wise Minimum and Maximum Membership Fluctuation over Iterations. by Sulav Baral (22477755)

Functional and Structural Characterization of Mechanosensitive Piezo1 Channel in Disease by Sophia Liu (722004)

“…</p><p><br></p><p dir="ltr">This is the first comprehensive structural analysis of PIEZO1 variants using multiple predictive tools. The findings highlight the limitations of current in-silico methods in resolving functional classifications and underscore the need for integrated biochemical and structural approaches to support clinical interpretation.…”

Normalized convergence time. by Song Qian (5031221)

“…The traditional artificial intelligence routing algorithm cannot deal with the low model prediction accuracy and poor generalization ability caused by large noise and small data volume. …”

VGR structure. by Song Qian (5031221)

“…The traditional artificial intelligence routing algorithm cannot deal with the low model prediction accuracy and poor generalization ability caused by large noise and small data volume. …”

Comparison of normalized throughput and load. by Song Qian (5031221)

“…The traditional artificial intelligence routing algorithm cannot deal with the low model prediction accuracy and poor generalization ability caused by large noise and small data volume. …”

Principle of transfer learning. by Song Qian (5031221)

“…The traditional artificial intelligence routing algorithm cannot deal with the low model prediction accuracy and poor generalization ability caused by large noise and small data volume. …”

Body-connected routing scenario. by Song Qian (5031221)

“…The traditional artificial intelligence routing algorithm cannot deal with the low model prediction accuracy and poor generalization ability caused by large noise and small data volume. …”

Data Sheet 1_Development of a urine-based metabolomics approach for multi-cancer screening and tumor origin prediction.docx by Xinping Xu (553652)

“…Background<p>Cancer remains a leading cause of mortality worldwide. A non-invasive screening solution was required for early diagnosis of cancer. …”

Patient Demographics. by Yiye Zhang (9028589)

“…Chart reviews categorized predicted cases across index ED discharge diagnosis and RVA root cause classifications. The best-performing model achieved an AUC of 0.87 in the development site (test set) and 0.75 in the independent validation set. …”

Characteristics of RVA visits. by Yiye Zhang (9028589)

“…Chart reviews categorized predicted cases across index ED discharge diagnosis and RVA root cause classifications. The best-performing model achieved an AUC of 0.87 in the development site (test set) and 0.75 in the independent validation set. …”

72-hr RVA Predictive Performance. by Yiye Zhang (9028589)

“…Chart reviews categorized predicted cases across index ED discharge diagnosis and RVA root cause classifications. The best-performing model achieved an AUC of 0.87 in the development site (test set) and 0.75 in the independent validation set. …”

SHAP visualization of 72-hour RVA features. by Yiye Zhang (9028589)

“…Chart reviews categorized predicted cases across index ED discharge diagnosis and RVA root cause classifications. The best-performing model achieved an AUC of 0.87 in the development site (test set) and 0.75 in the independent validation set. …”