Data Sheet 1_Machine learning based assessment of hoarseness severity: a multi-sensor approach centered on high-speed videoendoscopy.docx by Tobias Schraut (21488108)

“…A videoendoscopic model was developed by selecting a suitable classification algorithm and a minimal-optimal subset of glottal parameters. …”

Multivariate brain models of pain response can be interpreted as nested models that draw on increasing amounts of unique information. by Bogdan Petre (12488354)

Data_Sheet_1_A Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain.pdf by Justin L. Balsor (9310544)

Data_Sheet_1_A Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain.pdf by Justin L. Balsor (9310544)

Overall flowchart of the model. by Yao He (290155)

“…Our research objective is to develop and validate a multimodal deep learning radiology (MDLR) model based on the integration of multimodal data using deep learning radiology (DLR) scores from preoperative magnetic resonance imaging (MRI) images and clinical variables.…”

Training and validation loss of the model. by Yao He (290155)

“…Our research objective is to develop and validate a multimodal deep learning radiology (MDLR) model based on the integration of multimodal data using deep learning radiology (DLR) scores from preoperative magnetic resonance imaging (MRI) images and clinical variables.…”

Minimal anonymized dataset. by Yao He (290155)

“…Our research objective is to develop and validate a multimodal deep learning radiology (MDLR) model based on the integration of multimodal data using deep learning radiology (DLR) scores from preoperative magnetic resonance imaging (MRI) images and clinical variables.…”

Flowchart of the numbers of patients and images. by Yao He (290155)

“…Our research objective is to develop and validate a multimodal deep learning radiology (MDLR) model based on the integration of multimodal data using deep learning radiology (DLR) scores from preoperative magnetic resonance imaging (MRI) images and clinical variables.…”

Training and validation macro AUC of the model. by Yao He (290155)

“…Our research objective is to develop and validate a multimodal deep learning radiology (MDLR) model based on the integration of multimodal data using deep learning radiology (DLR) scores from preoperative magnetic resonance imaging (MRI) images and clinical variables.…”

Patient characteristics^a. by Yao He (290155)

“…Our research objective is to develop and validate a multimodal deep learning radiology (MDLR) model based on the integration of multimodal data using deep learning radiology (DLR) scores from preoperative magnetic resonance imaging (MRI) images and clinical variables.…”

Image 1_Machine learning-based personalized risk prediction model for breast cancer-related lymphedema after surgery.png by Xuemei Peng (2493622)

“…</p>Methods<p>Clinical and follow-up data were collected from patients who underwent breast cancer surgery between June 2020 and June 2025. A total of 38 variables were analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO) method for feature selection. …”

Table 1_Machine learning-based personalized risk prediction model for breast cancer-related lymphedema after surgery.xlsx by Xuemei Peng (2493622)

“…</p>Methods<p>Clinical and follow-up data were collected from patients who underwent breast cancer surgery between June 2020 and June 2025. A total of 38 variables were analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO) method for feature selection. …”