123 machine learning datasets
123 dataset results
Objective This study introduces the BlendedICU dataset, a massive dataset of international intensive care data. This dataset aims to facilitate generalizability studies of machine learning models, as well as statistical studies of clinical practices in the intensive care units.
We introduce an open-source physical-layer dataset of Bluetooth Low Energy (BLE) IoT sensor devices recorded in an anechoic chamber using USRP x310. With a 100Msps sampling rate, it covers the entire BLE spectrum, featuring on-body and off-body scenarios with 13 BLE devices (ESP32s) from the same manufacturer. the goal is to study the physical layer characteristics of both on-body and off-body signals. The dataset is also available through MongoDB with a Python tool for analysis; for more details, please visit our GitHub page. https://github.com/mkashani-phd/BLEWBAN_Dataset
This collection consists of DICOM images and DICOM Segmentation Objects (DSOs) for 197 patients with Colorectal Liver Metastases (CRLM). The collection consists of a large, single-institution consecutive series of patients that underwent resection of CRLM and matched preoperative computed tomography (CT) scans for quantitative image analysis. Inclusion criteria were (a) pathologically confirmed resected CRLM, (b) available data from pathologic analysis of the underlying non-tumoral liver parenchyma and hepatic tumor, (c) available preoperative conventional portal venous contrast-enhanced multi-detector computed tomography (MDCT) performed within 6 weeks of hepatic resection. Patients with 90-day mortality or that had less than 24 months of follow-up were excluded. Additionally, because pathologic and radiographic alterations of the non-tumoral liver parenchyma caused by hepatic artery infusion (HAI) of chemotherapy are not well described, any patient who received preoperative HAI was e