A deep-learning algorithm interpreting preoperative ECGs can improve discrimination of postoperative mortality. The deep-learning algorithm worked equally well for risk stratification of cardiac surgeries, non-cardiac surgeries, and catheterisation laboratory procedures, and was validated in three independent health-care systems. This algorithm can provide additional information to clinicians making the decision to perform medical procedures and stratify the risk of future complications.

Acquired Resistance to Therapy Network (ARTNet) focuses on the mechanistic bases of acquired resistance to cancer therapies and disease recurrence. Its goal is to balance basic, pre-clinical, and translational research that will enable hypothesis testing on the biological basis of resistance in clinically relevant models.

While the vast majority of today’s IT shops have deployed server virtualization technologies, a significant minority of operations teams feel that they have all of the management tools they need

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Background: Artificial intelligence (AI)–enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provide

Background Despite remarkable advances in cancer research, cancer remains one of the leading causes of death worldwide. Early detection of cancer and localization of the tissue of its origin are key to effective treatment. Here, we leverage technological advances in machine learning or artificial intelligence to design a novel framework for cancer diagnostics. Our proposed framework detects cancers and their tissues of origin using a unified model of cancers encompassing 33 cancers represented in The Cancer Genome Atlas (TCGA). Our model exploits the learned features of different cancers reflected in the respective dysregulated epigenomes, which arise early in carcinogenesis and differ remarkably between different cancer types or subtypes, thus holding a great promise in early cancer detection. Results Our comprehensive assessment of the proposed model on the 33 different tissues of origin demonstrates its ability to detect and classify cancers to a high accuracy (> 99% overall F-measure). Furthermore, our model distinguishes cancers from pre-cancerous lesions to metastatic tumors and discriminates between hypomethylation changes due to age related epigenetic drift and true cancer. Conclusions Beyond detection of primary cancers, our proposed computational model also robustly detects tissues of origin of secondary cancers, including metastatic cancers, second primary cancers, and cancers of unknown primaries. Our assessment revealed the ability of this model to characterize pre-cancer samples, a significant step forward in early cancer detection. Deployed broadly this model can deliver accurate diagnosis for a greatly expanded target patient population.

Scientific Reports - Development and validation of a machine learning method to predict intraoperative red blood cell transfusions in cardiothoracic surgery

PACENET is a complete electronic database for managing the implantation and follow up of all pacemakers, ICDs and other implanted cardiac pacing devices.

KecNet: A Light Neural Network for Arrhythmia Classification Based on Knowledge Reinforcement

https://preop.com/knee-replacement-surgery/Knee Replacement SurgeryKnee replacement surgery, also known as knee arthroplasty, is a procedure that involves re...