A machine learning-based framework to identify type 2 diabetes through electronic health records

TL;DR: It is found that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art.

TL;DR: In this paper, the authors present an overview of various application areas in healthcare that leverage ML/DL from security and privacy point of view and present associated challenges and potential methods to ensure secure and privacy-preserving ML for healthcare applications.

TL;DR: This paper presents a comprehensive view on “Data Science” including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios.

TL;DR: Experimental results on this dialogue system show that additional symptoms extracted from conversation can greatly improve the accuracy for disease identification and the dialogue system is able to collect these symptoms automatically and make a better diagnosis.

TL;DR: Since intensive glucose control with metformin appears to decrease the risk of diabetes-related endpoints in overweight diabetic patients, and is associated with less weight gain and fewer hypoglycaemic attacks than are insulin and sulphonylureas, it may be the first-line pharmacological therapy of choice in these patients.

TL;DR: A research initiative that aims to accelerate progress toward a new era of precision medicine, with a near-term focus on cancers and a longer-term aim to generate knowledge applicable to the whole range of health and disease.

TL;DR: It is proposed that one of the major long-term consequences of inadequate early nutrition is impaired development of the endocrine pancreas and a greatly increased susceptibility to the development of Type 2 diabetes.