Contextual Care Protocol using Neural Networks and Decision Trees

A contextual care protocol is used by a medical practitioner for patient healthcare, given the context or situation that the specified patient is in. This paper proposes a method to build an automated self-adapting protocol which can help make relevant, early decisions for effective healthcare delivery. The hybrid model leverages neural networks and decision trees. The neural network estimates the chances of each disease and each tree in the decision trees represents care protocol for a disease. These trees are subject to change in case of aberrations found by the diagnosticians. These corrections or prediction errors are clustered into similar groups for scalability and review by the experts. The corrections as suggested by the experts are incorporated into the model

To study fasting and post prandial lipid profile in type 2 diabetes mellitus in comparison to non diabetics

Background: Postprandial diabetic dyslipidemia creates proatherogenic conditions which are associated with microvascular and macrovascular complications. Its timely identification might help prevent complications. Aims and Objectives: The aims of this study were to assess the fasting and postprandial lipid abnormalities in type 2 diabetes mellitus (DM) in comparison to non-diabetic patients attending SGMH, Rewa, M.P. Materials and Methods: This was a cross-sectional case–control study done from April 2021 to March 2022 in SSMC and SGMH Rewa (M.P). 200 cases and 200 controls taken as per inclusion and exclusion criteria, age, and sex matched. Relevant examination and investigations including fasting and postprandial lipid profile were done. All data compiled and compared with the previous studies. Results: The comparative findings of fasting and postprandial lipid profile in type 2 DM compared to controls (non-diabetics) revealed the following observations. In fasting state, 56% cases and 30% controls had triglyceride levels of >150 mg/dL, while, in postprandial state, it was 82% cases and 32% controls. In fasting state, 28% cases and 8% controls had very low-density lipoproteins-cholesterol (VLDL-C) levels of >40 mg/dL, while, in postprandial state, it showed 46% cases and 14% of controls. In fasting state, 70% cases and 60% controls had high-density lipoproteins-cholesterol (HDL-C) levels of <35 mg/dL, while, in postprandial state, it was found in 80% cases and 58% controls. Conclusion: In the postprandial state, there was significant hyper-triglyceridemia, increased VLDL-C, and decreased HDL-C levels in cases than controls. In the fasting state, there was significant hyper-triglyceridemia and increased VLDL-C levels in cases than controls

An Introduction to Lifelong Supervised Learning

This primer is an attempt to provide a detailed summary of the different facets of lifelong learning. We start with Chapter 2 which provides a high-level overview of lifelong learning systems. In this chapter, we discuss prominent scenarios in lifelong learning (Section 2.4), provide 8 Introduction a high-level organization of different lifelong learning approaches (Section 2.5), enumerate the desiderata for an ideal lifelong learning system (Section 2.6), discuss how lifelong learning is related to other learning paradigms (Section 2.7), describe common metrics used to evaluate lifelong learning systems (Section 2.8). This chapter is more useful for readers who are new to lifelong learning and want to get introduced to the field without focusing on specific approaches or benchmarks. The remaining chapters focus on specific aspects (either learning algorithms or benchmarks) and are more useful for readers who are looking for specific approaches or benchmarks. Chapter 3 focuses on regularization-based approaches that do not assume access to any data from previous tasks. Chapter 4 discusses memory-based approaches that typically use a replay buffer or an episodic memory to save subset of data across different tasks. Chapter 5 focuses on different architecture families (and their instantiations) that have been proposed for training lifelong learning systems. Following these different classes of learning algorithms, we discuss the commonly used evaluation benchmarks and metrics for lifelong learning (Chapter 6) and wrap up with a discussion of future challenges and important research directions in Chapter 7.Comment: Lifelong Learning Prime