Development of a machine learning-based model for the prediction and progression of diabetic kidney disease: A single centred retrospective study.

Background: Diabetic kidney disease (DKD) is a diabetic microvascular complication often characterized by an unpredictable progression. Hence, early detection and recognition of patients vulnerable to progression is crucial.

Objective: To develop a prediction model to identify the stages of DKD and the factors contributing to progression to each stage using machine learning.

Antimicrobial resistance and the post antibiotic era: better late than never effort.

: Antimicrobial resistance (AMR) is a multi-layered problem with a calamitous impact on humans, livestock, the environment, and the biosphere. Initiatives and action plan to preclude AMR remain poorly implemented in India.: This review highlights essential factors contributing to AMR, epidemiology of the resistant bacteria, current treatment options, economic impact, and regulatory efforts initiated by the Indian government to tackle AMR.

Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction.

Objective: The neonatal period of a child is considered the most crucial phase of its physical development and future health. As per the World Health Organization, India has the highest number of pre-term births [1], with over 3.5 million babies born prematurely, and up to 40% of them are babies with low birth weights, highly prone to a multitude of diseases such as Jaundice, Sepsis, Apnea, and other Metabolic disorders.

Design Framework for a Data Mart in the Neonatal Intensive Care Unit.

Neonates who are critically ill are cared for in a neonatal intensive care unit (NICU) for continuous monitoring of their conditions. Physiological parameters such as heart rate, respiratory wave form, blood oxygen saturation, and body temperature are constantly monitored in the NICU. However, NICUs are not always equipped with a computer system for analyzing such data, identifying critical events, and providing decision support for a neonatologist.