The NUTRIC score (Nutrition Risk in the Critically Ill) is a method designed to calculate the risk of critically ill patients developing adverse conditions that is likely be improved by optimal nutritional therapy The objective of this study is to validate the NUTRIC score for aggressive nutrition therapy and identify critically ill patients that are most likely to benefit from macronutrients with an aim to improving mortality rate A logistic model is proposed to identify the relationship between the observed parameters of NUTRIC score like age, APACHE II score, SOFA score and number of co-morbidities using statistical methods and further authenticate it The relationship between these parameters is often aggravated in the ICU due to the hypermetabolic nature of critical illness This care can be extended by use of IoT devices which may be wearable for monitoring patients health It will help not only to keep track patient's health, but to improve how the physicians deliver care as well New technology innovations can be leveraged in nutritional health care context to monitor patients remotely

Extending Nutritional Risk Screening in Critically Ill Patients using IoT devices

It is a big challenge to assess and monitor the well-being of geriatric people due to the rise in the average population and keep track of their health status on a daily basis. Nutrition screening is a method used to find the nutrition risk in patients that can be improved by nutritional therapy. Nutrition screening strategy helps to evaluate the nutrition risk of patients which can be improved by nutritional therapy. There are different types of screening forms identified based on age group and hospital settings. The primary objective of the paper is to study the screening forms MUST, NRS-2002, and MNA to identify the correlation between the form parameters using statistical methods. The purpose of this study is to design a system to understand the nutritional status of the patient and identify patients that will be benefited from nutrition therapy. A classification algorithm of data mining is proposed to identify patients with comorbidities and hence suffering from malnutrition. It will help physicians to take the daily decision-making activities to prevent the events before occurring. The patients’ health care can be monitored by the use of devices like mobile applications by observing patients’ nutrition intake. The developed system helps to keep track of the geriatric patient’s health and improve the method of how dietician or practitioner delivers care when the patient is monitored remotely.

Vaishali P. Suryawanshi

Papers

Extending Nutritional Risk Screening in Critically Ill Patients using IoT devices

Malnutrition Identification in Geriatric Patients Using Data Mining

Presleep Cognitive Thoughts Impact on Subsequent Day's After-Sleep Thoughts using Regression