Contributing factors to hypophosphatemia development in critically Ill ventilated patients: a retrospective cohort study.

Hypophosphatemia (serum phosphate < 2.5 mg/dL) is a major concern when initiating nutritional support. We evaluated which factors contribute to hypophosphatemia development in critically ill patients, as well as the association between hypophosphatemia and mortality.

The future of artificial intelligence in clinical nutrition.

Purpose Of Review: Artificial intelligence has reached the clinical nutrition field. To perform personalized medicine, numerous tools can be used. In this review, we describe how the physician can utilize the growing healthcare databases to develop deep learning and machine learning algorithms, thus helping to improve screening, assessment, prediction of clinical events and outcomes related to clinical nutrition.

Gastrointestinal failure, big data and intensive care.

Purpose Of Review: Enteral feeding is the main route of administration of medical nutritional therapy in the critically ill. However, its failure is associated with increased complications. Machine learning and artificial intelligence have been used in intensive care to predict complications.

Correlations between First 72 h Hypophosphatemia, Energy Deficit, Length of Ventilation, and Mortality-A Retrospective Cohort Study.

Introduction: Hypophosphatemia may prolong ventilation and induce weaning failure. Some studies have associated hypophosphatemia with increased mortality. Starting or restarting nutrition in a critically ill patient may be associated with refeeding syndrome and hypophosphatemia.

Towards personalized nutritional treatment for malnutrition using machine learning-based screening tools.

Early identification of patients at risk of malnutrition or who are malnourished is crucial in order to start a timely and adequate nutritional therapy. Yet, despite the presence of many nutrition screening tools for use in the hospital setting, there is no consensus regarding the best tool as well as inadequate adherence to screening practices which impairs the achievement of effective nutritional therapy. In recent years, artificial intelligence and machine learning methods have been widely used, across multiple medical domains, to aid clinical decision making and to improve quality and efficiency of care.

Improvement in the Prediction of Coronary Heart Disease Risk by Using Artificial Neural Networks.

Background And Objectives: Cardiovascular diseases, such as coronary heart disease (CHD), are the main cause of mortality and morbidity worldwide. Although CHD cannot be entirely predicted by classic risk factors, it is preventable. Therefore, predicting CHD risk is crucial to clinical cardiology research, and the development of innovative methods for predicting CHD risk is of great practical interest.