NUTRITIONAL SUPPORT FEEDING EFFICIENCY

A computer-implemented method of treating a patient's and automated enteral feeding, comprising: monitoring a plurality of reflux-related parameters and at least one reflux event while the patient is automatically enterally fed by an enteral feeding controller according to a baseline feeding pr...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	IDDAN, Gavriel J, ELIA, Liron
Format:	Patent
Sprache:	eng ; fre ; ger
Schlagworte:	BABY COMFORTERS CONTAINERS SPECIALLY ADAPTED FOR MEDICAL OR PHARMACEUTICALPURPOSES DEVICES FOR ADMINISTERING FOOD OR MEDICINES ORALLY DEVICES FOR RECEIVING SPITTLE DEVICES OR METHODS SPECIALLY ADAPTED FOR BRINGINGPHARMACEUTICAL PRODUCTS INTO PARTICULAR PHYSICAL ORADMINISTERING FORMS DIAGNOSIS HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA HUMAN NECESSITIES HYGIENE IDENTIFICATION INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS MEDICAL OR VETERINARY SCIENCE PHYSICS SURGERY
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Beschreibung
Zusammenfassung:	A computer-implemented method of treating a patient's and automated enteral feeding, comprising: monitoring a plurality of reflux-related parameters and at least one reflux event while the patient is automatically enterally fed by an enteral feeding controller according to a baseline feeding profile including a target nutritional goal, training a classifier component of a model for predicting likelihood of a future reflux event according to an input of scheduled and/or predicted plurality of reflux-related parameters, the classifier trained according to computed correlations between the plurality of reflux-related parameters and the at least one reflux event, feeding scheduled and/or predicted reflux-related parameters into the trained classifier component of the model for outputting risk of likelihood of a future reflux event, and computing, by the model, an adjustment to the baseline feeding profile for reducing likelihood of the future reflux event and for meeting the target nutritional goal.