MedTrace Logo

Establishing Malnutrition Diagnosis System by Using Artificial-intelligence Technology

NCT04776070 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 500

Last updated 2023-03-29

No results posted yet for this study

Summary

The prevalence of malnutrition is estimated at 30-50% of hospitalized patients in China. Disease-related malnutrition increases the risk of infection, mortality, length of hospitalization as well as the economic burden. National Nutrition Plan proposed to reduce malnutrition, but a clear, effective roadmap and protocol has not existed yet. Several factors impede to resolve the above challenges. They include :1) the low efficiency of current malnutrition diagnosis methods; 2) the lack of dynamic, standard method that can evaluate nutritional status in quantitative way. To this end, the investigators aim to establish an artificial-intelligence malnutrition diagnosis system to improve the application of malnutrition Clinical Pathway. Firstly, the investigators will establish a multidimensional malnutrition large data set, based on our previously built national hospital nutrition screening data set.

It will contain deep 3D facial images, semi-structured and structured electronic medical record. Then, the investigators will use ensemble learning algorithm to establish a fully automatic, artificial-intelligence malnutrition diagnosis model that includes both etiological and phenotypic diagnosis.

Conditions

  • Disease-related Malnutrition

Sponsors & Collaborators

  • Sichuan Academy of Medical Sciences

    collaborator OTHER

  • Peking Union Medical College

    collaborator OTHER

  • Peking Union Medical College Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
100 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-08-13
Primary Completion
2022-04-21
Completion
2022-05-21

Countries

  • China

Study Locations

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04776070 on ClinicalTrials.gov