Establishing Malnutrition Diagnosis System by Using Artificial-intelligence Technology
Establishing Malnutrition Diagnosis System by Using Artificial-intelligence Technology to Improve the Application of Clinical Pathway
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.
Study Overview
Status
Status
Conditions
Conditions
Study Type
Study Type
Enrollment (Actual)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Wei Chen
- Phone Number: +8669154095
- Email: chenw@pumch.cn
Study Locations
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Beijing
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Beijing, Beijing, China, 100010
- Dongcheng district,Peking union medical college hospital
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- Adults (≥18 years old);
- Within 48 hours of admission;
- Inpatients at high risk of malnutrition, such as malignant tumors, chronic obstructive pulmonary disease, etc;
- Han nationality;
- Able to given informed consent.
Exclusion Criteria:
- Patients with artificial facial changes (such as plastic surgery , head and neck radiotherapy , head and neck trauma);
- Diseases with special facial changes (such as acromegaly);
- High dose glucocorticoid users;
- Patients with facial edema;
- Emergency admission with an expected length of stay of less than 3 days;
- Other conditions researchers thought could not be included
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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malnutrition diagnosis
Time Frame: Within 48 hours of admission
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Using Global Leadership Initiative on Malnutrition(GLIM) to diagnose malnutrition among hospitalized patients
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Within 48 hours of admission
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Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- JS-2768
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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