Application of deep learning to predict the low serum albumin in new hemodialysis patients.
Background: Serum albumin level is a crucial nutritional indicator for patients on dialysis. Approximately one-third of patients on hemodialysis (HD) have protein malnutrition. Therefore, the serum albumin level of patients on HD is strongly correlated with mortality. Methods: In study, the data set...
| Publicado en: | Nutrition & Metabolism Vol. 20; no. 1; pp. 1 - 22 |
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| Autores principales: | , , , , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
BioMed Central
4/24/2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=163294083&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 163294083 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17437075 1CYX jtl: Nutrition & Metabolism issn: 17437075 maglogo: N pubinfo: dt: 4/24/2023 vid: 20 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 163294083 163294083 163294083 10.1186/s12986-023-00746-z 163294083 ppf: 1 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Application of deep learning to predict the low serum albumin in new hemodialysis patients. aug: au: Yang, Cheng-Hong Chen, Yin-Syuan Chen, Jin-Bor Huang, Hsiu-Chen Chuang, Li-Yeh affil: Department of Information Management, Tainan University of Technology, Tainan, Taiwan sug: subj: Deep Learning Methods Serum Albumin Analysis Hemodialysis Adverse Effects Theory Validation Prediction Models Malnutrition Risk Factors Risk Assessment Human Taiwan Prospective Studies Logistic Regression Multivariate Analysis Dialysis Patients Age Factors Sex Factors Hypertension Complications Hemoglobins Alkaline Phosphatase Triglycerides Blood Algorithms Descriptive Statistics Middle Age Aged Female Male Funding Source Middle Aged: 45-64 years Aged: 65+ years Female Male ab: Background: Serum albumin level is a crucial nutritional indicator for patients on dialysis. Approximately one-third of patients on hemodialysis (HD) have protein malnutrition. Therefore, the serum albumin level of patients on HD is strongly correlated with mortality. Methods: In study, the data sets were obtained from the longitudinal electronic health records of the largest HD center in Taiwan from July 2011 to December 2015, included 1,567 new patients on HD who met the inclusion criteria. Multivariate logistic regression was performed to evaluate the association of clinical factors with low serum albumin, and the grasshopper optimization algorithm (GOA) was used for feature selection. The quantile g-computation method was used to calculate the weight ratio of each factor. Machine learning and deep learning (DL) methods were used to predict the low serum albumin. The area under the curve (AUC) and accuracy were calculated to determine the model performance. Results: Age, gender, hypertension, hemoglobin, iron, ferritin, sodium, potassium, calcium, creatinine, alkaline phosphatase, and triglyceride levels were significantly associated with low serum albumin. The AUC and accuracy of the GOA quantile g-computation weight model combined with the Bi-LSTM method were 98% and 95%, respectively. Conclusion: The GOA method was able to rapidly identify the optimal combination of factors associated with serum albumin in patients on HD, and the quantile g-computation with DL methods could determine the most effective GOA quantile g-computation weight prediction model. The serum albumin status of patients on HD can be predicted by the proposed model and accordingly provide patients with better a prognostic care and treatment. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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