Dynamic prediction of hypoxemia risk at different time points based on preoperative and intraoperative features: machine learning applications in outpatients undergoing esophagogastroduodenoscopy

Fang, Zhaojing; Zou, Daizun; Xiong, WeiGen; Bao, Huihui; Xin, Zhao; Chen, Chen; Si, Yanna; Zou, Jianjun

doi:10.1080/07853890.2023.2187878

Cited by 2 publications

(5 citation statements)

References 30 publications

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“…To predict the risk of hypoxemia, Fang et al developed and evaluated four machine-learning models based on preoperative and intraoperative data. The models showed satisfactory prognostic characteristics [134].…”

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confidence: 90%

“…The integration of digital technologies into pulse oximetry significantly enhances healthcare delivery by streamlining the flow of patient data, improving patient safety, enabling timely medical care, and augmenting the objectivity of clinical results and the accuracy of clinical outcomes, while reducing both the time and use of material resources. Generalized approaches to risk reduction when using pulse oximetry based on the example of a number of patents and scientific articles [104,113,114,120,130,[132][133][134][135][136]. Generalized approaches to risk reduction when using pulse oximetry based on the example of a number of patents and scientific articles [104,113,114,120,130,[132][133][134][135][136].…”

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confidence: 99%

“…Generalized approaches to risk reduction when using pulse oximetry based on the example of a number of patents and scientific articles [104,113,114,120,130,[132][133][134][135][136]. Generalized approaches to risk reduction when using pulse oximetry based on the example of a number of patents and scientific articles [104,113,114,120,130,[132][133][134][135][136].…”

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confidence: 99%

“…Figure 9. Generalized approaches to risk reduction when using pulse oximetry based on the example of a number of patents and scientific articles[104,113,114,120,130,[132][133][134][135][136].…”

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confidence: 99%

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Patent and Bibliometric Analysis of the Scientific Landscape of the Use of Pulse Oximeters and Their Prospects in the Field of Digital Medicine

Litvinova,

Hammerle,

Stoyanov

et al. 2023

Healthcare

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This study conducted a comprehensive patent and bibliometric analysis to elucidate the evolving scientific landscape surrounding the development and application of pulse oximeters, including in the field of digital medicine. Utilizing data from the Lens database for the period of 2000–2023, we identified the United States, China, the Republic of Korea, Japan, Canada, Australia, Taiwan, and the United Kingdom as the predominant countries in patent issuance for pulse oximeter technology. Our bibliometric analysis revealed a consistent temporal trend in both the volume of publications and citations, underscoring the growing importance of pulse oximeters in digitally-enabled medical practice. Using the VOSviewer software(version 1.6.18), we discerned six primary research clusters: (1) measurement accuracy; (2) integration with the Internet of Things; (3) applicability across diverse pathologies; (4) telemedicine and mobile applications; (5) artificial intelligence and deep learning; and (6) utilization in anesthesiology, resuscitation, and intensive care departments. The findings of this study indicate the prospects for leveraging digital technologies in the use of pulse oximetry in various fields of medicine, with implications for advancing the understanding, diagnosis, prevention, and treatment of cardio-respiratory pathologies. The conducted patent and bibliometric analysis allowed the identification of technical solutions to reduce the risks associated with pulse oximetry: improving precision and validity, technically improved clinical diagnostic use, and the use of machine learning.

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confidence: 90%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Patent and Bibliometric Analysis of the Scientific Landscape of the Use of Pulse Oximeters and Their Prospects in the Field of Digital Medicine

Litvinova,

Hammerle,

Stoyanov

et al. 2023

Healthcare

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“…The rapid advancement of machine learning and artificial intelligence has provided novel tools and methodologies for medical research(Fang et al, 2023). Machine learning excels in handling vast datasets, recognizing latent patterns, and predicting trends.…”

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confidence: 99%

The role of immune inflammatory response and inflammatory infiltrates in major depression disorder and atopic dermatitis: new insights from machine learning

Jiang,

Gong,

Yao

et al. 2023

Preprint

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Background: Major depression disorder (MDD) and atopic dermatitis (AD) are distinct disorders involving immune inflammatory responses. This study aimed to investigate the comorbid relationship between AD and MDD and to identify possible common mechanisms. Methods: We obtained AD and MDD data from the Gene Expression Omnibus (GEO) database. Differential expression analysis and the Genecard database were employed to identify shared genes associated with inflammatory diseases. These shared genes were then subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Hub genes were selected based on the protein-protein interactions using CytoHubba, and key regulatory genes were identified through enrichment analysis. Subsequently, we conducted immune infiltration and correlation analyses of the shared genes in AD. Finally, we employed three machine learning models to predict the significance of shared genes. Results: A total of 17 shared genes were identified in the AD_Inflammatory_MDD dataset (S100A9, PTGER2, PI3, SNCA, DAB2, PDGFA, FSTL1, CALD1, XK, UTS2, DHRS9, PARD3, NFIB, TMEM158, LIPH, RAB27B, and SH3BRL2). These genes were associated with biological processes such as the regulation of mesenchymal cell proliferation, mesenchymal cell proliferation, and glial cell differentiation. The neuroactive ligand-receptor interaction, IL-17 signaling, and Rap1 signaling pathways were significantly enriched in KEGG analysis. SNCA, S100A9, SH3BGRL2, RAB27B, TMEM158, DAB2, FSTL1, CALD1, and XK were identified as hub genes contributing to comorbid AD and MDD development. The three machine learning models consistently identified SNCA and PARD3 as important biomarkers. Conclusion: SNCA, S100A9, SH3BGRL2, RAB27B, TMEM158, DAB2, FSTL1, CALD1, and XK were identified as significant genes contributing to the development of AD and MDD comorbidities. Immune infiltration analysis showed a notable increase in the infiltration of various subtypes of CD4+ T cells, suggesting a potential association between the development of skin inflammation and the immune response. Across different machine learning models, SNCA and PARD3 consistently emerged as important biomarkers.

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Dynamic prediction of hypoxemia risk at different time points based on preoperative and intraoperative features: machine learning applications in outpatients undergoing esophagogastroduodenoscopy

Cited by 2 publications

References 30 publications

Patent and Bibliometric Analysis of the Scientific Landscape of the Use of Pulse Oximeters and Their Prospects in the Field of Digital Medicine

Patent and Bibliometric Analysis of the Scientific Landscape of the Use of Pulse Oximeters and Their Prospects in the Field of Digital Medicine

The role of immune inflammatory response and inflammatory infiltrates in major depression disorder and atopic dermatitis: new insights from machine learning

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