Assisting the diagnosis of Graves’ hyperthyroidism with pattern recognition methods and a set of three routine tests parameters, and their correlations with free T4 levels: Extension to male patients

Aoki, Sorama; Hoshi, Kenji; Kawakami, Junko; Sato, Kengo; Satoh, Ken; Mori, Keiichiro; Sugawara, Akira; Saito, Yoshihiko; Yoshida, Katsumi

doi:10.1016/j.biopha.2010.10.005

Cited by 9 publications

(12 citation statements)

References 9 publications

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“…For the hypothyroidism classification in this study, the hypothyroidism group was used as a positive label, whereas both the control and hyperthyroidism groups were negatively labeled (referred to as "crosstalk on"). On the other hand, related studies 16,17 performed classification by setting thyroid dysfunction patients (with hyperthyroidism or hypothyroidism) as a positive label and only the control group as a negative label (referred to as "crosstalk off"). Therefore, we evaluated the performance of the models with similar settings as these studies.…”

Section: Comparison With Related Studiesmentioning

confidence: 99%

“…Several previous studies revealed promising results from the use of machine learning approaches for predicting thyroid dysfunction (16,17).…”

Section: Comparison With Related Studiesmentioning

confidence: 99%

“…In another study, the incidence of myocardial infarction or cerebral infarction was predicted using the results of a health checkup 10 . Numerous studies have also attempted to assess the efficacy of detecting misdiagnosed diseases, including thyroid dysfunction [11][12][13][14][15][16][17] . Aoki et al 16,17 found that there were strong, multiple correlations between a set of routine clinical parameters and FT4 in patients with both overt hyperthyroidism and overt hypothyroidism.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous studies have also attempted to assess the efficacy of detecting misdiagnosed diseases, including thyroid dysfunction [11][12][13][14][15][16][17] . Aoki et al 16,17 found that there were strong, multiple correlations between a set of routine clinical parameters and FT4 in patients with both overt hyperthyroidism and overt hypothyroidism. These studies used pattern recognition methods such as neural networks and predicted the likelihood of thyroid dysfunction from a set of routine clinical tests.…”

Section: Introductionmentioning

confidence: 99%

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Thyroid dysfunction diagnosis from routine laboratory tests based on machine learning

Hu¹,

Asami²,

Iwakura

et al. 2021

Preprint

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Approximately 2.4 million patients need treatment for thyroid disease, including Graves disease and Hashimoto's disease, in Japan. However, only 450,000 of them are receiving treatment, and many patients with thyroid dysfunction remain largely overlooked. In this retrospective study, we aimed to screen patients with hyperthyroidism and hypothyroidism who would greatly benefit from prompt medical treatment, and examined routine laboratory finding data and machine learning algorithms to investigate whether such accurate and robust screening is possible to prevent overlooking and misdiagnosing thyroid dysfunction. We succeeded in developing a machine learning method to construct the classification model for detecting hyperthyroidism and hypothyroidism in patients using 11 routine laboratory tests. We collected electronic health record and medical checkup data from four hospitals in Japan. As a result of cross-validation and external evaluation, we achieved a high classification accuracy for the hyperthyroidism and hypothyroidism models.

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Section: Comparison With Related Studiesmentioning

confidence: 99%

“…Several previous studies revealed promising results from the use of machine learning approaches for predicting thyroid dysfunction (16,17).…”

Section: Comparison With Related Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Thyroid dysfunction diagnosis from routine laboratory tests based on machine learning

Hu¹,

Asami²,

Iwakura

et al. 2021

Preprint

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show abstract

“…Trends of each species in terms of pathological phases and Spearman's correlation coefficient in relation to clinical markers of metabolic disorders and chronic inflammation were analyzed. Self-organization map (SOM) and Bayesian regularized neural-network (BRNN) analysis were performed as described previously (Aoki et al, 2011).…”

Section: Tlc and Lc-ms/ms Analysis Of Gm3 Speciesmentioning

confidence: 99%

Homeostatic and pathogenic roles of GM 3 ganglioside molecular species in TLR 4 signaling in obesity

Kanoh

Nitta

et al. 2020

The EMBO Journal

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Innate immune signaling via TLR4 plays critical roles in pathogenesis of metabolic disorders, but the contribution of different lipid species to metabolic disorders and inflammatory diseases is less clear. GM3 ganglioside in human serum is composed of a variety of fatty acids, including long‐chain (LCFA) and very‐long‐chain (VLCFA). Analysis of circulating levels of human serum GM3 species from patients at different stages of insulin resistance and chronic inflammation reveals that levels of VLCFA‐GM3 increase significantly in metabolic disorders, while LCFA‐GM3 serum levels decrease. Specific GM3 species also correlates with disease symptoms. VLCFA‐GM3 levels increase in the adipose tissue of obese mice, and this is blocked in TLR4‐mutant mice. In cultured monocytes, GM3 by itself has no effect on TLR4 activation; however, VLCFA‐GM3 synergistically and selectively enhances TLR4 activation by LPS/HMGB1, while LCFA‐GM3 and unsaturated VLCFA‐GM3 suppresses TLR4 activation. GM3 interacts with the extracellular region of TLR4/MD2 complex to modulate dimerization/oligomerization. Ligand‐molecular docking analysis supports that VLCFA‐GM3 and LCFA‐GM3 act as agonist and antagonist of TLR4 activity, respectively, by differentially binding to the hydrophobic pocket of MD2. Our findings suggest that VLCFA‐GM3 is a risk factor for TLR4‐mediated disease progression.

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Assisting the diagnosis of Cushing syndrome by pattern recognition methods, using a combination of eight routine tests and their multiple correlation with serum cortisol

Kudo

Satoh

Aoki

et al. 2014

Biomedicine & Aging Pathology

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Assisting the diagnosis of Graves’ hyperthyroidism with pattern recognition methods and a set of three routine tests parameters, and their correlations with free T4 levels: Extension to male patients

Cited by 9 publications

References 9 publications

Thyroid dysfunction diagnosis from routine laboratory tests based on machine learning

Thyroid dysfunction diagnosis from routine laboratory tests based on machine learning

Homeostatic and pathogenic roles of GM 3 ganglioside molecular species in TLR 4 signaling in obesity

Assisting the diagnosis of Cushing syndrome by pattern recognition methods, using a combination of eight routine tests and their multiple correlation with serum cortisol

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