Machine learning algorithm as a diagnostic tool for hypoadrenocorticism in dogs

Reagan, Krystle L.; Reagan, Brendan; Gilor, Chen

doi:10.1016/j.domaniend.2019.106396

“…The boosting-based algorithm AdaBoost iteratively trained weak learners and summarized these weak learners’ results into a weighted sum [31]. Multiple variants of Adaboost were proposed for recognizing human actions [32], diagnosing the dog hypoadrenocorticism [33], and predicting protein binding sites [34], etc.…”

Section: Methodsmentioning

confidence: 99%

Severity detection for the coronavirus disease 2019 (COVID-19) patients using a machine learning model based on the blood and urine tests

Yao

¹

,

Zhang

²

,

Zhang

³

et al. 2020

Preprint

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The recent outbreak of the coronavirus disease-2019 (COVID-19) caused serious challenges to the human society in China and across the world. COVID-19 induced pneumonia in human hosts and carried a highly inter-person contagiousness. The COVID-19 patients may carry severe symptoms, and some of them may even die of major organ failures. This study utilized the machine learning algorithms to build the COVID-19 severeness detection model. Support vector machine (SVM) demonstrated a promising detection accuracy after 32 features were detected to be significantly associated with the COVID-19 severeness. These 32 features were further screened for inter-feature redundancies. The final SVM model was trained using 28 features and achieved the overall accuracy 0.8148. This work may facilitate the risk estimation of whether the COVID-19 patients would develop the severe symptoms. The 28 COVID-19 severeness associated biomarkers may also be investigated for their underlining mechanisms how they were involved in the COVID-19 infections.

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“…2011, Reagan et al . 2019). Some risk factors associated with hypoadrenocorticism in dogs have been described.…”

Section: Introductionmentioning

confidence: 99%

Hypoadrenocorticism in dogs underUKprimary veterinary care: frequency, clinical approaches and risk factors

Schofield

¹

,

Woolhead²,

Johnson

³

et al. 2021

J of Small Animal Practice

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Objectives To estimate the frequency, clinical approaches and risk factors of hypoadrenocorticism in dogs under UK primary veterinary care. Materials and Methods Dogs diagnosed with hypoadrenocorticism were identified from the UK VetCompass™ programme by searching anonymised electronic patient records. Pre‐existing and newly diagnosed cases of disease during 2016 were included. Cases were further sub‐categorised as having a laboratory‐confirmed or presumed diagnosis of hypoadrenocorticism based on the information recorded in the electronic patient records. Descriptive data were manually extracted. Multivariable logistic regression methods were used to identify demographic risk factors. Results There were 177 hypoadrenocorticism cases identified from 905,543 dogs in 2016; 72 laboratory‐confirmed and 105 presumed. The 1‐year period prevalence for hypoadrenocorticism in all dogs was 0.06% (95% confidence interval: 0.05‐0.07%). The most common presenting clinical signs in laboratory‐confirmed dogs were lethargy (51/66, 77.3%), anorexia (48/66, 66.7%) and vomiting (48/66, 66.7%). Hyperkalaemia was reported in 47 of 53 (88.7%), hyponatraemia in 46 of 53 (86.8%). Median sodium: potassium ratio was 19.00 (interquartile range: 16.20‐20.60). Breed, age, neuter status and insurance status were associated with a laboratory‐confirmed diagnosis of hypoadrenocorticism. No sex association with hypoadrenocorticism was observed in the multivariable model. The standard poodle had 51.38 times the odds (95% CI: 14.49‐182.18) of hypoadrenocorticism compared with crossbreeds. The labradoodle and West Highland white terrier also had increased odds. Clinical Significance This is the first epidemiological study to report on hypoadrenocorticism in dogs within the UK primary‐care population. These results provide benchmark data of current veterinary activity relating to hypoadrenocorticism in primary‐care practices.

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“…The boosting-based algorithm AdaBoost iteratively trained weak learners and summarized these weak learners' results into a weighted sum (Ratsch et al, 2001). Multiple variants of Adaboost were proposed for recognizing human actions (Lv and Nevatia, 2006), diagnosing the dog hypoadrenocorticism (Reagan et al, 2019), and predicting protein binding sites (Qiao and Xie, 2019), etc.…”

Section: Prediction Algorithmsmentioning

confidence: 99%

Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests

Yao

¹

,

Zhang

²

,

Zhang

³

et al. 2020

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

The recent outbreak of the coronavirus disease-2019 (COVID-19) caused serious challenges to the human society in China and across the world. COVID-19 induced pneumonia in human hosts and carried a highly inter-person contagiousness. The COVID-19 patients may carry severe symptoms, and some of them may even die of major organ failures. This study utilized the machine learning algorithms to build the COVID-19 severeness detection model. Support vector machine (SVM) demonstrated a promising detection accuracy after 32 features were detected to be significantly associated with the COVID-19 severeness. These 32 features were further screened for inter-feature redundancies. The final SVM model was trained using 28 features and achieved the overall accuracy 0.8148. This work may facilitate the risk estimation of whether the COVID-19 patients would develop the severe symptoms. The 28 COVID-19 severeness associated biomarkers may also be investigated for their underlining mechanisms how they were involved in the COVID-19 infections.

show abstract

Machine learning algorithm as a diagnostic tool for hypoadrenocorticism in dogs

Cited by 27 publications

References 23 publications

Severity detection for the coronavirus disease 2019 (COVID-19) patients using a machine learning model based on the blood and urine tests

Severity detection for the coronavirus disease 2019 (COVID-19) patients using a machine learning model based on the blood and urine tests

Hypoadrenocorticism in dogs underUKprimary veterinary care: frequency, clinical approaches and risk factors

Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests

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