Machine learning-based data analytic approaches for evaluating post-natal mouse respiratory physiological evolution

Wang, Wesley; Alzate‐Correa, Diego; Alves, Michele Joana; Jones, Mikayla; Garcia, Alfredo J.; Zhao, Jing; Czeisler, Catherine; Otero, José

doi:10.1016/j.resp.2020.103558

Cited by 5 publications

(3 citation statements)

References 49 publications

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“… 18 Using the factoextra package, PCA was performed over the data set and relations of clinical features were visualized using eigenvector plotting of our PCA as described in our previous work. 19 , 20 …”

Section: Methodsmentioning

confidence: 99%

Unsupervised machine learning models reveal predictive clinical markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation

Wang,

Kumm,

et al. 2023

Neuro-Oncology Advances

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Background Glioblastoma is a malignant brain tumor requiring careful clinical monitoring even after primary management. Personalized medicine has suggested use of various molecular biomarkers as predictors of patient prognosis or factors utilized for clinical decision making. However, the accessibility of such molecular testing poses a constraint for various institutes requiring identification of low-cost predictive biomarkers to ensure equitable care. Methods We collected retrospective data from patients seen at Ohio State University, University of Mississippi, Barretos Cancer Hospital (Brazil), and FLENI (Argentina) who were managed for glioblastoma—amounting to 581 patient records documented using REDCap. Patients were evaluated using an unsupervised machine learning approach comprised of dimensionality reduction and eigenvector analysis to visualize the inter-relationship of collected clinical features. Results We discovered that serum white blood cell count of a patient during baseline planning for treatment was predictive of overall survival with an over 6-month median survival difference between the upper and lower quartiles of white blood cell count. By utilizing an objective PD-L1 immunohistochemistry quantification algorithm, we were further able to identify an increase in PD-L1 expression in glioblastoma patients with high serum white blood cell counts. Conclusions These findings suggest that in a subset of glioblastoma patients the incorporation of white blood cell count and PD-L1 expression in the brain tumor biopsy as simple biomarkers predicting glioblastoma patient survival. Moreover, machine learning models allow distillation of complex clinical datasets to uncover novel and meaningful clinical relationships.

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Section: Methodsmentioning

confidence: 99%

Unsupervised machine learning models reveal predictive clinical markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation

Wang,

Kumm,

et al. 2023

Neuro-Oncology Advances

Self Cite

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show abstract

“…1; 18). Using the factoextra package, principal component analysis (PCA) was performed over the dataset and relations of clinical features were visualized using eigenvector plotting of our PCA as described in our previous work (19)(20).…”

Section: Exploration Of Clinical Featuresmentioning

confidence: 99%

Unsupervised machine learning models reveal predictive markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation

Wang

Kumm

et al. 2023

Preprint

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Purpose: Glioblastoma is a malignant brain tumor requiring careful clinical monitoring even after primary management. Personalized medicine has suggested use of various molecular biomarkers as predictors of patient prognosis or factors utilized for clinical decision making. However, the accessibility of such molecular testing poses a constraint for various institutes requiring identification of low-cost predictive biomarkers to ensure equitable care. Methods: We collected retrospective data from patients seen at Ohio State University, University of Mississippi, Barretos Cancer Hospital (Brazil), and FLENI (Argentina) who were managed for glioblastoma—amounting to nearly 600 patient records documented using REDCap. Patients were evaluated using an unsupervised machine learning approach comprised of dimensionality reduction and eigenvector analysis to visualize the inter-relationship of collected clinical features. Results: We discovered that white blood cell count of a patient during baseline planning for treatment was predictive of overall survival with an over 6-month median survival difference between the upper and lower quartiles of white blood cell count. By utilizing an objective PDL-1 immunohistochemistry quantification algorithm, we were further able to identify an increase in PDL-1 expression in glioblastoma patients with high white blood cell counts. Conclusion: These findings suggest that in a subset of glioblastoma patients the incorporation of white blood cell count and PDL-1 expression in the brain tumor biopsy as simple biomarkers predicting glioblastoma patient survival. Moreover, use of machine learning models allows us to visualize complex clinical datasets to uncover novel clinical relationships.

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“…The severe effect caused by the spread of the SARS-CoV-2 virus, which resulted in an uncontrollable infectivity rate and a significant number of deaths (Seo et al, 2020;Taleghani and Taghipour, 2021). It has recapitulated the historical evidence caused by pneumonias like SARS and MERS (Ramanathan et al, 2022b;Wang et al, 2021). Medical professionals have been compelled to concentrate on the disease's virology, physiology, and immunology for clinical practices to stop the pandemic due to the widespread COVID-19 global pandemic.…”

Section: Introductionmentioning

confidence: 99%

Identification of COVID-19 Infection from Features Extraction of Exhaled Breath Using Signal Processing Methods

Malarvili,

Ramanathan,

Manikam

et al. 2024

Preprint

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The goal of the exhaled breath waveform used in ventilation monitoring is to improve COVID-19 illness detection. Using exhaled breath patterns to differentiate between COVID and non-COVID healthy individuals, an algorithm for valid exhaled breath waveform segmentation and feature computation is created to identify the COVID-19 infection. A two-minute exhaled breath pattern was recorded using a device and a nasal cannula sampling tube, resulting in the collection of exhaled breath waveforms from each subject. The developed algorithm is utilized to evaluate the valid exhaled breath waveforms and compute the features classified to distinguish between individuals who contracted COVID-19 infection and this who are healthy. Slope e2, activity e2 and intersection angle of expiration and inspiration phase showed p-value of 0.000, denoting the strong significance difference between COVID-19 patients and non-COVID healthy individuals. The statistical analyses revealed p-values of 0.039, 0.008, and 0.024 for area e2, mobility of e2 and complexity e3, indicating its significance in differentiating COVID-19 patients with non-COVID healthy individuals. The slope, area, and intersection angle as significant features showed good predictive power for compliance with p-value analysis with area under the receiver operating characteristic curve with 0.667, 0.693, and 0.775. Slope of e2, area of e2, intersection angle of expiration and inspiration phases are identified as the promising features to be chosen in discriminating COVID and non-COVID individuals. Trial registration: Clinical trial approval by Medical Research and Ethics Committee (MREC), 53 Malaysia, NMRR-21-763-59692. Registered 9th June 2021 and valid till 8th June 2023.

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Machine learning-based data analytic approaches for evaluating post-natal mouse respiratory physiological evolution

Cited by 5 publications

References 49 publications

Unsupervised machine learning models reveal predictive clinical markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation

Unsupervised machine learning models reveal predictive clinical markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation

Unsupervised machine learning models reveal predictive markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation

Identification of COVID-19 Infection from Features Extraction of Exhaled Breath Using Signal Processing Methods

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