Interpretable Machine Learning Approach Reveals Developmental Gene Expression Biomarkers for Cancer Patient Outcomes at Early Stages

Kamat, Alisha; Jin, Ting; Min, So Yeon; Talos, Flaminia; Almeida, Jonas S.; Wang, Daifeng

doi:10.1145/3233547.3233619

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“…While there does not exist an intuitive method for risk stratification of patients with ovarian cancer, there is strong evidence of the potential of machine learning for stratification of patients in other diseases such as heart failure (Rasmy et al 2018; Choi et al 2017; Chen et al 2019; Ng et al 2017), kidney disease (Makino et al 2019), and critical care(Chen, Su, et al 2015; Chen, Kumar, et al 2015; Katuwal and Chen 2016; Kamat et al 2018; Yu, Liu, and Nemati 2019; Johnson et al 2016). Furthermore, machine learning has been shown to be effective for readmission prediction (Rajkomar et al 2018; Desautels et al 2017; Chen, Su, et al 2015), drug adverse event prediction (Cheng and Zhao 2014).…”

Section: Introductionmentioning

confidence: 99%

Machine learning for ovarian cancer: lasso regression-based predictive model of early mortality in patients with stage I and stage II ovarian cancer

Chen

2020

Preprint

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While machine learning has shown promise in prediction of mortality in situations such as intensive care units, there is limited evidence of its application towards ovarian cancer. In this study, we extracted clinical covariates from a cohort of 273 patients with stage I and II ovarian cancer, and trained a machine learning algorithm, L2-regularized logistic regression, on the set of patients in prediction problem for mortality less than 20 months, representing the 25th percentile of overall survival. Our model achieves an AUC of 0.621, accuracy 0.761, sensitivity 0.130, positive predictive value 0.659, and F1 score 0.216. This study serves as a proof of concept for a predictive model customized towards mortality prediction for malignant neoplasm of the left testis, and can be adapted and generalized to related tumors such as spermatic cord and scrotal tumor types.

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Section: Introductionmentioning

confidence: 99%

Machine learning for ovarian cancer: lasso regression-based predictive model of early mortality in patients with stage I and stage II ovarian cancer

Chen

2020

Preprint

View full text Add to dashboard Cite

show abstract

Interpretable Machine Learning Approach Reveals Developmental Gene Expression Biomarkers for Cancer Patient Outcomes at Early Stages

Cited by 1 publication

References 0 publications

Machine learning for ovarian cancer: lasso regression-based predictive model of early mortality in patients with stage I and stage II ovarian cancer

Machine learning for ovarian cancer: lasso regression-based predictive model of early mortality in patients with stage I and stage II ovarian cancer

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