Machine Learning–Based Interpretation and Visualization of Nonlinear Interactions in Prostate Cancer Survival

Li, Richard; Shinde, Ashwin; Liu, An; Glaser, Scott; Lyou, Yung; Yuh, Bertram; Wong, Jeffrey Y.C.; Amini, Arya

doi:10.1200/cci.20.00002

Cited by 59 publications

(45 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The risk and corresponding hazard ratio (HR) for severe condition were analysed using Python by fourfold cross-validation. Cut-off values of BG to predict COVID-19 severity were analysed using Python by the SHAP (SHapley Additive exPlanations) method [14]. A difference with a two-tailed P value < 0.05 was considered statistically significant.…”

Section: Discussionmentioning

confidence: 99%

Risk factors associated with the progression of COVID-19 in elderly diabetes patients

Zhang¹,

Wang²,

Wang³

et al. 2021

Diabetes Research and Clinical Practice

View full text Add to dashboard Cite

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

show abstract

Section: Discussionmentioning

confidence: 99%

Risk factors associated with the progression of COVID-19 in elderly diabetes patients

Zhang¹,

Wang²,

Wang³

et al. 2021

Diabetes Research and Clinical Practice

View full text Add to dashboard Cite

show abstract

“…With this approach, exact solutions can be found in the case of tree-based models [33,34]. SHAP values have been used fairly extensively in recent biomedical applications [65,32,66,67]. For each of the assessments, we provide overall global SHAP values across all 100 simulations.…”

Section: Identifying Digital Measures Of Interestmentioning

confidence: 99%

An aging focused unobtrusive and Privacy-Preserving Digital Behaviorome

Schuetz

Knobel

Botros

et al. 2021

Preprint

View full text Add to dashboard Cite

Digital measures are increasingly used as objective health measures in remote-monitoring settings. In addition to their use in purely clinical research, such as in clinical trials, one promising application area for sensor-derived digital measures is in technology-assisted ageing and ageing-related research. In this context, digital measures may be used to measure the risk of certain adverse events such as falls, and also to provide novel research insights into ageing and ageing-related conditions, like cognitive impairment. While major emphasis has been placed on deriving one or more digital measures from wearable devices, a more holistic approach inspired by systems biology that leverages large, non-exhaustive sets of digital measures may prove highly beneficial. Such an approach would be useful if combined with modern big data approaches like machine learning. As such, extensive sets of digital measures, which may be referred to as digital behavioromes, could help characterise new phenotypes in deep phenotyping efforts. These measures could also assist in the discovery of novel digital biomarkers or in the creation of digital clinical outcome assessments. While clinical research into digital measures focuses primarily on measures derived from wearable devices, proven technology used for long-term remote monitoring of older adults is generally contactless, unobtrusive, and privacy-preserving. In this context, we introduce and describe a digital behaviorome: a large, non-exhaustive set of digital measures based entirely on contactless, unobtrusive, and privacy-preserving sensor technologies. We also demonstrate how such a behaviorome can be used to build digital clinical outcome assessments that are relevant to ageing and derived from machine learning. These outcomes included fall risk, frailty, mild cognitive impairment, and late-life depression. With the exception of late-life depression, all digital outcome assessments demonstrated a promising ability (ROC AUC ≥ 0.7) to discriminate between positive and negative health outcomes, often in the range of comparable work with wearable devices. Finally, we highlight the possibility of using these digital behaviorome-based outcome assessments to discover novel potential digital biomarkers for each outcome. Here, we found reasonable contributors but also some potentially interesting new candidates regarding fall risk and mild cognitive impairment.

show abstract

“…Overall, their purpose is to generate an explicit knowledge representation (in terms understandable to humans) of the models' inner workings and of how they generate their predictions [23]. The use of explainable ML (XML) as a novel paradigm has started to grow in health care [24][25][26] and has been used in a few studies in oncology [27][28][29][30][31], but its potential remains largely unexplored and underused.…”

Section: Introductionmentioning

confidence: 99%

Using Explainable Machine Learning to Explore the Impact of Synoptic Reporting on Prostate Cancer

et al. 2022

View full text Add to dashboard Cite

Machine learning (ML) models have proven to be an attractive alternative to traditional statistical methods in oncology. However, they are often regarded as black boxes, hindering their adoption for answering real-life clinical questions. In this paper, we show a practical application of explainable machine learning (XML). Specifically, we explored the effect that synoptic reporting (SR; i.e., reports where data elements are presented as discrete data items) in Pathology has on the survival of a population of 14,878 Dutch prostate cancer patients. We compared the performance of a Cox Proportional Hazards model (CPH) against that of an eXtreme Gradient Boosting model (XGB) in predicting patient ranked survival. We found that the XGB model (c-index = 0.67) performed significantly better than the CPH (c-index = 0.58). Moreover, we used Shapley Additive Explanations (SHAP) values to generate a quantitative mathematical representation of how features—including usage of SR—contributed to the models’ output. The XGB model in combination with SHAP visualizations revealed interesting interaction effects between SR and the rest of the most important features. These results hint that SR has a moderate positive impact on predicted patient survival. Moreover, adding an explainability layer to predictive ML models can open their black box, making them more accessible and easier to understand by the user. This can make XML-based techniques appealing alternatives to the classical methods used in oncological research and in health care in general.

show abstract

Machine Learning–Based Interpretation and Visualization of Nonlinear Interactions in Prostate Cancer Survival

Cited by 59 publications

References 16 publications

Risk factors associated with the progression of COVID-19 in elderly diabetes patients

Risk factors associated with the progression of COVID-19 in elderly diabetes patients

An aging focused unobtrusive and Privacy-Preserving Digital Behaviorome

Using Explainable Machine Learning to Explore the Impact of Synoptic Reporting on Prostate Cancer

Contact Info

Product

Resources

About