CamDavidsonPilon/lifelines: v0.23.4

Davidson-Pilon, Cameron; Kalderstam, Jonas; Zivich, Paul N; Kuhn, Ben; Williamson, Mike; AbdealiJK,; Fioré-Gartland, Andrew; Moneda, Luis; Gabriel, Joseph M.; WIlson, Daniel; Parij, Alex; Stark, Kyle; Anton, Steven R.; Besson, Lilian; Jona,; Gadgil, Harsh; Golland, Dave; Hussey, Sean; Kumar, Ravin; Noorbakhsh, Javad; Klintberg, Andreas; dhuynh,; Medvinsky, Dmitry; Zgonjanin, Denis; Katz, Daniel S.; Chen, Daniel; Ahern, Christopher A.; Fournier, Chris; Moncada‐Torres, Arturo; Rendeiro, André F.

doi:10.5281/zenodo.3576382

Cited by 2 publications

(1 citation statement)

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“…Applying VIF before developing the model is essential to remove highly correlated variables, as the models consisting of them fails to converge because of non-invertibility of singular matrices. The model is then developed in Python's lifeline package (Davidson-Pilon et al, 2019) starting with all the remained variables and iteratively removing insignificant variables. Models are developed over a training dataset, consisting of 80% of the data, and evaluated on a test dataset, both of which selected randomly.…”

Section: Cox Proportional Hazards Modelmentioning

confidence: 99%

Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning

Kalatian,

Farooq

2020

Preprint

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To ensure pedestrian friendly streets in the era of automated vehicles, reassessment of current policies, practices, design, rules and regulations of urban areas is of importance. This study investigates pedestrian crossing behaviour, as an important element of urban dynamics that is expected to be affected by the presence of automated vehicles. For this purpose, an interpretable machine learning framework is proposed to explore factors affecting pedestrians' wait time before crossing mid-block crosswalks in the presence of automated vehicles. To collect rich behavioural data, we developed a dynamic and immersive virtual reality experiment, with 180 participants from a heterogeneous population in 4 different locations in the Greater Toronto Area (GTA). Pedestrian wait time behaviour is then analyzed using a data-driven Cox Proportional Hazards (CPH) model, in which the linear combination of the covariates is replaced by a flexible non-linear deep neural network. The proposed model achieved a 5% improvement in goodness of fit, but more importantly, enabled us to incorporate a richer set of covariates. A game theoretic based interpretability method is used to understand the contribution of different covariates to the time pedestrians wait before crossing. Results show that the presence of automated vehicles on roads, wider lane widths, high density on roads, limited sight distance, and lack of walking habits are the main contributing factors to longer wait times. Our study suggested that, to move towards pedestrian-friendly urban areas, national level educational programs for children, enhanced safety measures for seniors, promotion of active modes of transportation, and revised traffic rules and regulations should be considered.

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Section: Cox Proportional Hazards Modelmentioning

confidence: 99%

Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning

Kalatian,

Farooq

2020

Preprint

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show abstract

DeepBTS: Prediction of Recurrence-free Survival of Non-small Cell Lung Cancer Using a Time-binned Deep Neural Network

Lee

Chun

Hong

et al. 2020

Sci Rep

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Accurate prediction of non-small cell lung cancer (nScLc) prognosis after surgery remains challenging. the cox proportional hazard (pH) model is widely used, however, there are some limitations associated with it. in this study, we developed novel neural network models called binned time survival analysis (DeepBTS) models using 30 clinico-pathological features of surgically resected NSCLC patients (training cohort, n = 1,022; external validation cohort, n = 298). We employed the root-mean-square error (in the supervised learning model, s-DeepBtS) or negative log-likelihood (in the semi-unsupervised learning model, su-DeepBtS) as the loss function. the su-DeepBtS algorithm achieved better performance (c-index = 0.7306; AUC = 0.7677) than the other models (Cox PH: C-index = 0.7048 and AUC = 0.7390; s-DeepBtS: c-index = 0.7126 and AUC = 0.7420). The top 14 features were selected using su-DeepBTS model as a selector and could distinguish the low-and high-risk groups in the training cohort (p = 1.86 × 10 −11) and validation cohort (p = 1.04 × 10 −10). When trained with the optimal feature set for each model, the su-DeepBtS model could predict the prognoses of nScLc better than the traditional model, especially in stage i patients. follow-up studies using combined radiological, pathological imaging, and genomic data to enhance the performance of our model are ongoing.

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CamDavidsonPilon/lifelines: v0.23.4

Cited by 2 publications

References 0 publications

Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning

Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning

DeepBTS: Prediction of Recurrence-free Survival of Non-small Cell Lung Cancer Using a Time-binned Deep Neural Network

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