Countdown Regression: Sharp and Calibrated Survival Predictions
Personalized probabilistic forecasts of time to event (such as mortality) can be crucial in decision making, especially in the clinical setting. Inspired by ideas from the meteorology literature, we approach this problem through the paradigm of maximizing sharpness of prediction distributions, subject to calibration. In regression problems, it has been shown that optimizing the continuous ranked probability score (CRPS) instead of maximum likelihood leads to sharper prediction distributions while maintaining calibration. We introduce the Survival-CRPS, a generalization of the CRPS to the time to event setting, and present right-censored and interval-censored variants. To holistically evaluate the quality of predicted distributions over time to event, we present the scale agnostic Survival-AUPRC evaluation metric, an analog to area under the precision-recall curve. We apply these ideas by building a recurrent neural network for mortality prediction, using an Electronic Health Record dataset covering millions of patients. We demonstrate signiﬁcant beneﬁts in models trained by the Survival-CRPS objective instead of maximum likelihood.
 My main concern is that the authors fail to compare their appproach to any of the modelling approaches discussed in the related works section. In particular, as mentioned by the authors the WTTTE-RNN has a similar architecture and thus would have been a crucial baseline for comparisons. Furthermore, I would have liked to see an evaluation on more datasets, especially since the data in Appendix H indicate that the proposed approach is only marginally better than MLE-based model fitting. Finally, in addition to the metrics presented, conventional metrics such as the C-statistic would have been interesting. I further miss a discussion of alternative approaches to achieve well calibrated scores, especially posthoc calibration using the validation set as discussed in Guo et al, ICML 2017. Related work is incomplete, for example the use of tensor-trains in RNNs to model EHR data (Yang et al) - would the proposed approach not benefit for the use of such tensorization to better model the high-dimensional, sparse EHR data? references: Guao et al, On Calibration of Modern Neural Networks, ICML 2017 Yang et al, Modeling progression free survival in breast cancer with tensorized recurrent neural networks and accelerated failure time models, Machine Learning for Healthcare Conference 2017
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Countdown Regression: Sharp and Calibrated Survival Predictions
Personalized probabilistic forecasts of time to event (such as mortality) can be crucial in decision making, especially in the clinical setting. Inspired by ideas from the meteorology literature, we approach this problem through the paradigm of maximizing sharpness of prediction distributions, subject to calibration. In regression problems, it has been shown that optimizing the continuous ranked probability score (CRPS) instead of maximum likelihood leads to sharper prediction distributions while maintaining calibration. We introduce the Survival-CRPS, a generalization of the CRPS to the time to event setting, and present right-censored and interval-censored variants. To holistically evaluate the quality of predicted distributions over time to event, we present the scale agnostic Survival-AUPRC evaluation metric, an analog to area under the precision-recall curve. We apply these ideas by building a recurrent neural network for mortality prediction, using an Electronic Health Record dataset covering millions of patients. We demonstrate signiﬁcant beneﬁts in models trained by the Survival-CRPS objective instead of maximum likelihood.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 My main concern is that the authors fail to compare their appproach to any of the modelling approaches discussed in the related works section. In particular, as mentioned by the authors the WTTTE-RNN has a similar architecture and thus would have been a crucial baseline for comparisons. Furthermore, I would have liked to see an evaluation on more datasets, especially since the data in Appendix H indicate that the proposed approach is only marginally better than MLE-based model fitting. Finally, in addition to the metrics presented, conventional metrics such as the C-statistic would have been interesting. I further miss a discussion of alternative approaches to achieve well calibrated scores, especially posthoc calibration using the validation set as discussed in Guo et al, ICML 2017. Related work is incomplete, for example the use of tensor-trains in RNNs to model EHR data (Yang et al) - would the proposed approach not benefit for the use of such tensorization to better model the high-dimensional, sparse EHR data? references: Guao et al, On Calibration of Modern Neural Networks, ICML 2017 Yang et al, Modeling progression free survival in breast cancer with tensorized recurrent neural networks and accelerated failure time models, Machine Learning for Healthcare Conference 2017
assistant
 My main concern is that the authors fail to compare their appproach to any of the modelling approaches discussed in the related works section. In particular, as mentioned by the authors the WTTTE-RNN has a similar architecture and thus would have been a crucial baseline for comparisons. Furthermore, I would have liked to see an evaluation on more datasets, especially since the data in Appendix H indicate that the proposed approach is only marginally better than MLE-based model fitting. Finally, in addition to the metrics presented, conventional metrics such as the C-statistic would have been interesting. I further miss a discussion of alternative approaches to achieve well calibrated scores, especially posthoc calibration using the validation set as discussed in Guo et al, ICML 2017. Related work is incomplete, for example the use of tensor-trains in RNNs to model EHR data (Yang et al) - would the proposed approach not benefit for the use of such tensorization to better model the high-dimensional, sparse EHR data? references: Guao et al, On Calibration of Modern Neural Networks, ICML 2017 Yang et al, Modeling progression free survival in breast cancer with tensorized recurrent neural networks and accelerated failure time models, Machine Learning for Healthcare Conference 2017