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.
 The paper proposes the use of Survival Continuous Ranked Probability score instead of maximum likelihood estimation for personalised probabilistic forecasts of time-to-event data, thus estimating a distribution over future time. The authors describe the evaluation their method using (1) proper scoring rule objectives; (2) evaluation of calibration using sharpness as a metric; (3) the survival precision recall curve. The authors then apply these techniques to predicting time-to-mortality using an RNN that takes EHR patient records to predict the probability of death at a given time point. It’s not clear how this is related to the Survival CRPS model or how this model is incorporated into the RNN. Overall, this is an important framework for estimating personalised predictions of survival events for patients with interval-censored data. The authors present a well thought-out paper with clearly and realistically articulated modelling assumptions. The authors also give an excellent critique of the underlying assumptions of current state-of-the-art survival methods. The authors are also to be commended for the mathematical elegance Although the paper is very well written and extremely well structured, I struggled with the lack of experiments available in the paper. The text embedded in Figure 3 is too small. The results section is somewhat sparse. Although the mathematical formulation is well-motivated and structured, it’s not clear what the contribution of this work is. The difference between CRPS-INTVL and MLE-INTVL is incremental and it’s unclear what the significant benefits are of CRPS vs MLE. What would the interpretation of these differences in a real-world setting?
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>



 The paper proposes the use of Survival Continuous Ranked Probability score instead of maximum likelihood estimation for personalised probabilistic forecasts of time-to-event data, thus estimating a distribution over future time. The authors describe the evaluation their method using (1) proper scoring rule objectives; (2) evaluation of calibration using sharpness as a metric; (3) the survival precision recall curve. The authors then apply these techniques to predicting time-to-mortality using an RNN that takes EHR patient records to predict the probability of death at a given time point. It’s not clear how this is related to the Survival CRPS model or how this model is incorporated into the RNN. Overall, this is an important framework for estimating personalised predictions of survival events for patients with interval-censored data. The authors present a well thought-out paper with clearly and realistically articulated modelling assumptions. The authors also give an excellent critique of the underlying assumptions of current state-of-the-art survival methods. The authors are also to be commended for the mathematical elegance Although the paper is very well written and extremely well structured, I struggled with the lack of experiments available in the paper. The text embedded in Figure 3 is too small. The results section is somewhat sparse. Although the mathematical formulation is well-motivated and structured, it’s not clear what the contribution of this work is. The difference between CRPS-INTVL and MLE-INTVL is incremental and it’s unclear what the significant benefits are of CRPS vs MLE. What would the interpretation of these differences in a real-world setting?
assistant
 The paper proposes the use of Survival Continuous Ranked Probability score instead of maximum likelihood estimation for personalised probabilistic forecasts of time-to-event data, thus estimating a distribution over future time. The authors describe the evaluation their method using (1) proper scoring rule objectives; (2) evaluation of calibration using sharpness as a metric; (3) the survival precision recall curve. The authors then apply these techniques to predicting time-to-mortality using an RNN that takes EHR patient records to predict the probability of death at a given time point. It’s not clear how this is related to the Survival CRPS model or how this model is incorporated into the RNN. Overall, this is an important framework for estimating personalised predictions of survival events for patients with interval-censored data. The authors present a well thought-out paper with clearly and realistically articulated modelling assumptions. The authors also give an excellent critique of the underlying assumptions of current state-of-the-art survival methods. The authors are also to be commended for the mathematical elegance Although the paper is very well written and extremely well structured, I struggled with the lack of experiments available in the paper. The text embedded in Figure 3 is too small. The results section is somewhat sparse. Although the mathematical formulation is well-motivated and structured, it’s not clear what the contribution of this work is. The difference between CRPS-INTVL and MLE-INTVL is incremental and it’s unclear what the significant benefits are of CRPS vs MLE. What would the interpretation of these differences in a real-world setting?