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 presents a new loss function for survival analysis based on proper scoring functions to less then penalty wrong predictions that are confident make under the log-loss. The paper is interesting however the benefit over the traditional maximum likelihood estimator is small and the writing needs a bunch of work. I would also like to see an eval on data with far less censoring. A couple of comments 1) EHRs have only been generally adopted in the last couple of years. Only A couple of places have more. 2) Binary classifier citation on page 1 (Avati, Rajkomar) should also cite the plethora of recent machine learning for healthcare results in this field 3) Likelihoods are calibrated (as is any error measured by a proper scoring loss) 4) There are other methods to fit survival functions such as "Adversarial Time-to-Event Modeling" by Chapfuwa in ICML 2018. There are probably also moment methods 5) I think the evaluation might also want utilty because sharpness is a utility claim 6) Some of the statements in the writing are funny like probability distributions are uniquely identified by parameters. I*m not sure this is true with neural nets with symmetries. The paper doesn*t need such claims 7) Instead of log-normals, I would like to see something nonparametric like the categoricals used for maximum likelihood estimation without latents in the limiting model in "Deep Survival Analysis: Missingness and Nonparametrics" by Miscouridou at MLHC 2018
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 presents a new loss function for survival analysis based on proper scoring functions to less then penalty wrong predictions that are confident make under the log-loss. The paper is interesting however the benefit over the traditional maximum likelihood estimator is small and the writing needs a bunch of work. I would also like to see an eval on data with far less censoring. A couple of comments 1) EHRs have only been generally adopted in the last couple of years. Only A couple of places have more. 2) Binary classifier citation on page 1 (Avati, Rajkomar) should also cite the plethora of recent machine learning for healthcare results in this field 3) Likelihoods are calibrated (as is any error measured by a proper scoring loss) 4) There are other methods to fit survival functions such as "Adversarial Time-to-Event Modeling" by Chapfuwa in ICML 2018. There are probably also moment methods 5) I think the evaluation might also want utilty because sharpness is a utility claim 6) Some of the statements in the writing are funny like probability distributions are uniquely identified by parameters. I*m not sure this is true with neural nets with symmetries. The paper doesn*t need such claims 7) Instead of log-normals, I would like to see something nonparametric like the categoricals used for maximum likelihood estimation without latents in the limiting model in "Deep Survival Analysis: Missingness and Nonparametrics" by Miscouridou at MLHC 2018
assistant
 The paper presents a new loss function for survival analysis based on proper scoring functions to less then penalty wrong predictions that are confident make under the log-loss. The paper is interesting however the benefit over the traditional maximum likelihood estimator is small and the writing needs a bunch of work. I would also like to see an eval on data with far less censoring. A couple of comments 1) EHRs have only been generally adopted in the last couple of years. Only A couple of places have more. 2) Binary classifier citation on page 1 (Avati, Rajkomar) should also cite the plethora of recent machine learning for healthcare results in this field 3) Likelihoods are calibrated (as is any error measured by a proper scoring loss) 4) There are other methods to fit survival functions such as "Adversarial Time-to-Event Modeling" by Chapfuwa in ICML 2018. There are probably also moment methods 5) I think the evaluation might also want utilty because sharpness is a utility claim 6) Some of the statements in the writing are funny like probability distributions are uniquely identified by parameters. I*m not sure this is true with neural nets with symmetries. The paper doesn*t need such claims 7) Instead of log-normals, I would like to see something nonparametric like the categoricals used for maximum likelihood estimation without latents in the limiting model in "Deep Survival Analysis: Missingness and Nonparametrics" by Miscouridou at MLHC 2018