Neural Distribution Learning for generalized time-to-event prediction
Predicting the time to the next event is an important task in various domains. However, due to censoring and irregularly sampled sequences, time-to-event prediction has resulted in limited success only for particular tasks, architectures and data. Using recent advances in probabilistic programming and density networks, we make the case for a generalized parametric survival approach, sequentially predicting a distribution over the time to the next event. Unlike previous work, the proposed method can use asynchronously sampled features for censored, discrete, and multivariate data. Furthermore, it achieves good performance and near perfect calibration for probabilistic predictions without using rigid network-architectures, multitask approaches, complex learning schemes or non-trivial adaptations of cox-models. We firmly establish that this can be achieved in the standard neural network framework by simply switching out the output layer and loss function.
 The paper "Neural Distribution Learning for generalized time-to-event prediction" proposes HazardNet, a neural network framework for time-to-event prediction with right-censored data. First of all, this paper should be more clear from the begining of the kind of problems it aim to tackle. The tasks the proposal is able to consider is not easy to realize, at least before the experiments part. The problem should be clearly formalized in the begining of the paper (for instance in the introduction of section 3). It the current form, it is very hard to know what are the inputs, are they sequences of various kinds of events or only one type of event per sequence. It is either not clear to me wether the censoring time is constant or not and wether it is given as input (censoring time looks to be known from section 3.4 but in that case I do not really understand the contribution : does it not correspond to a very classical problem where events from outside of the observation window should be considered during training ? classical EM approaches can be developped for this). The problem of unevenly spaced sequences should also be more formally defined. Also, while the HazardNet framework looks convenient, by using hazard and survival functions as discusses by the authors, it is not clear to me what are the benefits from recent works in neural temporal point processes which also define a general framework for temporal predictions of events. Approaches such at least like "Modeling the intensity function of point process via recurrent neural networks" should be considered in the experiments, though they do not explicitely model censoring but with slight adapations should be able to work well of experimental data.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Neural Distribution Learning for generalized time-to-event prediction
Predicting the time to the next event is an important task in various domains. However, due to censoring and irregularly sampled sequences, time-to-event prediction has resulted in limited success only for particular tasks, architectures and data. Using recent advances in probabilistic programming and density networks, we make the case for a generalized parametric survival approach, sequentially predicting a distribution over the time to the next event. Unlike previous work, the proposed method can use asynchronously sampled features for censored, discrete, and multivariate data. Furthermore, it achieves good performance and near perfect calibration for probabilistic predictions without using rigid network-architectures, multitask approaches, complex learning schemes or non-trivial adaptations of cox-models. We firmly establish that this can be achieved in the standard neural network framework by simply switching out the output layer and loss function.
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 "Neural Distribution Learning for generalized time-to-event prediction" proposes HazardNet, a neural network framework for time-to-event prediction with right-censored data. First of all, this paper should be more clear from the begining of the kind of problems it aim to tackle. The tasks the proposal is able to consider is not easy to realize, at least before the experiments part. The problem should be clearly formalized in the begining of the paper (for instance in the introduction of section 3). It the current form, it is very hard to know what are the inputs, are they sequences of various kinds of events or only one type of event per sequence. It is either not clear to me wether the censoring time is constant or not and wether it is given as input (censoring time looks to be known from section 3.4 but in that case I do not really understand the contribution : does it not correspond to a very classical problem where events from outside of the observation window should be considered during training ? classical EM approaches can be developped for this). The problem of unevenly spaced sequences should also be more formally defined. Also, while the HazardNet framework looks convenient, by using hazard and survival functions as discusses by the authors, it is not clear to me what are the benefits from recent works in neural temporal point processes which also define a general framework for temporal predictions of events. Approaches such at least like "Modeling the intensity function of point process via recurrent neural networks" should be considered in the experiments, though they do not explicitely model censoring but with slight adapations should be able to work well of experimental data.
assistant
 The paper "Neural Distribution Learning for generalized time-to-event prediction" proposes HazardNet, a neural network framework for time-to-event prediction with right-censored data. First of all, this paper should be more clear from the begining of the kind of problems it aim to tackle. The tasks the proposal is able to consider is not easy to realize, at least before the experiments part. The problem should be clearly formalized in the begining of the paper (for instance in the introduction of section 3). It the current form, it is very hard to know what are the inputs, are they sequences of various kinds of events or only one type of event per sequence. It is either not clear to me wether the censoring time is constant or not and wether it is given as input (censoring time looks to be known from section 3.4 but in that case I do not really understand the contribution : does it not correspond to a very classical problem where events from outside of the observation window should be considered during training ? classical EM approaches can be developped for this). The problem of unevenly spaced sequences should also be more formally defined. Also, while the HazardNet framework looks convenient, by using hazard and survival functions as discusses by the authors, it is not clear to me what are the benefits from recent works in neural temporal point processes which also define a general framework for temporal predictions of events. Approaches such at least like "Modeling the intensity function of point process via recurrent neural networks" should be considered in the experiments, though they do not explicitely model censoring but with slight adapations should be able to work well of experimental data.