Predicting Medications from Diagnostic Codes with Recurrent Neural Networks
It is a surprising fact that electronic medical records are failing at one of their primary purposes, that of tracking the set of medications that the patient is actively taking. Studies estimate that up to 50% of such lists omit active drugs, and that up to 25% of all active medications do not appear on the appropriate patient list. Manual efforts to maintain these lists involve a great deal of tedious human labor, which could be reduced by computational tools to suggest likely missing or incorrect medications on a patient’s list. We report here an application of recurrent neural networks to predict the likely therapeutic classes of medications that a patient is taking, given a sequence of the last 100 billing codes in their record. Our best model was a GRU that achieved high prediction accuracy (micro-averaged AUC 0.93, Label Ranking Loss 0.076), limited by hardware constraints on model size. Additionally, examining individual cases revealed that many of the predictions marked incorrect were likely to be examples of either omitted medications or omitted billing codes, supporting our assertion of a substantial number of errors and omissions in the data, and the likelihood of models such as these to help correct them.
This is a well-conducted and well-written study on the prediction of medication from diagnostic codes. The authors compared GRUs, LSTMs, feed-forward networks and random forests (making a case for why random forests should be used, instead of SVMs) and analysed the predictions and embeddings. The authors also did address the questions of the reviewers. My only negative point is that this work might be more relevant for a data science or medical venue rather than at ICLR.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Predicting Medications from Diagnostic Codes with Recurrent Neural Networks
It is a surprising fact that electronic medical records are failing at one of their primary purposes, that of tracking the set of medications that the patient is actively taking. Studies estimate that up to 50% of such lists omit active drugs, and that up to 25% of all active medications do not appear on the appropriate patient list. Manual efforts to maintain these lists involve a great deal of tedious human labor, which could be reduced by computational tools to suggest likely missing or incorrect medications on a patient’s list. We report here an application of recurrent neural networks to predict the likely therapeutic classes of medications that a patient is taking, given a sequence of the last 100 billing codes in their record. Our best model was a GRU that achieved high prediction accuracy (micro-averaged AUC 0.93, Label Ranking Loss 0.076), limited by hardware constraints on model size. Additionally, examining individual cases revealed that many of the predictions marked incorrect were likely to be examples of either omitted medications or omitted billing codes, supporting our assertion of a substantial number of errors and omissions in the data, and the likelihood of models such as these to help correct them.
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>



This is a well-conducted and well-written study on the prediction of medication from diagnostic codes. The authors compared GRUs, LSTMs, feed-forward networks and random forests (making a case for why random forests should be used, instead of SVMs) and analysed the predictions and embeddings. The authors also did address the questions of the reviewers. My only negative point is that this work might be more relevant for a data science or medical venue rather than at ICLR.
assistant
This is a well-conducted and well-written study on the prediction of medication from diagnostic codes. The authors compared GRUs, LSTMs, feed-forward networks and random forests (making a case for why random forests should be used, instead of SVMs) and analysed the predictions and embeddings. The authors also did address the questions of the reviewers. My only negative point is that this work might be more relevant for a data science or medical venue rather than at ICLR.