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 written, organized, and presented paper that I enjoyed reading. I commend the authors on their attention to the narrative and the explanations. While it did not present any new methodology or architecture, it instead addressed an important application of predicting the medications a patient is using, given the record of billing codes. The dataset they use is impressive and useful and, frankly, more interesting than the typical toy datasets in machine learning. That said, the investigation of those results was not as deep as I thought it should have been in an empirical/applications paper. Despite their focus on the application, I was encouraged to see the authors use cutting edge choices (eg Keras, adadelta, etc) in their architecture. A few points of criticism: -The numerical results are in my view too brief. Fig 4 is anecdotal, Fig 5 is essentially a negative result (tSNE is only in some places interpretable), so that leaves Table 1. I recognize there is only one dataset, but this does not offer a vast amount of empirical evidence and analysis that one might expect out of a paper with no major algorithmic/theoretical advances. To be clear I don*t think this is disqualifying or deeply concerning; I simply found it a bit underwhelming. - To be constructive, re the results I would recommend removing Fig 5 and replacing that with some more meaningful analysis of performance. I found Fig 5 to be mostly uninformative, other than as a negative result, which I think can be stated in a sentence rather than in a large figure. - There is a bit of jargon used and expertise required that may not be familiar to the typical ICLR reader. I saw that another reviewer suggested perhaps ICLR is not the right venue for this work. While I certainly see the reviewer*s point that a medical or healthcare venue may be more suitable, I do want to cast my vote of keeping this paper here... our community benefits from more thoughtful and in depth applications. Instead I think this can be addressed by tightening up those points of jargon and making the results more easy to evaluate by an ICLR reader (that is, as it stands now researchers without medical experience have to take your results after Table 1 on faith, rather than getting to apply their well-trained quantitative eye). Overall, a nice paper.
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 written, organized, and presented paper that I enjoyed reading. I commend the authors on their attention to the narrative and the explanations. While it did not present any new methodology or architecture, it instead addressed an important application of predicting the medications a patient is using, given the record of billing codes. The dataset they use is impressive and useful and, frankly, more interesting than the typical toy datasets in machine learning. That said, the investigation of those results was not as deep as I thought it should have been in an empirical/applications paper. Despite their focus on the application, I was encouraged to see the authors use cutting edge choices (eg Keras, adadelta, etc) in their architecture. A few points of criticism: -The numerical results are in my view too brief. Fig 4 is anecdotal, Fig 5 is essentially a negative result (tSNE is only in some places interpretable), so that leaves Table 1. I recognize there is only one dataset, but this does not offer a vast amount of empirical evidence and analysis that one might expect out of a paper with no major algorithmic/theoretical advances. To be clear I don*t think this is disqualifying or deeply concerning; I simply found it a bit underwhelming. - To be constructive, re the results I would recommend removing Fig 5 and replacing that with some more meaningful analysis of performance. I found Fig 5 to be mostly uninformative, other than as a negative result, which I think can be stated in a sentence rather than in a large figure. - There is a bit of jargon used and expertise required that may not be familiar to the typical ICLR reader. I saw that another reviewer suggested perhaps ICLR is not the right venue for this work. While I certainly see the reviewer*s point that a medical or healthcare venue may be more suitable, I do want to cast my vote of keeping this paper here... our community benefits from more thoughtful and in depth applications. Instead I think this can be addressed by tightening up those points of jargon and making the results more easy to evaluate by an ICLR reader (that is, as it stands now researchers without medical experience have to take your results after Table 1 on faith, rather than getting to apply their well-trained quantitative eye). Overall, a nice paper.
assistant
This is a well written, organized, and presented paper that I enjoyed reading. I commend the authors on their attention to the narrative and the explanations. While it did not present any new methodology or architecture, it instead addressed an important application of predicting the medications a patient is using, given the record of billing codes. The dataset they use is impressive and useful and, frankly, more interesting than the typical toy datasets in machine learning. That said, the investigation of those results was not as deep as I thought it should have been in an empirical/applications paper. Despite their focus on the application, I was encouraged to see the authors use cutting edge choices (eg Keras, adadelta, etc) in their architecture. A few points of criticism: -The numerical results are in my view too brief. Fig 4 is anecdotal, Fig 5 is essentially a negative result (tSNE is only in some places interpretable), so that leaves Table 1. I recognize there is only one dataset, but this does not offer a vast amount of empirical evidence and analysis that one might expect out of a paper with no major algorithmic/theoretical advances. To be clear I don*t think this is disqualifying or deeply concerning; I simply found it a bit underwhelming. - To be constructive, re the results I would recommend removing Fig 5 and replacing that with some more meaningful analysis of performance. I found Fig 5 to be mostly uninformative, other than as a negative result, which I think can be stated in a sentence rather than in a large figure. - There is a bit of jargon used and expertise required that may not be familiar to the typical ICLR reader. I saw that another reviewer suggested perhaps ICLR is not the right venue for this work. While I certainly see the reviewer*s point that a medical or healthcare venue may be more suitable, I do want to cast my vote of keeping this paper here... our community benefits from more thoughtful and in depth applications. Instead I think this can be addressed by tightening up those points of jargon and making the results more easy to evaluate by an ICLR reader (that is, as it stands now researchers without medical experience have to take your results after Table 1 on faith, rather than getting to apply their well-trained quantitative eye). Overall, a nice paper.