Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: This paper addresses the important problem of learning good representations for clinical medical data. The authors propose learning representations from less important tasks with abundant labels and using these to train better models for more important tasks. The exposition needs to be improved. The reader shouldn't have to read multiple pages to find out what the source task and target task are.  

The authors compare the transfer learning neural network method to an L1-regularized logistic regression baseline. The presented approach uses neural networks and transfer learning. The empirical work is incomplete. As it stands, I don't see how the reader can tell how much of the improvement owes to transfer learning and how much owes to the use of neural networks. 

Pros:
Important problems (learning representations of clinical time series, modeling disease progression/temporal phenotyping)
Decent motivation

Cons:
Incomplete evaluation - reader cannot make a useful conclusion 
No methodological contribution

I would have liked to see this paper either provide a solid empirical result (e.g. some actionable insight, as might have been achieved with more thorough experiments). For a workshop, it's not necessary for a contribution to be enormous, but the conclusions (whatever they are) should clearly follow from the presented work. This work in progress doesn't yet meet the burden of proof.

Side Q:
How did you end up choosing 746 & 936 hidden nodes - did you grid search over every possible setting of nodes? 
Did you verify that rx2med and med2rx add anything to each other?

Typos / nits:
* "they train use an autoencoder" 
* What is a "semi-automated deterministic labeling function"?
* "to predict a source task with ubiquitous labels that" - you predict the values  of targets, you don't predict a task


review_title: Important Problem but Incomplete Evaluation

final_score: rating: 4: Ok but not good enough - rejection
review_content: This paper investigates employing transfer of learnt features on electronic health records, where labels for certain diagnosis might be sparse or scarcely available. Transfer learning is proposed as a way for leverage the data available from different tasks/label classes to infer a target class: in this case, predicting phenotypes.

Pro:
Real & important problem in practice. Exposes a new application domain.

Cons:
1) Unclear to me: "our phenotype labels are derived from diagnosis codes, which are included in our input". Are the diagnosis codes given as input to the network? If so, " we approximate ground truth phenotype labels using diagnostic code categories based on the HCUP Clinical Classification Software" -- this suggests that the phenotypes are a direct mapping from the diagnostic code, so any other information is ignored?
2) Since it's not a widely used dataset in this community, the paper could benefit from the description how the input data looks like. (Details like: dimensionality of input/output) 
3) It's not clear to me that the slight boost in performance is due to the split-brain architecture or just due to the fact that the representation (2 layers + logistic regression) is richer than the baseline (logistic regression). 
4) Given the nature of the data, a more pertinent comparison probably would have been multi-task methods.  
5) Task description and experimental setup need to be more clear. 
review_title: Review

Decision: Accept