Knowledge-Driven New Drug Recommendation
Authors: Zhenbang Wu
Huaxiu Yao
Zhe Su
David M Liebovitz
Lucas M Glass
James Zou
Chelsea Finn
Jimeng Sun
Venue: Submitted to ICLR 2023 (2023)
Keywords: drug recommendation
medication recommendation
healthcare
electronic health record
few-shot learning

Abstract: Drug recommendation assists doctors in prescribing personalized medications to patients based on their health conditions. Existing drug recommendation solutions adopt the supervised multi-label classification setup and only work with existing drugs with sufficient prescription data from many patients. However, newly approved drugs do not have much historical prescription data and cannot leverage existing drug recommendation methods. To address this, we formulate the new drug recommendation as a few-shot learning problem. Yet, directly applying existing few-shot learning algorithms faces two challenges: (1) complex relations among diseases and drugs and (2) numerous false-negative patients who were eligible but did not yet use the new drugs. To tackle these challenges, we propose EDGE, which can quickly adapt to the recommendation for a new drug with limited prescription data from a few support patients. EDGE maintains a drug-dependent multi-phenotype few-shot learner to bridge the gap between existing and new drugs. Specifically, EDGE leverages the drug ontology to link new drugs to existing drugs with similar treatment effects and learns ontology-based drug representations. Such drug representations are used to customize the metric space of the phenotype-driven patient representations, which are composed of a set of phenotypes capturing complex patient health status. Lastly, EDGE eliminates the false-negative supervision signal using an external drug-disease knowledge base. We evaluate EDGE on two real-world datasets: the public EHR data (MIMIC-IV) and private industrial claims data. Results show that EDGE achieves 7.3% improvement on the ROC-AUC score over the best baseline.

Under review as a conference paper at ICLR 2023
EDGE: KNOWLEDGE-DRIVER NEW DRUG RECOM- MENDATION
Anonymous authors Paper under double-blind review
ABSTRACT
Drug recommendation assists doctors in prescribing personalized medications patients based on their health conditions. Existing drug recommendation solutions adopt the supervised multi-labe classification setup and only work with existing drugs with sufficient prescription data from many patients. However, newly ap- proved drugs do not have much historical prescription data and cannot leverage existing drug recommendation methods. To address this, we formulate the new drug recommendation as a few-shot learning problem. Yet, directly applying ex- isting few-shot learning algorithms faces two challenges: (I) complex relations among diseases and drugs and (2) numerous false-negative patients who were eligible but did not yet the drugs. To tackle these challenges, pose EDCE, which can quickly adapt to the recommendation for new drug with limited prescription data from few support patients. EDGE maintains a drug- dependent multi-phenotype few-shot learner to bridge the gap between existing and new drugs. Specifically, EDGE leverages the drug ontology link new drugs to existing drugs with similar treatment effects and learns ontology- representations. Such drug representations are used to customize the metric space of the phenotype-driver patient representations, which are composed of set of phenotypes capturing complex patient health status. Lastly, EDGE eliminates the false-negative supervision signal using external drug-disease knowledge base. We evaluate EDGE on two real-world datasets: the public EHR data (MIMIC- IV) and private industrial claims data. Results show that EDGE achieves 7.3% improvement on the ROC-AUC score over the best baseline.
pro-
drug
INTRODUCTION
With the wide adoption of electronic health records (EHR) and the advance of deep learning models, we have seen great opportunities in assisting clinical decisions with deep learning models to improve resource utilization, healthcare quality, and patient safety (Xiao et al. 2018). Drug recommendation is one of the essential applications which aims at assisting doctors recommending personalized medications to patients based on their health conditions. Existing drug recommendation methods typically formulate it supervised multi-lahed classification problem (Zhang al., 2017 Zitnik et al. 2018 [hang et al., 20196] Yang et al. 2021: Rui Wu & Wu. 2022: Tan et al. 2022b They often train on massive prescription data learn patient representations and use the learned repre- sentations to predict medications (i.e., labels). However, in reality, new drugs come to the market all the time. For example, U.S. Food and Drug Administration (FDA) approves wide range of new drugs every year (FDA 2022). Most of these newly approved drugs do not have much historical data to support model training (Blass) 202T). Even if sufficient prescription data for new drugs ex- ists, existing models must be periodically re-trained or updated to recommend new drugs, which is expensive and complex. As a result, existing drug recommendation methods can only recommend the same set of drugs seen during training and are no longer applicable when new drugs appear.
To address this, we formulate the recommendation new drugs as a few-shot classification problem. Given a new drug with limited prescription data from a few support patients (e.g., from clinical trials (uijnhoven et al. 2013)), the model should quickly adapt to the recommendation for this drug. Meta-learning approaches have been widely used in such problems by learning how to quickly adapt the classifier to a new label unseen during training. given only a few support examples (Finn et al.] 2017 Snell et al. 2017). However, most prior meta-learning works focus vision language-

---

Under review as a conference paper at ICLR 2023
related tasks. In the new drug recommendation, applying existing meta- meta-leariing algorithms faces the following challenges. (1) Complex relations among diseases and drugs: diseases and medicines can have inherent and higher order relations. Deciding whether to prescribe drug to specific patient depends on many factors, such as disease progression, comorbidities, ongoing treatments, individual drug response, and drug side effects. General meta-learning algorithms do not explicitly capture such dependencies. (2) Numerous false-negative patients: many drugs can treat the same disease, but usually, only one of them is prescribed. For any given drug, there exist many false- negative patients who were eligible but did not yet use the new drug (e.g., due to drug availability, doctor's preference, or insurance coverage). The number of false- nelgatvve supervision signals will substantially confuse the model learning, especially in the few-shot learning setting.
To address these challenges, we introduce EDGE, drug-dependent multi-phenotype few-shot learner to quickly adapt to the recommendation for new drug with limited support patients. Specif- ically, since drugs within the same category often have similar treatment effects, EDGE utilizes the drug ontology for drug representation learning to link new drugs with existing drugs. Further, EDGE learns multi-phenotype patient representations to capture the complex patient health status from dif- ferent aspects such chronic diseases, current symptoms, and ongoing treatments. Given new drug with a few support patients, EDGE makes recommendations by performing a drug- phenotype-level comparison between representations of query patients and corresponding support prototypes. Lastly, to reduce the false-negative supervision signal, EDGE leverages the MEDI (Wei al. 2013) drug-disease knowledge base to guide the negative sampling process.
The main contributions of this work include:
To our best knowledge, this the first work formulating the task of new drug recommendation;
We propose a meta-learing framework EDGE to solve this problem by considering complicated relations among diseases and drugs, and eliminating numerous false-negative patients.
We conduct extensive experiments on the public EHR data MIMIC-IV (Johnson et al. 2020 and private industrial claims data. Results show that our approach achieves 5.6% over ROC-AUC, 6.3% over Precision @1 100, and 5.5% over @ecall@ 100 when providing recommended patient lists for new drugs. We also include detailed analyses and ablation studies to show the effectiveness of multi-phenotype patient representation, drug-dependent patient distance, and knowledge-guided negative sampling.
2 PROBLEM FORMULATION AND PRELIMINARIES
Denote the set of all drugs as M: the goal of drug recommendation is to prescribe drugs in M that are suitable for a patient with a record v [ci...., cv), which consists of list of diseases (and procedures] [ and V is the total number of diseases and procedures in the record Prior works (Zhang et al., 2017 Shang et al,, 2019b) Yang et al., 202T TTan et al., 2022b) formulate drug recommendation
multi-label classification problem by generating a multi-hot output of size [M]. However, this formulation assumes that the drug label space M remains unchanged after training and is not applicable when new drugs appear. Thus, we propose alternative formulation for the new drug recommendation as follows.
Assume the entire drug set M is partitioned into set of existing drugs Mald and set of new drugs M where Mold n Mnnww = Each existing drug m; E Mold has sufficient patients using the drug m. (e.g., from EHR data). Each new drug me E Anew is associated with small support set S. = { consisting of patients using the drug m, (e.g., from clinical trials), and an unlabeled query patient set Q. { where N, and Ng are the number of patients in the support and query sets, respectively. The goal of new drug recommendation is to train model f-)-) parameterized by & on existing drugs Mood, such that it can adapt to new drug m, E Mnnw given the small support set S,, and make correct recommendation on the query set Q.
Our work is inspired by the prototypical network (Snell et al. 2017), which learns representation model (-) such that patients using a specific drug will cluster around a prototype representation. Recommendation can then be performed by computing the distance to the prototype. To equip the
'To reduce clutter, use unified notation for both discases and procedures.. Since we focus record- level prediction, "patient" and "record" used interchangeahly.
2

---

Under review as a conference paper at ICLR 2023
Medication
Disease
Procedure
Patient Record
Phenotype
Pos/Neg Support
g
Query U
m
1
m
m
Be
C2 C3
Ontology-Enhancnn Enhanced
Multii-Phenatpe
Drug Representation
Patient Representation
Drug-Dependent Patient Distance
Figure 1: EDGE. learns the ontology-enhanced drug representation h and multi multi-phenotype patient 11=1- For new drug EDGE decides whether to prescribe it to query patient " by performing drug-dependent phenotype-level comparison between multi mhlti-phenotype query representations {g] 13 and corresponding support prototypes J2
representations {g())
model with the ability adapt new drug with limited support patients, prototypical network trains the model via episodic training, where each episode is designed to mimic the low-data testing regime. Concretely, an episode is formed by first sampling an existing drug m, from Mald and then sampling set of patients using the drug my3 The sampled patients are divided into two disjoint sets: (1) support set S; used to calculate the prototype, and (2) a query set Q used to calculate the loss. From the support set Sü, prototypical network calculates the prototype representation as,
Σ (05), PER., JES:
p
where p is an e-dimensional vector in the metric space, and |- denotes cardinality. Next, given query patient uy. the probability of recommending drug is measured by the distance d(-)) between its representation and the corresponding prototypes as,
exp (v4) .p)) (M = +|w)) = exp (-d (v,). exp (-d (v)),p'))
where p' the negative prototype obtained from another negative support set S² of patients not using the drug m, (i.e., negative sampling). The loss is computed the negative log-likelihood (NLL) loss L(Ø) = log (3, ( of the true label E {+,-}. }. And the model fa(.) () is optimized on both the query set Q, and another negative query set Ø obtained via negative sampling (similarly
as ST).
3 KNOWLEDGE-DRIVER NEW DRUG RECOMMENDATION
In this section, we introduce EDGE, which can adapt to new drugs with limited support patients via a drug-dependent multi-phenotype few-shot learner. Specifically, EDGE consists the following modules: (1) Ontology-enhanue
drug encoder that fuses ontology information into drug repre- sentation link new drugs existing drugs with similar treatment effects; (2) Multi-phenotype patient encoder that represents each patient with set of phenotype-level representations cap- ture the complex patient's 's health status; (3) Drug-dependent distance measures that learns drug- dependent phenotype importance scores to customize the patient similarity; (4) knowledge-guidd negative sampling that eliminates the false- -eggtieee supervision signal. Figure [[rovvdessv an illus- tration of EDGE. In the following, we will describe how EDGE decides whether prescribe drug my to a query patient Vg given small set of support patients S, using the drug m,,
3.1
ONTOLOGY-
DRUG REPRESENTATION LEARNING
Though many new drugs have not been used regularly in clinical practice, they still belong to the same drug category (from drug ontology) some existing drugs and share similar treatment ef- fects, implicitly indicating similar patient populations. For example, the newly approved Quviviq for treating insomnia belongs to the category (Orexin Receptor Anlagonist) some existing
3

---

Under review as a conference paper at ICLR 2023
drugs, like Belsomra and Dayvigo, which also sleeping aids. We here leverage the drug ontol- ogy to enrich the drug representation by attentively combing the drug itself and its corresponding ancestors (e.g.. higher-level drug categories).
Concretely, for the drug m, we obtain its basic embeddings m, E I by feeding its description into Clinical-BERG (Alsentzer et al. 2019). Then, follow Choi al. (2017), we use the basic embeddings of drug m, and its ancestors to calculate the
drug representation as,
a E R,,
JEA,
where A, denotes the set of drug my and its ancestors, and the attention score 04 represents the importance of ancestor m, for drug mi, which is calculated as,
exp(Je. (m; m;))
O41 ExEA, exp(fe. (m, a m²)) and E 10,
(4)
where denotes the concatenation operator, and (.) R + R is defined as two-layer fully connected neural network with Tanh activation. In this way, we fuse the ontology information into the representation h for drug my, which is later used to customize the metric space of phenotype- driven patient representations, introduced next.
3.2 MUL.TI-PHENOTPPE PATIENT REPRESENTATION LEARNING
Patient health status includes many factors, such discase progression, comorbidities, ongoing treatments, individual drug response, and drug side effects. Encoding each patient into a single vector may not capture the complete information, especially for patients with complex health ditions. Therefore, we define a set of phenotypes and represent each patient with a set of phenotype vectors. Each phenotype can provide helpful guidance in patient representation learning and further benefit the new drug recommendation.
Specifically, for every support/query patient with list of diseases [ci; (ei...., cvl, EDGE first com- putes the contextualized disease representations by applying the embedding function for (-) as, [ri...., rv] = fer (er......cvl). ry ER:
where ry is the contextualized representation for disease ey. We model (.) using bi-directional gated recurrent unit (GRU) due to its popularity in prior works (Zhang et al. 2017 [Shang et al.) 2019b. Yang et al.] 2021). and also show results with multilayer perceptron (MLP) and Trans- former Vaswani et al. 2017) in our experiments.
Next, we leverage domain knowledge to group diseases into different phenotypes. To obtain the representation for the I-th phenotype, take the representations from all diseases that belong to that phenotype, project them to a lower dimension, and calculate their mean representations as,
Σ (r;), gd) ER,
|
eggin
where g()) represents the set of diseases whose phenotype is and (.): IR - IR9 is single-layer neural network and g < e. We show results with differrnnt values of g in the experiment. If (()) is empty, we take the pooled sequence representation as substitute. The phenotypes are extracted from Clinical Classification Software (CCS) (H. CUP 2010). There are 511 phenotypes in total. In this way, each support/query patient is represented with set of phenotype vectors (g" )/
Based on the multi multi- phenotype patient representations, we further calculate the phenotype-level pro- totypes from the support set S; of drug m,, where equation i revised as,
(2) E
p
JES,
g is the 1-th phenotype representation for patient Uy from the support set S, of drug m,- In this way, we encode the support set S; into a set of phenotype-level prototypes {p )/ which is
where (1)
further used to calculate the frug-deepndeet patient distance with the multi multi-phenotype representa- tions (5) ( of the query patient Vg. described next.
FFgg, Ibuprofen is nonsteroidal onti-inflammatory drug that is used for treating pain, fever, and inflam- mation.
4

---

Under review as a conference paper at ICLR 2023
3.3 DRUG-DEPENDENT PATIENT DISTANCE
We finally focus on defining a reasonable met- ric to measure the distance between query pa- tient and support prototypes. Intuitively, dif- ferent drugs may have different focuses when comparing patients. For example, chronic drugs may emphasize chronic diseases and pa- tients' past health history, while drugs accom-
Algorithm 1 Training for EDGE
Require:
Moldd existing training drugs; A: drug ontology: (g J phenotypes
1: while not done do
2: Sample drug m, from Mold
3: Sample support and query set S. Q
panying specific treatments may focus on ongo- ing treatments. Therefore, we leverage the drug representation to learn a deruuddeenddent patient distance.
4: Get drug representation with Eq.
5: Calculate phenotype-driven prototypes
)L, for support S, with Eq. 5 7
6:
for all v, Q do
7:
Compute multi-phenotype patient repre-
Specifically, given the phenotype-driven proto- types { from support patients S, and the multi multi-phenottpe representations (m:)) for the query patient Vg. we first calculate the per-phenotype distance between the query and support. Formally, for a specific phenotype I, the distance is defined as,
sentations (g ) for v. with Eq. 5
/i=1
8:
Compute drug-dependent phenotype im-
portance 33r with Eq.
9:
Get per-phenotype distances between support and query with Eq. 8
10:
Get recommendation with Eq. 10
11:
end for
2 = d(g.')
E
(8)
Use gradient descent update parameters
é based on the NLL
where d(-) is the euclidean distance. We also show results with cosine distance in the exper- iment. In practice, we only calculate the distances between the union of phenotypes that appear in both query and support patients and mask all other distances zero. Combining all L. per-phenotype distances, we obtain a L-dimensional distance vector, denoted as z.
end while
Next, with the ontology-enhanee drug representation h for drug my we calculate the drug- dependent importance weights over different phenotypes as,
ofas (h)), B E [0,
99)
where (-)) denotes the sigmoid function, and for (-) R + R is a single-layer fully connected neural network. According to the importance weight, the probability of recommending drug m, to the query patient by (equation 2 is reformulated as,
exp( 3 2)
(y = + +l,))= = exp(- x)
(10)
where B z aggregates the per-phenotype distances with respect to drug m. using the learned importance weights B, and z' is the distance vector between the query Vy and the negative (i.e., not using drug mi) support patients obtained via negative sampling, introduced next.
3.4 ENOWLEDGE-GUDDD NEGATIVE SAMPLING
In addition to the model design, negative sampling plays a vital role in model training. Uniform negative sampling the default method, where all patients not using a par- ticular drug are treated as negatives. However, in real- world scenarios, many drugs can treat the same disease, but usually, only one of them is prescribed. For any given drug, there exist many felse-negative patients that could but eventually did not use the drug for several reasons, such as availability, doctor preference, or insurance coverage. For example, in figure 2 for drug Ibuprofen, uniform negative sampling treats both the patients 02 and not prescribed with Ibuprofen. However, patient Vy is false negative as Ibuprofen can also be used. We quantitatively evaluate the prevalence of fegse-ne patients in experiment, which ranges from 20% to 30%. The number of false-negative supervision signals will confuse the model learn- ing, especially in the few-shot learning setting.
True Posttive
Dx: Headache
Rs: lbuprofen
False Negative
Dxc Headache
Ibuproten
Rx: Aspirin
Pain Reliver
True Nogative
Dx: Respiratory Allergies
daproes
Ro: Zyrtec
Figure 2: A false negative example.
negatives since they are
5

---

Under review as a conference paper at ICLR 2023
To address this issue, EDGE introduces the following knowledge guiied negative sampling strategy- We leverage the MEDI (Wei et al., 2013) drug-disease relationship (i.e., pairs of drugs and their target diseases ). When performing negative sampling for a given drug, only sample from the negative patients whose diagnoses do not overlap with the listed target diseases of that drug. Note that we only use this strategy during training so that the information will not leak to testing. We empirically show that this simple strategy can improves the performance.
4 EXPERIMENTS
4.1
EXPERIMENTAL SETTINGS
Dataset Description. We evaluate EDGE on two real-world EHR datasets: MIMIC-IV and Claims, which are described as follows (see detailed data statistics and descriptions in appendix A.I]):
MIMIC-IV (Johnson et al. 2020) covers more than 382K patients admitted the ICU or the emergency department at the Beth Israel Deaconess Medical Center between 2008 to 2019. We split the drugs by the time they were first prescribed. We use drugs first appeared in 2008 as the training (existing) drugs, drugs appeared in 2009 as the validation drugs, and drugs appeared after 2010 as the testing (new) drugs. The admissions are split accordingly: if an admission contains any test/validation//raiingg drug. it will be in lest/validation/rraiinng set. If admission contains drugs from more than one split, priority is given to test, validation, and training. There are 49 new drugs in total.
Claims are captured from payers and healthcare providers across the United States. We randomly sample 30K patients and select all their claims from 2015 to 2019, resulting in 691K claims. We split the drugs into 10%/10%/20555 training/validationn// way as MIMIC-IV. There are 99 new drugs in total.
sets. The admissions are split the same
Baselines. We compare EDGE three categories of baselines. (1) The first category is drug rec- ommendation methods, including RNN (Choi et al.) 2016b). GAMENet (Shang et al. 2019b), SafeDrug (Yang et al. (021). For a fair comparison, the drug recommendation is first trained on the training set of existing drugs, and then fine-tuned on the support set of new drugs. (2) The second category few-shot learning approaches, including MAML (Finn et al,, 2017). ProtoNet (Snell et al. (017). FEAT YEe et 20021, CTML (Peng & Pan (022). (3) Lastly, we include the cold- start recommendation methods, including MeLU (Lee et al.) 2019) and TaNP (Lin et al.) 2021)
See appendix A.2 for details of all baselines.
Evaluation Metrics. We randomly generate 1000 episodes for the test drugs. Each episode contains a randomly sampled test drug, a support set consisting of 5 positive and 25 negative records for this drug, and a query set consisting of all the rest of testing records (over 15K for MIMIC-IV data and 29K for claims data). Each episode is binary classification task: whether to prescribe the drug to query record or not. We calculate the Area Under Receiver Operator Characteristic Curve (ROC- AUC), Area Under Precision-Reca Curve (PR-AUC). Precision @ K, Recall @ K. For each metric, we also report the 95% confidence interval calculated from the 1000 episodes. We also perform the independent two-sample t-test evaluate if EDGE achieves significant improvement over baseline methods. Due to space limitations, we only show ROC-AUC, Precision @ 100, and Recall 100 in the main paper and leave other metrics in the appendix B.1
Implementation Details. We use ICD codes (WHO 1993) to represent discase and procedure, and ATC-5 level codes (WHO 1976) to represent medication. CCS category (H. CUP 2010) is used to define the phenotype for diseases and procedures. ATC ontology WHO (1976) is used to build the drug ontology. We obtain the basic code embeddings by encoding code description with Clinical- BERT (Alsentzer et al. (019).. For all methods, we use the bi-directional GRU as the backbone encoder, with 768 as input dimension and 512 as the output dimension. We set the phenotype vector dimension g 64. All models are trained for 100K episodes with 5 positive and 250 negative supports. We select the best model by monitoring the ROC-AUC score on the validation set.
4.2 MAIN RESULTS
Table I evaluates EDGE on the performance of recommendations new drugs. First, observe that existing drug recommendation methods perform poorly for this task due to limited training ex- amples. Among them, SafeDrug achieves slightly better performance, probably due to the usage of

---

Under review as a conference paper at ICLR 2023
Table I: Results on new drug recommendation. + denotes the 95% confidence interval. Bold indi-
cates the best result and underline indicates second best. indicates that EDGE achieves significant improvement over the best baseline method (i.e., the p-value is smaller than 0.05). Experiment re- sults show that EDGE can adapt to new drugs and make correct recommendations.
Method
MIMIC-IV
Claims
ROC-AUC
Precision 100
Recall@ 100
ROC-AUC
Precision@ 100
&ecall@e 100
RNN
0.4612 = 0.0091
0.0279 + 0.0062
0,0040 + 0.0005
0,5012 +: 0.0062
0.0130 + 0.0014
0.0047 = 0.0006
GAMENet
0.6831 0.0087
0.1047 + 0.0121
0.0722 + 0.0070
0.6361 0.0077
0.0338 0.0058
0.0203
0.0044
SafeDrug
0,7488 = 0.0102
0.1055 = 0.0103
0.0655 + 0.0064
0.4792 +: 0.0048
0.0129 1 0.0016
0.0022 = 0.0003
MAML
0.7197
0.0105
0.0549
0.0356 0.0045
0.6019
0.0074
0.0223
0.0044
0.0086
0.0015
ProtoNet
0.7903 + 0.0084
0.1426 1: 0.0116
0,1187 + 0.0090
0.6740 1: 0.0070
0 1 0.0087
0.0436 =: 0.0053
0,8020
0.0081
0.1427 0.0120
0.1080
0.0077
0.6709
0.0079
0,0718
0.010T
0.0349
CTML
0.5960
- 0.0113
0.0985
1 0.0098
0,0829 +: 0.0072
0,5550 1: 0.0072
0,0102 1 0.0014
0.0049
1 0,0008
McLU
0.7070
0.0080
0.0600
0.0355
0.0052
0.5932
0.0068
0,0264
0.0050
0.0060
0.0009
TANP
0.6093
0,0093
0.0243 + 0.0061
0.0092
+ 0.0019
0,5608
0.0076
0,0134
0.0021
0.0067
0.0012
EUGE
0.8608 0.0069*
0,2251 0.0139
0.1907 0.0126
0.7275 1. 0.0066+
0.1254 0.0102
0.0803 0.0075
drug molecule information. General few-shot learning achieves inconsistent performance. Metric- based methods ProtoNet and FEAT performance better compared to optimization- -basd approaches MAML and CTML. This may be due to the challenge in imbalanced classification setting, where the number of negatives is much larger than that of positives, and the aptimization-basee approach might get trapped in the local minimum. Among general recommendation methods, MeLU and TaNP do not perform very well for this task despite their strong performance reported in Tan et al. (022a).. We suspect that they adapt the model based on patient history for rare disease diagnoses. While the setting is different in our case, we need to adapt the model based on the drug (i.e., item) prescription history. Lastly, EDGE achieves the best performance in all metrics on both datasets. Specifically, compared to the best baseline, on MIMIC-IV, EDGE achieves 5.9% absolute improve- ments on ROC-AUC, 8.2% on Precision@ @ 100, and 7.2% on Recall @ 100; for claims data, EDGE achieves 5.3% on ROC-AUC, 4.4% on Precision @ 100, and 3.7% on Recall @ 100.
4.3 QUANTITATIVE ANALYSIS OF PERFORMANCE GAINS
This section presents quantitative analyses to understand the performance gains of EDGE, including the analysis of false negatives, the ablation study of proposed modules, and sensitivity of the ratio of negative supports and the dimension of phenotype. We conduct additional analysis on different backbone models in appendix B.2 and analysis on different distance measures in appendix B.3
Analysis on False Negatives. Due to the one-to-many mapping between disease and drug, there exist many false-negative patients for a given drug. For each drug, we calculate the prevalence of false-negative records (i.c., percentage of records that could but did not use the drug) using the MEDI (Wei et al. (213)) ding-disesee database. We report the average prevalence across all new drugs in table 2 The false-negative records range from 20 to 30% for both datasets. This level of noise can largely confuse the model if not appropriately handled. We then correct the celse-negative la- bels (i.c., swap them from negative to positive) in the test query set, and re-calculate the adjusted Precision@ @ 100 for EDGE, which increases by 18.0% on MIMIC-IV data and by 20.6% on claims data.
Table 2: Analysis on the influence of false negatives. Prev.: prevalence; Orig.: original; Adj: adjusted.
Datasel
Prev.
Orig. Pör 100
Adj. P0100
MIMIC-IV
31.5%
0.2251
0.4053
Claims
22.1%
0.1254
0.3317
Ablation Study of Module Importance. In the ablation study, we evaluate the contribution of each proposed module. According to the results in table 3 we observe that the multi multi-phenotyy phenotype patient encoder contributes the most: switching it with a single vector patient representation will decrease the ROC-AUC by 16.9%. Removing drug-dependent patient distance also decreases the performance by large margin, as the model cannot adapt the metric space to a specific drug. The untology-enhancee drug representation and knowledge-guidee negative sampling both slightly im- prove the model performance. Changing ontology-enhancee representation obtained from the drug name and description ignores the high-level category infor- mation shared between existing and new drugs. Changing mooledge-guuides negative sampling to uniform random sampling might confuse the model with the prevalent false negatives.
drug representation to the basic drug
7

---

Under review as a conference paper at ICLR 2023
Table 3: Ablation study.
MIMIC-IV
Claims
Method
ROC-AUC
P@ 100
RØ 100
ROC-AUC
P@ 100
R@ 100
Remove ontology-enhancee drug representation
0.8239
0.1584
0.1411
0.6809
0.0985
0.0672
Remove multi-phenotype patient representation
0,7992
0,1555
0.1312
0.6910
0.0818
0.055]
Remove drug-dependent distance mensure
0.8033
0.1687
0.1542
0.7078
0.1055
0.0758
Remove knowledge guidee negative sampling
0.8175
0.1899
0.1725
0.7149
0.1116
0.0789
EDGE
0.8608
0.2251
0.1907
0.7275
0.1254
0,0803
Analysis on the Influence of Hyper- parameters. We evaluate the effect of
two important hyper-parameters: the ratio of
ROCAUC
negative supports and the dimension of the
phenotype vector on MIMIC-IV data. The
results can be found in figure 3 where the
results of both EDGE and FEAT (best baseline) reported. The performance increases as the ratio of negative support records or the dimension of phenotype representation. Inter- estingly, we find the EDGE can outperform the best baseline method even if we set the dimension of phenotype to 1. That is to represent each patient with single vector (the same the prototypical network). This confirms the benefits of leveraging phenotype knowledge and drug ontology.
support
Dimension
Figure 3: The effect of negative support ratio and dimension of phenotype representation.
4.4 QUALITATIVE ANALYSTS
This section presents qualitative analysis to further investigate the performance of different drugs and the learned importance scores and representations.
Analysis on Per- Per-Category Performance. We first group new drugs by ATC-2 level and calculate the per-category performance of EDGE on MIMIC-IV data. Due to space limitations, we report the top-5 and bottom-5 categories in table 4 and show the full results in the appendix [..4] From the per-category result, the berf-perfo drugs are typically more targeting specific diseases. For exam- ple, antineoplastic agents are medications used to treat cancer, and antivirals are drugs targeting specific virals such as influenza and rabies. The support patients for such drugs usually have specific diseases that act as prominent indicator for the prescription of the drugs. On the other hand, the worst performing drugs are usually very general, such antibacterials or anti- infectives, which applies to various circumstances. The limited number of support patients cannot capture the broad applications of these drugs, which leads to lower performance.
Table Performance by drug category.
Drug Category
ROC-AUC
Antincoplastic agents
0.9882
Antivirals
0.9754
Urologicals
0,9738
Anti-park parkiarkkinnn drugs
0,9611
Other alimentary tract products
0,9412
Antibacterials
0.7888
All other therapeutic products
0.7827
Antinfective
0.7367
Calcium channel blockers
0.7285
Beta-adren adrenergic blockers
0.6074
Analysis of the Importance of Phenotypes. We show three example drugs from the testing set with the top-3 phenotypes ranked by the learned importance scores. The result can be found in table The top-ranked phenotypes match well with the usage of each drug. For example, for dactinomycin which treats variety of cancers, the top- ranked phenotypes are all cancer-related. Interestingly, EDGE. also learns to pay attention to some common co- morbidities for a given disease. For example, for the drug rasagiline which is used for Parkinson's 's disease, EDGE also attends to hypertension and anemia, which often seen in patients with Parkinson's 's disease.
Table 5: Top-3 phenotypes ranked by the learned importance scores. Drug
Usage
Top-3 Phenotypes
Other
Dactinomycin
Lang Cancer
Ncoplasms
Essential hypertension
Nehivolol
Hypertension
Circulatory disease
Surgical procedures
Purkinson
Rasagiline
Parkinson
Hypertension
Anemia
Analysis on Drug Representation. For qualitative analysis, visualize the learned ontology- enhanced drug representation on MIMIC-IV data with t-SNE (van der Maaten & Hinton 2008). As shown in figure 4 the representations for drugs with similar treatment effects cluster into smaller
8

---

Under review as a conference paper at ICLR 2023
categories. Further, the representations for new drugs also align with existing drugs. This indicates that the model adapts to new drugs by linking the new drugs to existing drugs with similar treatment effects.
Ophthalmology
Ord drug New cnve
Psycholoptics
Mineral supplementi
Antihistamines
Corticosterolds
for systemic use
Ohuregice
Innhunosuppressang
Hormonas
Antimycosttterikk
Plychoneoleptic
Butbbattrfaas
Amtiepllepticg
Antineoplastic
Brugs
Antilungal
obstrycttin
Corticosterolds.
dermatological
preparations
modityina
Amtithrombotic
agents
Figure 4: I-SNE plot of the learned ontology-enhanced drug representation. Node color represents ATC-2 level (coarse) categories while circle represents ATC-4 (fine-grained level categories.
RELATED WORK
Drug Recommendation. Drug recommendation aims at suggesting personalized medications to patients based on their specific health conditions. In practice, drug recommendation can act as a de- cision support system that helps doctors by supporting decision-making and reducing workload. Ex- isting works typically formulate the problem as a multi-label classification task. LEAP (Zhang et al.] 2017) proposes sequence-tor -t-sequence model to predict drugs given the patient's diagnoses. Later works try to model the longitudinal relations via knowledge graph (Wang et al. (017). attention mechanism (Choi al., 2016a), model pre-training (Shang al. 2019a), and memory network (Le) ct al. 2018 Shang et al. | 2019b). More recent works further incorporate more domain-related in- ductive bias. For example, SafeDrug (Yang al.) (02I) leverages the global and local molecule information, and 4SDrug (Tan et al. (022b) utilizes the set-to-set (set of symptoms to set of med- ications) module. Despite the good performance on existing drugs, these models are no longer applicable when new drugs appear. That is why we study new drug recommendation problem in this paper.
Few-Shot Learning. Few-shot learning aims at quickly generalizing the model to new tasks with a few labeled samples (Wang et al. 2020). Existing works can categorized into metric-learning based approaches that aims establish similarity or dissimilarity between classes (Vinyals al. 2016) [Sung et al.) 2017 Snell et al.] 2017 Oreshkin et al.) 2019 Cao et al. 2021 Ye et al. 2021) and optimization based approach seeks learn good initialization point
that can adapt to new tasks within a few parameter updates (Finn et al. 2017, Nichol et al. 2018 Rusu et al. 2019 Yao et al.) (2021b) [Png & Pan 2022). For recommendation problem, few-shot learning is leveraged to solve the cold-start problem, where the goal is to quickly adapt the model to new users with limited history (Lee al. 2019 Du al. 2022 Dong et al. 2020: Lin et al. 202T Shi al. 2022). For healthcare problem. few-shot learning is applied to improve diagnosis of uncommon diseases (Zhang Suo et al. 20200 Tan al. 2022a), drug discovery (Altae- TrannTTrrneee al. [017]] Yao al., 2021a Luo et al.] 2019). novel cell type classification (Brbić et al.) 2020). and genomic survival analysis QQu al. 2020). However, when it comes to new drug recommendations, these works fail to capture the complex relationship between diseases and drugs, and will also be largely influenced by the noisy supervision signal.
al. 2019:
CONCLUSION
As new drugs get approved and come to the market, drug recommendation models trained on existing drugs can quickly become outdated. In this paper, we reformulate the task of drug recommendation for new drugs, which is largely ignored by prior works. We propose meta-learning framework to solve the problem by modeling the complex relations among diseases and drugs, and eliminating the numerous false-nege -elgative patients with external knowledge base. We evaluate EDGE two med- ical datasets and show superior performance compared to all baselines. We also provided detailed analyses of ablation analyses and qualitative studies.

---

Under review as a conference paper at ICLR 2023
REFERENCES
Emily Alsentzer, John R. Murphy, Willie Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, and Matthew B. McDermott, Publicly available clinical bert embeddings, 2019. URL https:
//arxiv. 033333333
Han Altae-Tran, Bharath Ramsundar, Aneesh S Pappu, and Vijay Pande. Low data drug discovery with one-shot learning. ACS central science, ((4):283-293, 2017.
Benjamin E Blass. Basic principles of drug discovery and development. Academic Press, San Diego, CA, edition, June 2021.
Maria Brbić, Marinka Zitmik, Sheng Wang. Angela O Pisco, Russ B Altman, Spyros Darmanis, and Jure Leskovec. Mars: discovering novel cell types across heterogeneous single-cell experiments. 2020.
Nature methods,
Kaidi Cao, Maria Brbic, and Jure Leskovec. Concept Learners for Few-Shot Learning, March 2021.
URL httt:://axxv..org http://wwww..rrgaaa/////....... http:g/abs2200..000 07375 arXiv:2007.07375 [cs, stat].
Edward Choi, Mohammad Taha Bahadori, Joshua A. Kulas, Andy Schuetz, Walter E. Stewart, and Jimeng Sun. Retain: An interpretable predictive model for healthcare using reverse time attention mechanism. In Proceedings of the 30th International Conference on Neural Information Process- ing Systems, NIPS' 16, pp. 3212-351200 Red Hook, NY, USA, 2016a. Curran Associates Inc. ISBN 97815 10838108
Edward Choi, Andy Schuetz, Walter F Stewart, and Jimeng Sun. Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association, 24(2):361 08 2016b. ISSN 1067-5027. doi: 10.1093/jamia/cwwl/l 12. URL
htttps://doi.orge
1093/ jamm a ocw1
Edward Choi, Mohammad Taha Bahadori, Le Song, Walter F. Stewart, and Jimeng Sun. GRAM: Graph-based Attention Model for Healthcare Representation Learning, April 2017. URL /tthttp:
wwwxxiv.org/abaa 6161 07012 arXiv:161 1.07012 [cs, stat].
Edward Choi, Zhen Xu, Yujia Li, Michael W. Dusenberry, Gerardo Flores, Yuan Xue, and An- drew M. Dai. Learning the graphical structure of electronic health records with graph convolu- tional transformer, 2019. URL htttt:tttaatxiiiii www.wwwwwww..iaaooaaaaaaaaaaaeae hbtpg//bbs/999 04716
Manqing Dong, Feng Yuan, Lina Yao, Xiwei Xu, and Liming Zhu. Mamo: Memory-augmented meta-optimizzaton for cold-start recommendation, 2020. URL https::/azxiv..orr abs/ 2,0770011
Yuntao Du, Xinjun Zhu, Lu Chen, Ziquan Fang, and Yunjun Gao. Metakg: Meta-learning on knowl- edge graph for cold-start recommendation. IEEE Transactions Knowledge and Data Engineer- ing. 2022.
Ruben G. Duijnhoven, Sabine M. J. M. Straus, June M. Raine, Anthonius de Boer, Aro W. Hoes, and Marie L. De Bruin. Number patients studied prior to approval of new medicines: A database analysis. PLOS Medicine, 10(3):1-8, 03 2013. doi: 10.1371/jurmaa..pmed 1001407.
URI.httss:
htRt:hhtts://do/ //dp::///o.... org/ /0.gg/10.110 1371 13t///00111//u/rraaaa journal .medd. 1001007
U.S. Food and Drug Administration FDA. 2022.
Novel drug approvals for 2022,
URL
https://www who. int htt/sstanddrrd//cesssiifiiie
classification-On diseasisessssss
Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In Doina Precup and Yee Whye Teh (eds.). Proceedings of the 34th International Conference on Machine Learning. volume 70 of Proceedings of Machine Learning Research, pp.
1126-1135, PMLR, 06-11 Aug 2017. URL htttps::/pppoeeeeeetteete ngs m prcss//7C/ Finn 7a. htmll
Healthcare Cost & Utilization Project H. CUP. Clinical classifications software (cos), 2010. URL https: : www. hwwp-us.uuu....... awrr.argggg.. wttp:/..............oooooooo.o http:////oooosssoooaaeeee//////s jsp
10

---

Under review as a conference paper at ICLR 2023
A Johnson, L Bulgarelli, T Pollard, Horn, LA Celi, and R Mark. Mimic-iv (version 1.0), 2020.
Hung Le, Truyen Tran, and Svetha Venkatesh. Dual memory neural computer for asynchronous two- view sequential learning. In Proceedings of the 24th ACM SIGKDD International Conference on
Knowledge Discovery amp; Data Mining, KDD '18, pp. 1637-1645, New York, NY, USA, 2018. Association for Computing Machinery. ISBN 9781450355520. doi: 10. 10.1145/3219819.3219999
URL https: //tp:::////o.o..
/oi (oo.i.org/100. 1tt.//2.....11/2211111999999999 32199811
Hoyeop Lee, Jinbae Im, Seongwon Jang. Hyunsouk Cho, and Sehee Chung. MeLU: Meta-Learned
User Preference Estimator for Cold-Start Recommendation. arXiv: 1908.00413 [cs], July 2019.
URL http: org. abs /1t.:/918...0 00413
arXiv: 1908.00413.
Yikuan Li, Shishir Rao, José Roberto Ayala Solares, Abdelaali Hassaine, Rema Ramakrishnan, Dex- ter Canoy, Yajie Zhu, Kazem Rahimi, and Gholamreza Salimi-Khorshide BEHRT: Transformer for Electronic Health Records. Scientific Reports, 10(1):7155, December 2020, ISSN 2045- 2322. doi: 00000////1118800--2227777 641598- 020- 62922-y
URL http:/ /www
Xixun Lin, Jia Wu, Chuan Zhou, Shirui Pan, Yanan Cao, and Bin Wang. Task-adaptive Neural Process for User Cold-Start Recommendation, February 2021. URL http://arxiv.org/ abs/ / 220.2213...00 06137 arXiv:2103.06137 [cs].
Yunan Luo, Jianzhu Ma, Xiaoming Zhao, Yufeng Su, Yang Liu, Trey Ideker, and Jian Peng. Miti- gating data scarcity in protein binding prediction using meta-learning, In RECOMB, pp. 305-307. Springer, 2019.
Alex Nichol, Joshua Achiam, and John Schulman. On first-order meta-learning algorithms, 2018.
URL httpttts////xii..oooo attp//bb//1133 02999
Boris N. Oreshkin, Pau Rodriguez, and Alexandre Lacoste. TADAM: Task dependent adaptive met- ric for improved few-shot learning, January 2019. URL http://arr..v.oor//a///BBB
10123 arXiv:1805.100 10123 [cs, stat].
Danni Peng and Sinno Pan. Clustered task-aware meta-lcarning by learning from learning paths, 2022. URLhttps::
Yeping Lina Qiu, Hong Zheng, Arnout Devos, Heather Selby, and Olivier Gevaert. A meta-learning approach for genomic survival analysis. Nature communications, 11(1):1-11, 2020.
Laila Rasmy, Yang Xiang. Ziqian Xie, Cui Tao, and Degui Zhi. Med-bert: pretrained contextual-
ized embeddings on large-scale structured electronic health records for disease prediction. NPJ digital medicine, 4(1):86, May 2021. ISSN 2398- doi:
URL
Jiacheng Jiang Guilin Qi Rui Wu, Zhaopeng Qiu and Xian Wu. Conditional generation net for medication recommendation. In wwW '22 The Web Conference 2022, Virtual Event, Lyon, France, April 25-29, 2022, 2022.
Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell. Meta-learning with latent embedding optimization. In International Confer- ence on Learning Representations, 2019. URL httpp:://ppeeeeiieeets net. wttp:///wwwwe.eeeeoommmmmmm. forummidd BJgk1hAcK?
Junyuan Shang, Tengfei Ma, Cao Xiao, and Jimeng Sun. Pre-training of Graph Augmented Trans- formers for Medication Recommendation, November 2019a. URL http://arxiv.oro/ abs/1906. 00346 arXiv:1906.00344 [cs].
Junyuan Shang, Cao Xiao, Tengfei Ma. Hongyan Li, and Jimeng Sun. GAMENet: Graph Aug-
mented MEmory Networks for Recommending Medication Combination. Proceedings of the
AAA/ Conference Artificial Intelligence, 33:1 18:112661133, 1133, July 2019b. ISSN 2374-3468, 2159. 5399. doi: 10. 33000..331100001011133333 URL https: aaai.aaaaaa.. www.aaaaaaaaaaaaaaaaaaaaaa....at / /tt///idxx./.. php/
AAAT wtt.:ttt..eeeeeeeeeeeeeeeeeeeeee

---

Under review as a conference paper at ICLR 2023
Yuxiang Shi, Yue Ding, Bo Chen, Yuyang Huang, Ruiming Tang. and Dong Wang. Task aligned meta-learning based augmented graph for cold-start recommendation. arXiv preprint arXiv:2208.05776, 2022.
Jake Snell, Kevin Swersky, and Richard S. Zemel. Prototypical Networks for Few-shot Learning, June 2017. URL httL:tttp//axiii> wttp///gg///aaoooooooooa /tt.:////....o//a/////33333 05175 arXiv:1703.05175 [cs, stat].
Flood Sung, Yongxin Yang. Li Zhang, Tao Xiang. Philip H. S. Torr, and Timothy M. Hospedales. Learning to compare: Relation network for few-shot learning, 2017. URL /ttps://///xx
org/abs/171s 06025
Qiuling Suo, Jingyuan Chou, Weida Zhong, and Aidong Zhang. Tadanet: Task-adaptive net- work for graph-enriched meta-learning. In Proceedings of the 26th ACM SIGKDD Interna- tional Conference on Knowledge Discovery amp; Data Mining, KDD '20 pp. 1789-1799, New York, NY, USA, 2020. Association for Computing Machinery. ISBN 9781450379984. 10.1144/3394486.400333 URL
doi: htttss/s:://de /ttp:://///o...//////////// 1tt.://...11133444.....4 33444 3403230
Yanchao Tan, Chaochao Chen, Carl Yang, Weiming Liu, Xiangyu Wei, Longfei Li, Jun Zhou, and Xiaolin Zheng. MetaCare++: Meta-Learning with Hierarchical Subtyping for Cold-Start Diag- nosis Prediction in Healthcare Data. pp. 11, 2022a.
Yanchao Tan, Chengjun Kong, Leisheng Yu, Pan Li, Chaochao Chen, Xiaolin Zheng, Vicki Hertzberg, and Carl Yang. 4SDrug: Symptom-based Set-to-set Small and Safe Drug Recom- mendation. pp. 11, 2022b.
Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-SNE. Journal of Ma- chine Learning Research, 9:2579- 2605, 2008. URLhhttp://wet www. jmli www. pxxzg//pprs/v///
wandrrmaatteni htmll
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, É ukasz Kaiser, and Illia Polosukhin. Attention is all you need. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, nett (eds.), Advances in Neural Information Processing Systems, volume 30. Curran Asso- ciates, Inc., 2017. URL https: httL:httt:://ttttt http:::://proceeedinee www.ilnggsnneuiiiaee 7dee (egpa//sseccce11oobb00cccaaaaaa3 ott.://eeaaa...ooooooooo/e/rre/a cc/papper/211//iile/
Fergus, Vishwanathan, and R. Gar-
pdf
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukeuoglu. and Daan Wierstra. Match- ing networks for one shot learning, 2016. Ut//attss://axxxv....
abs 1606.04080
Meng Wang. Mengyue Liu, Jun Liu, Sen Wang, Guodong Long, and Buyue Qian. Safe medicine recommendation via medical knowledge graph embedding. CoRR, abs/1710.05980, 2017. URL http://arxiv. org/ abs/1710. 05980
Yaqing Wang, Quanming Yao, James T. Kwok, and Lionel M. Ni. Generalizing from few exam- ples: A survey on few-shot learning. ACM Compul. Surv, 53(3), jun 2020. ISSN 0360-0300. doi: 10.1 10.1145/3386252. URL https: /oo.. wwg/10........oo...oooooo....... 1ew.1115/3330 33862222
Wei-Qi Wei, Robert M Cronin, Hua Xu, Thomas A Lasko, Lisa Bastarache, and Joshua C Denny. Development and evaluation of an ensemble resource linking medications to their in- dications. Journal of the American Medical Informatics Association, 20(5):954 04 2013.
ISSN 1067-5027. doi: 10. 10.1136//mmaan12221-000044
ami amiajja 2011-111-0120000
World Health Organization WHO. Anatomical therapeutic chemical (ate) classifica-
tion system, 1976 URL httpp::/wwwtt who. who.iin/toooss/tteett dorlec-ddd-oooe tttps//tattt-ddddooookkk///
atc-classification
World Health Organization WHO. International statistical classification of diseases and
related health problems (ied), 1993. URL httpp::////wt who wtt...nnt/standad....
c classi aisssiiiiiiioonscsssssseee of-oonoooosseeeo
12

---

Under review as a conference paper at ICLR 2023
Cao Xiao, Edward Choi, and Jimeng Sun. Opportunities and challenges in developing deep learn- ing models using electronic health records data: systematic review. Journal of the Ameri- can Medical Informatics Association, 25(10):1419-142, 06 2018. ISSN 1527-974X. doi: 10. 00.1099/jamiaaocy068. httpsts::: /ooi wwe.dii..ogglleee www.r............. 1093/ jami samia a ocy068
Chaoqi Yang, Cao Xiao, Fengiong Ma, Lucas Glass, and Jimeng Sun. SafeDrug: Dual Molecular Graph Encoders for Safe Drug Recommendations. arXiv:2105.00 027111 [cs]. May 2021. URL
http://arxiv..n http://g.a///2.......o0 02711 arXiv: 2105.02711.
Huaxiu Yao, Kai Huang, Linjun Zhang, Ying Wei, Li Tian, James Zou, Junzhou Huang, et al. Improving generalization in meta-learning via task augmentation. In International Conference on Machine Learning, pp. 11887-11897. PMLR, 2021a.
Huaxiu Yao, Yu Wang, Ying Wei, Peilin Zhao, Mehrdad Mahdavi, Defu Lian, and Chelsea Finn. Meta-learning with an adaptive task scheduler. In A. Beygelzimer, Y. Dauphin, P. Liang, and Wortman Vaughan (eds.), Advances Neural Information Processing Systems, 2021b. URL net htt///ooommmmmmMssseeee
Oq.
Huaxiu Yao, Ying Wei, Loig-Kai Huang, Ding Xue, Junzhou Huang, and Zhenhui Jessie Li. Func- tionally regionalized knowledge transfer for low-resource drug discovery. Advances in Neural Information Processing Systems, 34:8256-8268, 2021c.
Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, and Fei Sha. Few-Shot Learning via Embedding Adapta- tion with Set-to-Set Set Functions, June 2021. URL http:::/aaaxxx..ogg////////2211/ arXiv:1812.0366 [es].
Xi Sheryl Zhang, Fengyi Tang, Hiroko H. Dodge, Jiayu Zhou, and Fei Wang. Metapred: Meta- learning for clinical risk prediction with limited patient electronic health records. In Pro- ceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery amp; Data Mining, KDD '19, pp. 2487-2495, New York, NY, USA, 2019. Association for Com- puting Machinery. ISBN 9781 450366006. doi: 10. 10.1145/292200.333777 URL https:
wttpg//1..115./// 1145/32925000 3330779
Yutao Zhang, Robert Chen, Jie Tang, Walter Stewart, and Jimeng Sun. Leap: Learning to prescribe effective and safe treatment combinations for multimorbidity. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD *17, pp. 1315-1324, New York, NY, USA, 2017. Association for Computing Machinery. ISBN 9781450348874. doi: 10.1145/3097993330981009
URL hatps:://eci.org/1.... 1145/
3097983. 3098109
Marinka Zitnik, Monica Agrawal, and Jure Leskovec. Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, 34(13)i447-i446, 06 2018. ISSN 1367-4803, doi: 10. 10.1093/hoomfomaati//ttty//
bty294
13


Reviews:
[{"review_id": "ycaNATSHXXn", "reviewer": "Reviewer_54ZT", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper proposes the use of few-shot learning together with the use of medical ontologies under the prototypical network architecture for new drug recommendation. The use of few-shot learning and ontologies is motivated by the fact that data related to new drugs is limited. Several tricks are suggested, including the use of ontological representation computed using Clinical-Bert and ontology, phenotype representation based on CCS, allowing the phenotype-based representation to be drug-dependent, as well as using ontology to obtain negative samples for training the prototypical network for handling the false-negative drug issue. They tried to compare with a number of benchmark methods and reported improvement on accuracy for new drug recommendation.", "summary_of_the_review": "Introducing various ways to integrate the ontological approaches to the address the challenges encountered in new drug recommendation is the main contribution of this paper. The contribution is not considered fundamental. While treating new drug recommendation as few-shot learning is an interesting one, the validity of recommending new drugs based on a small set of patient records related to the new drugs is unclear. This weakens the motivation of the paper, while I find this work and proposed tricks interesting.", "strength_and_weaknesses": "Strengths:\n+ The problem formulation is clearly presented in general. \n+ The use of ontological approach to deal with the cases with a small dataset is a reasonable one. Also the way they make use of the ontology is proper and reasonable. E.g., using the knowledge for the negative sampling to handle the false-negative cases is a smart one.\n+ Introducing the phenotypes can support interpretability of the model inferred as presented in the paper.\n\nWeaknesses:\n- While the problem seems interesting, it is not clear if the proposition of recommending new drugs based on only the support of a few patient record is a valid one or not. From the safety perspective, should drug be more carefully evaluated before it should be recommended? This seems to make it different from other recommendation tasks. This issue is not much discussed in the paper. This weakens the motivation of the problem and then the importance of the proposed techniques.\n- The main novelty is more on the way of integrating existing methods, and less on fundamental ones.\n- Only diagnosis and procedures are extracted to define the phenotypes. Works on computational phenotyping often include lab tests results. Should they be included as well? If not, what are the reasons? Related consideration is not presented in the paper.\n- Regarding presentation, the organization is generally ok and the writing can be further improved. For example, while I agree that the adoption of phenotype-based representation is good, related justification is not clearly presented. A bit more details regarding how to extract phenotypes from CCS will be useful.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "jn1TcqbQ6yA", "reviewer": "Reviewer_YURq", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "A meta-learning framework, EDGE, for new drug recommendation is proposed using relationships between drugs and diseases.", "summary_of_the_review": "The meta-learning framework proposed using few shot learning relies primarily on existing techniques, already well studied in literature. \nNo new techniques are presented.\n\nMany sophisticated algorithms exist for training -- why is a gradient descent algorithm chosen, given that it is known to be slow?", "strength_and_weaknesses": "Strengths\n- Paper is very well written\n- Uses two real-world datasets MMIC-IV and CLAIMS\n- The Edge framework is compared against several well known techniques from literature such as GameNet, SafeDrug etc.\n\nWeakness\n- Relies on standard techniques proposed in literature -- thus less innovative.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "Not applicable"}, "extra_text": {}}, {"review_id": "e5xhJu_331", "reviewer": "Reviewer_GvBg", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The authors formulate the task of new drug recommendation as a few-shot learning task and propose a framework to solve it. They first utilize the drug ontology to learn the drug representation. Then they represent each patient as a set of phenotype vectors. Given a new drug, they make recommendations by measuring the similarity between representations of query patients and corresponding prototypes. They further introduce a knowledge-guided negative sampling strategy to eliminate false-negative effect.", "summary_of_the_review": "New task, reasonable but less novel method, unclear application scenario. Less qualified to publish in ICLR. ", "strength_and_weaknesses": "Strengths:\n1.\tProposing a new and meaningful task of new drug recommendation.\n2.\tModeling the task as a few-shot classification problem which is reasonable. \n\nWeaknesses:\n1. The application scenario is not clear. The designed experiment to answer the following question: given a new drug, which patient would take it? While in clinical application, it's hard to recommend a set of proper drugs for a specific patient -- it may take too much time to check all drugs one by one as the proposed model only take one drug as input.\n\n2. It's not clear that, considering EDGE lacks patients' history and the few-shot learning setting, can EDGE perform well on \"old\" drugs compared with baselines such SafeDrug? If not, how to merge this new drug rec model with traditional \"old\" drug rec model to obtain a complete drug list is also a problem.\n\n3. \"Analysis on False Negatives\" in sec. 4.3 seems not reasonable, as there exist lots of drug-drug interaction and drug-disease interaction. Adjusting the metrics according to MEDI without considering DDI doesn't make sense.\n\n4. The proposed solution is composed four existing modules, which lowers the technical novelty of the work. \n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "Ehe7hkwi0S", "reviewer": "Reviewer_usk8", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper considers new drug recommendations as a few-shot learning problem.  The proposed model can quickly adapt to the recommendation for a new drug with limited prescription data from a few support patients by addressing complex relations among diseases and drugs and numerous false-negative patients. Experiments on two real-world datasets demonstrate the effectiveness of EDGE.\n", "summary_of_the_review": "Below the acceptance threshold. \nPlease see Strength And Weaknesses for detail.", "strength_and_weaknesses": "S1: The methodology part is written in a clear way and well addressed the main claims in the introduction part. \n\nS2: It is an interesting idea to consider new drug recommendations as a few-shot learning problem.\n\n\nW1: It's concerned to apply bi-direction GRU to the contextualized disease representations, as there are no directions among diseases. It just demonstrated better performance by using bi-directional GRG from the experiment.\n\nW2: The proposed model does not consider patients' historical EHR data. So some facts, such as disease progression, and ongoing treatments are hard to integrate into the EDGE model, which cannot support the paper’s claim about tackling complex relations among diseases and drugs\n\nW3: It is better to involve more components, e.g., loss functions, and data flow progress, in Figure 1 to make it clearer.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject