Title: REFINE: A Fine-Grained Medication Recommendation System Using Deep Learning and Personalized Drug Interaction Modeling
Venue: NeurIPS 2023
Accepted: yes
Keywords: Fine-grained Medication recommendation

Drug Interaction Severity
Abstract: Patients with co-morbidities often require multiple medications to manage their conditions. However, existing medication recommendation systems only offer class-level medications and regard all interactions among drugs to have the same level of severity. This limits their ability to provide personalized and safe recommendations tailored to individual needs. In this work, we introduce a deep learning-based fine-grained medication recommendation system called REFINE, which is designed to improve treatment outcomes and minimize adverse drug interactions. In order to better characterize patients’ health conditions, we model the trend in medication dosage titrations and lab test responses, and adapt the vision transformer to obtain effective patient representations. We also model drug interaction severity levels as weighted graphs to learn safe drug combinations and design a balanced loss function to avoid overly conservative recommendations and miss medications that might be needed for certain conditions. Extensive experiments on two real-world datasets show that REFINE outperforms state-of-the-art techniques.

REFINE: A Fine-Grained Medication Recommendation System Using Deep Learning and Personalized Drug Interaction Modeling

Abstract

Patients with co-morbidities often require multiple medications to manage their conditions. However, existing medication recommendation systems only offer classlevel medications and regard all interactions among drugs to have the same level of severity. This limits their ability to provide personalized and safe recommendations tailored to individual needs. In this work, we introduce a deep learning-based finegrained medication recommendation system called REFINE, which is designed to improve treatment outcomes and minimize adverse drug interactions. In order to better characterize patient's health conditions, we model the trend in medication dosage titrations and lab test responses, and adapt the vision transformer to obtain effective patient representations. We also model drug interaction severity levels as weighted graphs to learn safe drug combinations and design a balanced loss function to avoid overly conservative recommendations and miss medications that might be needed for certain conditions. Extensive experiments on two real-world datasets shows that REFINE outperforms state-of-the-art techniques.

1 Introduction

The widespread adoption of digital patient records in hospitals has led to the creation of a rich knowledge base in the form of Electronic Health Records (EHR). This valuable resource can be utilized for various tasks in the medical domain, such as mortality prediction [19], outcome prediction [14], and medication recommendation [16, 2]. Concurrently, advances in drug therapy have resulted in growing interest in research aimed at enabling personalized and safe treatment recommendations for managing patient conditions. Current medication recommendation systems are coarse-grained, providing recommendation at the class level rather than individual drugs, and are unable to differentiate between patients who require single versus multiple drugs from the same class. For example, while diabetic patients are often prescribed Metformin to manage their blood sugar levels, patients with uncontrolled blood sugar level may be given additional blood glucose lowering drug such as Glipizide which belong to the same medication class A10B 1 as Metformin. As such, the recommended treatment regime is the same for both types of patients which is suboptimal. Thus, it is essential to develop a fine-grained recommender system capable of providing recommendations at the drug level.

Existing recommender systems that consider drug interactions assume that all the interactions have the same severity. This is not true in practice as resources on drug information such as the DDInter database [23] record different levels of interaction severity, ranging from mild to moderate to severe. By assuming all interactions are equally severe, existing recommender systems tend to avoid any combination of drugs with known interactions, even when the benefits outweigh the potential risks.

For example, in the benchmark MIMIC-IV [10], Metformin and Lisinopril are prescribed together 36% of the time for patients with diabetes and hypertension to better manage their conditions, despite having a moderate level of interaction severity.

In this work, we propose a fine-grained medication recommender system that takes into account the severity of drug interactions and leverages information gathered over past visits to provide safe and personalized drug treatment regimes. We represent the varying severity of drug interactions as a weighted graph and employ a graph attention network to obtain drug interaction representations, enabling the system to learn safe drug pair combinations. Moreover, to avoid overly conservative recommendations, we design a balanced loss function that weighs the benefits of a pair of drugs against the severity of any potential drug interaction. This approach allows for more effective and personalized treatment recommendations while minimizing the risk of adverse drug interactions. Additionally, since clinicians often titrate treatment regimes for patients based on their past lab test responses and medication dosages, we incorporate such trend information and adapt the vision transformer [7] (ViT) to learn an effective patient representation.

Our contributions can be summarized as follows: (1) We design a novel deep learning system that combines knowledge from patient information over multiple visits and severity-based drug interaction database to provide personalized, safe, and accurate fine-grained medication recommendation. (2) We propose to extract and model trend information from the medication dosage titrations and lab test responses over multiple visits to obtain effective patient representation. This enables the system to adjust the treatment regime as the patient condition changes. (3) We design a balanced DDI loss function that weighs the benefits of a pair of drugs against the severity of any potential drug interaction. (4) We provide comprehensive quantitative and qualitative evaluations on a benchmark medical dataset MIMIC-IV [10] and a proprietary outpatient dataset to demonstrate the capability and effectiveness of our proposed approach in providing fine-grained medication recommendations while considering drug interaction severity and patient history.

2 Related Work

Early works in medication recommendation focus on learning a collection of rules from EHR. Solt and Tikk [17] extract rules from discharge summaries, while Lakkaraju et al. [11] use Markov Decision Processes to learn the mapping between the patient characteristics and treatments. However, this approach may introduce conflicting rules, and is difficult to generalize and scale. Subsequent works employ recurrent neural networks to model sequential dependency in patient's past visits to improve the performance of medication recommendation [1, 12]. However, they do not consider drug interactions, which are crucial to minimize adverse drug reactions.

LEAP [27] incorporates an external drug-drug interaction (DDI) database and uses a recurrent decoder to model the drug-disease relationship in the current visit for recommending a set of medications. GameNet [16] uses dynamic memory and Graph Convolutional networks (GCN) to personalize the medication recommendations based on patients' longitudinal visit history and drug interactions. CompNet [21] is another GCN based recommender system that models relation among patient's past visits as well as the drug interactions by combining reinforcement learning with relational GCN.

PREMIER [2] introduces a two-stage recommender system that employs dual attention-based recurrent neural networks to model patient visits and adapts graph attention networks to model drug co-occurrences and drug interactions. SafeDrug [25] uses RNN based patient visit representation and incorporates drug molecular structure information to enhance the safety of the recommended drugs. They introduce a threshold during training to penalize the recommended set of medications if the total drug interaction in the set is above the threshold. MICRON [24] uses recurrent residual networks to capture the medication changes over the visits, and incorporate drug interaction information from an external database in the design of their DDI loss function.

COGNet [22] introduces a copy module, which generates the probability of a medication being repeated from past visits. They model the drug interaction information with the help of a GCN and a DDI loss function. MoleRec [26] introduces a hierarchical architecture to model the association between drug substructures at the molecular level and the target disease to provide personalized medication recommendation. [18] proposes a causal inference based medication recommendation model. They model patient visit information as causal graphs and DDI as propositional satisfiability

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problem. However, they use additional information such as patient symptoms which are not available in our datasets, making a direct comparison difficult.

Our approach differs from existing works in that we provide fine-grained medication recommendation by explicitly modeling drug names, and the trend information in the medication dosages as well as the lab test responses gathered over multiple visits. We also ensure the safety of the recommendations by modeling the severity of drug interactions to reduce the risk of harmful combinations.

3 Methodology

Figure 1 shows the framework of the proposed system REFINE for REcommendation of FINE-grained medication. The input is a list of patient visits. The i th visit is represented as x i = [ d i , l i , m i ] where d i is a multi-hot vector of diagnosis, l i is a vector of lab tests and responses, m i is the vector of medications and dosages, and i ∈ [1 , T ] . All continuous values are normalized using min-max normalization [9]. The goal is to predict a set of medications y T for the T th visit, taking into account the patient history, the trend in the medication dosages and lab test responses, as well as the severity of drug interactions.

3.1 Trend Information Extraction

We observe that the treatment regimes for patients with the same disease vary depending on whether their conditions are well managed. This is typically reflected in the fluctuations in the lab test responses over the various visits. Further, clinicians often titrate the dosage of first-line medications before introducing additional medications to achieve the optimal treatment outcome. As such, we extract trend information from medication dosages and lab test responses over the past visits in the form of slope and variance from [15].

Let var i m and slope i m be the vectors of dosage variance and rate of dosage change for the medications across visits 1 to i respectively. The dosage variance and rate of dosage change for the k th medication can be computed as follows:

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where m j [ k ] is the dosage of the k th medication at the j th visit, µ i m [ k ] is the mean of the dosages of the k th medication across visits 1 to i , t j is the interval (in days) between the 1 st visit and the j th visit, t i is the average t j for j ∈ [1 , i ] . The variance var l i and slope slope l i for the lab test responses are obtained in the similar manner.

We stack the variance var i m and slope slope i m and medication dosages m i to obtain a 2D array ∈ R 3 × N m where N m is the total number of medications. We flatten the 2D array into a 1D array sequence, similar to the approach used in Vision Transformer (ViT) [7]. To do this, we concatenate the three values (medication dosage, slope, and variance) for every medication in a visit to obtain a 1D array sequence s i . We repeat the process for the lab test response information to obtain a 1D array sequence r i .

3.2 Patient Representation

To obtain a comprehensive representation of a patient's evolving health condition, it is crucial to capture and combine the sequential dependencies present in their EHR data. Specifically, we utilize three separate transformers [7, 20] to model the sequential dependencies in the patient's diagnosis, lab test response, and medication information over multiple visits. These transformer modules can be used individually or in combination to derive a patient representation even when some information sources are missing from the EHR data. The embeddings d e i , r e i , and s e i for diagnosis, lab test response, and medication dosage respectively are passed to their respective transformers to generate the patient representation q T as shown in Figure 2.

Each transformer is composed of multiple layers of encoders, and each encoder has a multi-head self-attention block followed by a position-wise feed-forward network. For the i th visit, the encoder in the first layer of the transformer takes as input the concatenation of the respective input embeddings from visits 1 to i . The input to the subsequent encoders is the concatenation of the outputs from the encoder of the previous layer. This helps to preserve and process the information from the patient's visit history. Residual connections are employed around self-attention mechanism to facilitate the propagation of lower layer features to higher layers. To stabilize and accelerate neural network training, we apply layer normalization after the multi-head self-attention block and the position-wise feed-forward network. We also apply dropout to avoid over-fitting. The outputs of the transformers for the i th visit are h d i , h r i , and h s i for diagnosis, lab test response, and medication dosage respectively.

Since our goal is to predict the set of medications for the current visit T , we fuse the encoded sequential representation for the diagnosis and lab test response up to the current visit, and the encoded representation for the medication dosage information up to the previous visit to obtain the

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3.3 Drug Information Representation

We observe that clinicians take into account the severity levels of interacting drug pairs and the severity of drug interactions can vary significantly. This information is crucial when prescribing medications to ensure patient safety. In addition, the frequency of co-occurrence of drug pairs observed in the EHR can provide valuable insights into commonly or rarely prescribed combinations, further guiding the decision-making process.

We represent drug co-occurrence information from the EHR and drug interaction from the DDI database DDInter [23] as two separate graphs G C = ( V, E C ) and G D = ( V, E D ) respectively. Each node n i ∈ V depicts a drug i . The weighted edges in E C and E D depicts the co-occurrence frequency and interaction severity between drug pairs, respectively. For co-occurrence, a weighted edge ( n i , n j , w C ij ) ∈ E C gives the co-occurrence of drug i and drug j with a frequency of w C ij . The co-occurrence frequency is calculated by counting the number of occurrences of a drug pair in the prescribed medication sets in the EHR. These values are normalized to range between 0 and 1.

For drug interactions, a weighted edge ( n i , n j , w D ij ) ∈ E D gives the interaction between drug i and drug j with a severity of w D ij . The severity information is given as Major , Moderate , Minor , or Unknown/No interaction in the DDInter database. In this work, we represent these severity levels numerically as 1, 0.66, 0.33, and 0, respectively.

We adapt GATv2 [5] to learn node representations from the weighted drug interaction graph G D . GATv2 has two layers with z attention heads in the first layer, and 1 attention head in the second layer (see Figure 3). For the first layer, the attention weight between nodes n j and n k at the b th attention head is calculated as follows:

where S j is the set of drugs that interact with drug j , a ′ is the transpose of the weight vector of a single layer feed-forward neural network, E b ∗ is the embedding matrix for the b th attention head with E b = [ E b ∗ ∥ E b ∗ ] [5], w D jk is the edge weight depicting the severity of interaction between drugs j and k , and ∥ denotes the concatenation operation. The output from the first layer for node n j is given by:

where σ is the sigmoid function. With this, the output from the second layer for node n j is given by:

where E is the embedding matrix for the second layer, and δ jk is obtained by applying Equation 4 on the output of the first layer for nodes j and k with w D jk updated to the average of δ b jk for 1 ≤ b ≤ z .

We obtain the drug interaction representation Z D by stacking u D j , j ∈ { 1 , 2 , ..., N m } and N m is the total number of drugs. Similarly, we can obtain a drug co-occurrence representation Z C by applying another GATv2 on the drug co-occurrence graph G C and stacking the node representation u C j , j ∈ { 1 , 2 , ..., N m } . The final drug information representation Z is given by:

where w is a parameter that controls the relative importance of the two representations.

The final output vector y T is obtained by applying attention-based aggregation on the patient representation and the weighted drug information representation as follows:

where λ = softmax( transpose( Z ) · q T ) and W is the gradient matrix of the transformation function in the linear layer.

We consider a medication is recommended if y T [ k ] > 0 . 5 , k ∈ { 1 , 2 , ..., N m } . To understand the factors influencing the model's recommendations, Appendix A shows how we can derive the contributions of the different information sources.

3.4 Training Objective

We formulate the recommendation task as a multi-label prediction task and employ the widely used binary cross entropy loss Lbce and hinge loss Lhinge [16, 3]. To capture the trade-off between the benefits and potential interactions of a drug pair, we propose a balanced drug interaction loss function Lbdi defined as follows:

where w D jk and w C jk depict the severity of drug interaction and the frequency of co-occurrence obtained from the graphs G D and G C respectively, β is a controllable factor which provides a balance between drug interaction and drug co-occurrence information.

The final objective function is given by:

where γ 1 and γ 1 are hyperparameters.

4 Performance Study

We implement REFINE in PyTorch and carry out the training on two NVIDIA Titan RTX GPU. We use two datasets, MIMIC-IV [10] and PRIVATE, in our experiments. MIMIC-IV is a publicly available inpatient dataset consisting of 299,712 patient (mostly ICU patients) data collected between 2008 and 2019, whereas PRIVATE is a proprietary outpatient dataset from 6 primary care clinics over a span of ten years. Table 1 gives the statistics. Patients with fewer than two visits are filtered out (see details in Appendix B).

We evaluate the accuracy as well as the level of drug interactions in the recommended medication set. For accuracy, we use the metrics precision-recall area under the curve (AUC), F1, and Jaccard

similarity [2, 25]. For the level of drug interactions, we introduce a weighted drug interaction score (wDDI) defined as follows:

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where M i is the set of recommended medications at the i th visit for a patient, | M i | is the cardinality of M i , w D jk is the severity of interaction between drug j and drug k obtained from the graph G D . The values of the various metrics are averaged over all the patients in the test set.

We split the datasets into training, validation and test sets in the ratio 4:1:1, and report the performance on the test set. Appendix C provides details of the training and hyperparameter settings. Sensitivity experiments and additional comparative and ablation studies can be found in Appendix D.

4.1 Comparative Analysis

We compare the performance REFINE with baseline methods such as Logistic Regression (LR) [13], Random Forest (RF) [4], BART [6] LEAP [27], DMNC [12], GameNet [16], CompNet [21], PREMIER [2], MICRON [24], SafeDrug [25], COGNet [22], and MoleRec [26]. We perform 10 rounds of bootstrapping sampling and report the mean and standard deviation.

As can be seen from Table 2, REFINE demonstrates superior performance compared to the baseline methods, with statistically significant differences (p-values < 0.05) based on a one-way ANOVA test [8]. The improved performance of REFINE can be attributed to its ability to account for medication dosage and lab test response trends across patient visits. Further, REFINE also outperforms the baselines in terms of wDDI, and is even lower than the wDDI present in MIMIC-IV (0.0448) and PRIVATE (0.1159) datasets. This suggests that REFINE can effectively reduce the potential risk of adverse drug interactions, which is a critical aspect of ensuring patient safety in clinical practice.

Table 3 shows the performance on a subset of patient instances where at least one change of medication occurs between consecutive visits. As more than 90% of the instances in MIMIC-IV already involve medication changes, we consider the performance reported in Table 2 to be representative of such

cases. Therefore, we focus our analysis on the PRIV ATE dataset, where around 50% of the instances involve medication changes between visits. We observe a drop in performance for all methods. This is expected as a change in treatment regime often implies a different patient response, making accurate predictions more challenging. Nevertheless, REFINE still outperforms the baseline methods in this challenging scenario. This indicates that even when faced with more complex situations involving medication changes, REFINE's integrated approach provides valuable insights and maintains a higher level of performance compared to other methods.

We also analyze the average number of recommended medications, average number of extra medications recommended, and average number of missed medications per visit for REFINE and the best performing baselines, as shown in Table 4. The average number of medications in the ground truth for the test set is 8.72 and 4.18 for MIMIC-IV and PRIVATE dataset respectively. We see that COGNet, PREMIER, and MoleRec generally recommend more medications whereas SafeDrug, MICRON tend to recommend fewer medications; whereas the average number of medications recommended by REFINE is closely aligned with the ground truth for both datasets. We find that COGNet has the highest average of extra medications in the recommended set. In contrast, REFINE recommends relatively lower extra medications. This can be attributed to the effective learning of patient condition from lab test and medication dosage trends. For the average number of missed medications, SafeDrug has the worst performance. On the contrary, REFINE misses the least number of medications from the ground truth set. This indicates that not weighing the benefits and potential interactions of drug pairs together may lead the system to omit medications necessary for managing a patient's condition.

4.2 Ablation Study

We conduct an ablation study to evaluate the effect of trend information and drug interaction severity on the performance of REFINE. We have four variants: (a) REFINE w/o med trend excludes variance and slope of the medication dosages, (b) REFINE w/o lab trend omits the variance and slope of the lab test responses, (c) REFINE w/o any trends does not incorporate any trend information, and (d) REFINE w/o severity does not incorporate severity-based drug interaction.

Table 5 shows the results. We observe that REFINE w/o any trend has the lowest accuracy, indicating that trend information is crucial for making accurate fine-grained medication recommendations. There is a significant drop in performance for REFINE w/o severity , highlighting the importance of considering drug interaction severity when recommending medications.

* indicates that the result is statistically significant when compared to the second best with p-value < 0.05.

5 Case Studies

Finally, we present case studies to highlight REFINE's ability to provide accurate medication recommendations which ultimately contributes to better patient care. Figure 4(a) shows a sample patient from the PRIVATE dataset who has been diagnosed with Type 2 Diabetes Mellitus and Hyperlipidemia, and the HbA1c ranges from 7.4 to 9.8. In the second visit, we observe that a diabetes medication Glipizide is added to the Metformin treatment to manage the increased HbA1c value. REFINE successfully recommends the addition of Glipizide, while SafeDrug and COGNet do not. This can be attributed to REFINE's ability to learn lab test trends and incorporate balanced drug interaction information. REFINE identifies the increase in HbA1c value for the current visit as the influencing factor for Glipizide, which is consistent with clinical knowledge. Further, the decrease in HbA1c in the third visit indicates that certain medication combinations with some risk of interaction may indeed be necessary to effectively manage a patient's condition.

Figure 4(b) shows another patient from the PRIVATE dataset who has been diagnosed with Diabetes Mellitus, Hypertension, and Hyperlipidemia in visit 1, followed by chronic kidney disease in visit 2. We see that the medication Simvastatin is replaced by Atorvastatin in the second visit as it is preferred over other hyperlipidemia medications for patients with chronic kidney disease 2 . REFINE closely follows the prescribed medication set and accurately recommends the medication change, indicating REFINE's ability to model co-morbidities. In contrast, SafeDrug and COGNet are unable to effectively capture this aspect.

Figure 4(c) shows a patient from the MIMIC-IV dataset who has been diagnosed with multiple diseases in visit 1, and is diagnosed with nontoxic multinodular goiter, and nausea with vomiting in visit 2. We observe that REFINE is able to recommend medications that matches the prescribed medications more closely compared to COGNet and SafeDrug. A closer inspection reveals that SafeDrug tends to under-recommend to avoid the interacting medication pairs, whereas COGNet tends to over-recommend many drugs outside the prescribed set.

6 Conclusion and Discussion

We have described a unique fine-grained medication recommender system that offers personalized medication suggestions while considering the severity of potential drug interactions and explicitly modeling the trends in medication dosage titrations and lab test responses over visits. To the best of our knowledge, REFINE is the only system that factors in these trends while also assessing the varying severity of drug interactions. Experiments on real-world datasets and case studies show the effectiveness of REFINE in providing personalized, accurate, and safe medication recommendation.

The current system can be extended to include additional rich data from EHR such as clinician notes to further refine the system's recommendations. While we have shown that the severity of drug interactions is a crucial factor, the dosage strength of the drugs could also have a significant impact. A lower dosage of an interacting drug pair may sometimes be clinically acceptable and beneficial. Future work could explore how dosage strength influences the acceptability of interacting drug pairs.

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Yutao Zhang, Robert Chen, Jie Tang, Walter F Stewart, and Jimeng Sun. Leap: learning to prescribe effective and safe treatment combinations for multimorbidity. In SIGKDD , pages 1315-1324. ACM, 2017.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this paper, the authors present a deep learning framework that utilizes patients' electronic health records and drug information to recommend safe and accurate medication combinations based on their symptoms and examination results. The proposed method is evaluated on two real-world datasets, MIMIC-IV and PRIVATE, and is compared with existing baselines. The results show that the proposed framework outperforms the baselines.\n\nSTRENGTHS:\nThe topic is pioneering and meaningful. Learning to recommend safe and accurate drug combinations is an important component of intelligent healthcare, and it makes a significant contribution to the development of human healthcare.\n\nWEAKNESSES:\n- The presentation of the paper is poor, and the equations cause confusion for the reader. Additionally, the figures presented in the article are of low quality and are not vector graphics. \n- The paper lacks necessary indicators and adequate comparisons with baselines, which undermines the overall validity of the experiment and the claimed contributions.\n\nQUESTIONS:\n**If the authors can provide satisfactory answers to my questions, I would consider revising my score.**\n\n- (Major) The proposed method lacks evaluation in terms of \"Jaccard\", which is a metric used in many of the baselines such as SafeDrug (https://arxiv.org/abs/2105.02711) and GameNet (https://ojs.aaai.org/index.php/AAAI/article/view/3905). Evaluating the distances between the ground truth medication combination and the predicted one is vital, as many combinations of drugs require their presence to exert a synergistic effect.\n- (Major) I find the validation to be insufficient. There should be comparisons to more state-of-the-art baselines such as MoleRec (https://dl.acm.org/doi/abs/10.1145/3543507.3583872) and 4SDrug (https://dl.acm.org/doi/abs/10.1145/3534678.3539089).\n- (Major) The author declares that the suggested system is more potent than the baselines. Multiple baselines, such as SafeDrug (https://arxiv.org/abs/2105.02711)  and GameNet (https://ojs.aaai.org/index.php/AAAI/article/view/3905), utilize Recurrent Neural Networks (RNNs) to extract features from Electronic Health Records (EHRs) for patient representation, whereas the proposed system employs transformers. However, no experiment validates that the performance boost is due to the proposed system and not only a mere replacement of RNNs with transformers. To ensure a just and unbiased comparison, the author should compare the RNN version of the proposed system to demonstrate that the performance improvement is genuinely caused by the proposed system instead of transformers.\n- (Major) MIMIC-IV benchmark AUC seems much lower than typical methods would produce, is it some mistake?  Reference: https://arxiv.org/abs/2305.12788. To my best knowledge, there does not seem to be any issue of out-of-distribution with the dataset. Comparing the result in the article to the reference, the reported performance of the baselines is noticeably lower by approximately **ten percentage** points. Despite the possibility of differences in train-valid-test dataset split, this discrepancy is still unusual.\n- (Major) The author claimed that the balanced DDI Loss is powerful, there are no experiments demonstrating that it outperforms the DDI loss used in SafeDrug (https://arxiv.org/abs/2105.02711). Based on my understanding, the **REFINE w/o severity** in the ablation study removes the severity information from both the loss and the graph neural networks\n\n- What's the meaning of GATv2 in Equation (4)? If it's a graph neural network, how can a GNN takes only one features as input instead of a graph; If it's not, what's the relation between it and the GATv2. Still how can the same linear transformation be applied to two vectors with different dimensions as it's shown in Equation (4) and Equation (5)? And does the two layers of GAT share the same parameters? It's claimed in the article that $\\delta_{jk}$ is calculated via the average of $\\delta_{jk}^b$. \n- The figures should be presented in vector graphics. The information in Figure (4) should be presented in form of table instead of figures.\n- Why the calculation of patient representation is obtained via $q_T=h_T^d+h_T^r+h_{T-1}^s$ instead of $q_T=h_T^d+h_T^r+h_{T}^s$？\n\nIf the author can respond satisfactorily to my questions, I am willing to adjust my score.\n\nLIMITATIONS:\nThe article is poorly written and the experiment is not sufficient to support the contribution claimed in the article.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2023-06-19"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new medication recommendation model that learns patient representations from diagnoses, lab tests, and medications. The new aspect of the paper is that it considers different severities of drug-drug interactions and can prevent overly conservative drug prescriptions.\n\nSTRENGTHS:\n1. The biggest strength of this paper is that it utilizes the DDInter databases into the drug recommendation tasks. As mentioned in the paper, the DDInter database has broken down the drug drug interactions into different severity levels. Previous DDI-aware drug recommendation models, such as SafeDrug, only considers the binary DDI existence and can provide overly conservative prescriptions. By considering the severity level, the proposed model can improve the performance by including some drug pairs that are beneficial and have low co-prescription severity.\n\n2. The paper is well written and the experiments are extensive. Figures in the paper are very informative.\n\nWEAKNESSES:\n1. The contribution of the paper is below the NeurIPS acceptance bar.\n2. Some statements are not well supported or overclaimed.\n    - In the conclusion, the paper states that the proposed model can offer personalized medication suggestions. It is not discussed in the main paper.\n    - In the conclusion, the paper states that REFINE is the only system that factors in the medication, lab test trends. This is not true. Many other models, such as GAMENet, also model the progression of medications, diagnoses, etc.\n3. Some details in the paper is missing.\n    - what is $\\mu_i^t$ in equation (2)\n    - how does equation (8) work? $q_T$ is a vector and $Z\\times \\lambda$ is a matrix.\n    - should equation (9) be $\\beta\\times xxx + (1-\\beta)\\times xxx$. Use the plus sign?\n    - what is the hinge loss?\n    - why choose 0.5 as the medication recommendation threshold? It seems that each binary classification is highly imbalanced, and thus the learned logits are more skewed towards 0. Maybe a threshold of 0.3 would be a better choice.\n4. Two datasets have a very small medication space (one has 735 medications, and another has 50). Are they individual drugs? As far as the reviewer knows, MIMIC-IV can have more than 10,000+ drug IDs. If the paper claims on individual drug recommendations (as stated in the intro), then this part needs explanation.\n\nQUESTIONS:\nsee the above.\n\nLIMITATIONS:\nNA", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-02"}

comments: SUMMARY:
This paper presents a medication recommendation system with 2 components: a GAT based medication interaction and a ViT-based patient embedding. The results on MIMIC and PRIVATE shows its superior performance.

STRENGTHS:
1: I think this paper is well-presented. The structure is very clear and the writing is polished. 
2: The results are generalizable -- consistent performance can be observed from two datasets. 
3: The experiments are comprehensive. The qualitative study is also interesting.

WEAKNESSES:
1: Reproducibility: The codes/notebooks are not shared either through supplementary materials or anonymous github. The paper does not provide much detail about the implementation. 
2: The choice of baselines: For EHR dataset, it is well-known that tree-based methods (like Gradient Boosting Tree, Random Forest, etc) perform the best. There are also some causal inference tree models like Causal Forest and BART. It is strange to me that the authors include LR but neglect all tree models. 
3: Give that the proposed work is a concatenation of ViT and GAT, the level of novelty is questionable.

QUESTIONS:
1: I really think you need to share the Jupyter Notebook (or codes) in whatever way to convince readers that the work is reproducible. It is very hard to implement simply based on the model description. 
2: How does the small improvement in AUC translate to clinical impact? You need to sell it to the readers. 
3: I encourage the authors to add some visualization figures. For example, how does the patient embedding look like, or how are the drug interations distributed?

LIMITATIONS:
N/A
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2023-07-05

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis manuscript proposes a new system for personalized medication recommendation.  The method relies on the history of patient visits: diagnoses, lab responses and medication dosages across time. For obtaining safe recommendations, it uses severity level information of drug interactions. An important aspect of the model is the extraction of trend information, measuring how much medication dosages and lab responses change across patient visits. The approach relies on deep learning for obtaining drugs’ and patients’ representations that are then combined. More specifically, it uses transformers for learning a patient’s representation, and graph attention networks for learning drug interaction representations. \n\nOne of the main contributions of the paper is modelling drug interaction severity, and providing fine-grained personalized recommendations, that is, personalized recommendations at the drug level. It combines existing approaches and adapts graph attention networks for weighted graphs containing drug interaction severity information. In addition, the authors designed a balanced drug interaction loss function that weighs the benefits of a pair of drugs against the severity of any potential drug interaction.\n\nSTRENGTHS:\n- The problem is difficult and relevant \n\n- The proposed approach is sound \n\n- The proposed method has a better performance than state-of-the art methods \n\n- The authors claim that REFINE is the first personalized medication recommendation systems to make predictions at the drug level \n\n- They show the importance of including the drug interaction severity information \n\n- The authors adapted attention weights for their problem \n\n- They designed a balanced drug interaction loss function that weighs the benefits of a pair of drugs against the severity of any potential drug interaction \n\n- The writing is clear.\n\nWEAKNESSES:\n**Major** \n\n- The paper did not provide the code used in the experiments. Notice, however, that the authors mentioned in the neurips form that they would make it available “soon”. \n\n- There are some aspects of the model that I did not fully understand. There are details missing about the dataset and how the information is represented. I will explain my concerns in the Questions section. \n\n- The paper relies on a private dataset containing patient data. I think it is important to indicate whether all ethics concerns were addressed.\n\n**Minor**\n\n- I suggest some improvements in the mathematical notation. In 3.3, node and node’s representation of a drug $i$ are denoted in the same way ($n_i$). Could the authors use different notation and clarify whether $n_i$ is a node embedding in Eq 4? \n\n- The authors use set notation {$1, N_m$} when they refer to intervals. For example, in lines 170, and 179, do the authors mean $[1, N_m]$ or {$1, 2, …, N_m$} instead of {$1, N_m$}? \n\n- Reference 4 refers to an arXiv version of the paper. The paper was published in ICLR 2022. I recommend also checking if references 22 and 23 were published \n\n- The venue where reference 5 was published is missing\n\nQUESTIONS:\n1. Previous approaches argue that they do class-level predictions for making the data compatible with MIMIC. Reference 16 uses the same version of the database as in this paper (MIMIC-IV). I am not used to this dataset. Could the authors explain why did they manage to make drug-level predictions using MIMIC database whereas the other methods did not? Are the NDC codes available for most of the drugs? \n\n2. For each patient visit, the model receives as input a multi-hot encoding vector of diagnoses, lab test response vector and medication dosage vector. Could the authors answer the questions below? \n\n    - (2.a) I am assuming that patients may have different types of exams and take different medications. I am also assuming that all patients share the same transformers. How is the input represented so that it is comparable across different patients? Could the authors clarify this in the paper?  \n\n    - (2.b) Also, could the authors clarify what they mean by “for the lab test response, only the first entry of  a lab test for a given visit is considered” in Section F Data Pre-Processing and Dataset Distribution, in Appendix. Is only the first exam result considered for a visit? In the main paper, the authors mention that each visit has a list o lab responses, so I was confused when I read the appendix. What is each entry of the a lab test response? Do the entries have a particular order? I recommend adding more details about how lab responses are pre-processed and represented in the model. \n\n3. The input of GATv2 corresponds to initial node embeddings. How are they initialized? If they are random, what are the range of values and distribution used?  \n\n4. One of the datasets used by the model is private. I noticed in Table 1 that it is very different from the public dataset. Could the authors explain why? Are the public and private datasets focused on different types of patients/diseases? If the private dataset refers to a particular group of patient or type of disease, I would recommend the authors to include this information in the paper. \n\n5. In the comparative analysis, Section 4.1, the authors mention that their method have a lower wDDI than in the original datasets (MIMIC and PRIVATE). I was wondering if it is necessarily a good thing. Could it mean that the predictions are too conservative and avoiding drug combinations that could still be important for the patient?\n\nLIMITATIONS:\nThe authors addressed some limitations of their work.\n\nAnother aspect that could be addressed is how safe it is to apply the proposed system in a real world scenario. Would a doctor trust the recommendations made by the system? What would be the main impacts of using this system? In which scenarios do the authors think it would be more useful?", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2023-07-06"}