# Clinical Decision Transformer: Intended Treatment
Recommendation through Goal Prompting

Seunghyun Lee  Da Young Lee  Sujeong Im  Nan Hee Kim  Sung-Min Park

###### Abstract

With recent achievements in tasks requiring context awareness, foundation
models have been adopted to treat large-scale data from electronic health
record (EHR) systems. However, previous clinical recommender systems based on
foundation models have a limited purpose of imitating clinicians’ behavior and
do not directly consider a problem of missing values. In this paper, we
propose Clinical Decision Transformer (CDT), a recommender system that
generates a sequence of medications to reach a desired range of clinical
states given as goal prompts. For this, we conducted goal-conditioned
sequencing, which generated a subsequence of treatment history with prepended
future goal state, and trained the CDT to model sequential medications
required to reach that goal state. For contextual embedding over intra-
admission and inter-admissions, we adopted a GPT-based architecture with an
admission-wise attention mask and column embedding. In an experiment, we
extracted a diabetes dataset from an EHR system, which contained treatment
histories of 4788 patients. We observed that the CDT achieved the intended
treatment effect according to goal prompt ranges (e.g., NormalA1c, LowerA1c,
and HigherA1c), contrary to the case with behavior cloning. To the best of our
knowledge, this is the first study to explore clinical recommendations from
the perspective of goal prompting. See https://clinical-decision-
transformer.github.io for code and additional information.

Clinical Recommender System, Electronic Health Records, Foundation Model,
Causal Inference

##  1 Introduction

As electronic health record (EHR) systems have become common in large medical
organizations, it is not unusual for the system to maintain many years of
diagnoses and treatment histories from millions of patients as large-scale
computerized data. With the recent increase in the size of both models and
data in machine learning, an increasing number of studies are attempting to
utilize EHR data for various purposes, such as clinical insights (Acosta et
al., 2022), treatment effect estimation (Ling et al., 2022), patient
representation (Solares et al., 2021), and clinical decision-support (Sutton
et al., 2020).

In utilizing the clinical histories of individuals, contextual embedding poses
a significant challenge. EHR data frequently contain missing values in
laboratory test results for several reasons, such as prior knowledge about an
individual’s condition or relative clinical priority. Che et al. (2018) note
this omission as informative missingness based on the analysis that the
missing rate can be correlated to mortality and diagnosis categories. Thus,
missing data must be carefully considered without exclusion, and in this
process, it is necessary to infer the underlying information intimately
through the context surrounding the missing points. Moreover, as the
individual treatment and response history is highly heterogeneous across
patients, recent studies and position statements from medical associations
have demonstrated that there is no one-size-fits-all treatment (Subramanian &
Hirsch, 2014). For this reason, understanding heterogeneity in the clinical
context is crucial in realizing individual-oriented usage of recorded health
data from various patients. Therefore, contextual embedding for informative
missing values and data heterogeneity must be considered when designing a
machine-learning architecture to treat EHR data.

Figure 1: Overview structure of the Clinical Decision Transformer. A pair of
the desired future goal state and the clinical-treatment history consisting of
tabular states and medication sets are embedded separately and fed into the
GPT architecture. The unmeasured missing values in the states are represented
by trainable special tokens of $\left[\textrm{MISS}\right]^{k}$. By
manipulating the goal prompts, the model recommends intended medications
conditioned on the goal state and treatment history.

Driven by the recent maturation of the pretraining paradigm on broad data,
foundation models (Bommasani et al., 2021) have been demonstrated to offer
solutions to various tasks requiring high-level context awareness. GPT-3
(Brown et al., 2020) can generate output sentences through “in-context
learning” when example prompts consisting of input-output pairs are provided.
As a commercialized pair-programming agent, the fine-tuned GPT-3 in public
GitHub repositories, Copilot (Chen et al., 2021b), recommends the next code
lines according to the codes and comments typed prior. Furthermore, Decision
Transformer (Chen et al., 2021a) and Trajectory Transformer (Janner et al.,
2021), both based on the GPT architecture (Radford et al., 2018), verified
that the decision-making task could be cast as a sequence-modeling framework
by demonstrating long-term optimized action selections conditioned on past
interactions. Furthermore, from the same framework, Multi-Game Decision
Transformer (Lee et al., 2022) and Gato (Reed et al., 2022) revealed the
generalist agents that could adapt to distinct tasks based on the input
context.

Similarly, in the medical domain, emerging studies have applied foundation
models to address the contextualization problem for EHR data. BEHRT (Li et
al., 2020) and Med-BERT (Rasmy et al., 2021), rooted in the BERT (Devlin et
al., 2018) architecture, receive a series of classification codes for
diagnosis over several hospital visitations. Subsequently, these models were
employed to predict future disease diagnoses by conjugating pre-trained
embeddings of the input code histories. Unlike both models that predict
disease prognosis, G-BERT (Shang et al., 2019) predicts the next treatments
from the previous visitations. In this process, it generates ontology
embeddings of the diagnoses and treatments through a graph neural network,
after which BERT projects them to visitation embeddings for each hospital
admission. However, the above three studies do not utilize laboratory test
results from EHR systems; instead, they solely use diagnosis codes, which are
less informative in reflecting individual heterogeneity. Moreover, due to
their low necessity, several codes are not inputted into the EHR systems, even
though they are diagnosed or treated.

In this work, we designed Clinical Decision Transformer (CDT), a GPT-based
sequential medication recommender system that directly utilizes the laboratory
test data with missing values and heterogeneity. Notably, G-BERT also aims to
recommend medication; however, it is not structurally possible for G-BERT to
achieve the intended direction as it is based on behavior cloning of the
prescriptions in the EHR data. In contrast, the proposed CDT can recommend
intended medications according to a preassigned goal, which acts like a
“prompt” that is extensively used in natural language foundation models.

CDT generates medications conditioned on (1) a desired clinical goal state and
(2) a sequential history from individual EHR data. In our experiment, we
trained CDT on an extracted EHR dataset of patients with diabetes. Treatment
effects of the recommended medications depending on the assigned goals, were
evaluated based on the HbA1c (A1c) values. To estimate counterfactual clinical
outcomes, as we do not have access to ground-truth A1c, we trained the
additional evaluation model Counterfactual Recurrent Network (Bica et al.,
2020). To the best of our knowledge, this is the first study to propose a
clinical recommender system based on goal prompting with foundation models.
Our contributions are as follows:

  1. 1.

We designed the model to recommend medications conditioned on the clinical
goal prompt that we want patients to achieve as long-term disease management.

  2. 2.

We applied column embeddings, special tokens $\left[\textrm{MISS}\right]$, and
admission-wise attention masks to the GPT architecture to capture context from
tabular data with missing values.

  3. 3.

We evaluated the treatment effects of the recommended medications by training
an auxiliary model for counterfactual estimation.

##  2 Related Work

Decision-making via sequence modeling. Inspired by the remarkable achievements
of foundation models in natural language and computer-vision tasks, a concept
that considers decision-making as a sequence-modeling problem has emerged.
Decision Transformer (Chen et al., 2021a) maps received past trajectories to
subsequent action distribution, and it is possible to produce improved actions
by conditioning high returns in the input trajectory. Similarly, the
Trajectory Transformer (Janner et al., 2021) maps input sequences to joint
distributions of state, reward, and action. Notably, the Trajectory
Transformer demonstrates that the concept of decision-making as a sequence-
modeling problem is also applicable to goal-conditioned trajectory generation.
By simply prepending the last state to the initial token of the input
sequence, the model produces the most possible future trajectory to reach that
state. For our medication recommender system, we adopt two approaches:
modeling action distributions and generating sequences through goal
conditioning.

Multivariate-time series with missing values. A high proportion of missing
data is a frequent problem in clinical data utilization. A simple and
plausible approach is to omit the missing data (Schafer & Graham, 2002);
however, this approach can cause selection bias if the missing rate is high or
the missingness is associated with meaningful information. Another approach is
to conduct imputation, such as smoothing, spline, and interpolation. As the
imputation step separates from the training step for tasks (e.g., prediction),
models trained with imputed data do not intentionally consider the missing
context. Moreover, inefficiency and bias can arise from these two-step
processes if the data volume increases. In this study, for end-to-end
training, we utilized column embeddings and special tokens for the missing
values.

Counterfactual estimation using deep learning. In evaluating the treatment
effects using observed data, as an emulated randomized clinical trial,
counterfactual estimation is essential, and so is selection bias reduction.
Compared with traditional methods, deep learning-based approaches for balanced
representation have shown improved performance with the presence of time-
dependent confounding. For example, the Counterfactual Recurrent Network (Bica
et al., 2020) uses a gradient reversal layer in the training process, and the
Causal Transformer (Melnychuk et al., 2022) adopts the domain confusion loss
with three separate subnetworks. To estimate the counterfactual outcomes
according to the medications recommended by CDT, which are different from
factual prescriptions, we implemented the encoder architecture from the
Counterfactual Recurrent Networks.

##  3 Problem Formulation

An EHR dataset,
$\mathcal{D}=\left\\{\left(s^{(i)}_{t},a^{(i)}_{t},y^{(i)}_{t+1},\right)^{T^{(i)}}_{t=1}\right\\}^{N}_{i=1}$,
consists of $N$ patients’ clinical state and medication histories. Each
sequence $\left(i\right)$ represents consecutive health records of a patient
$\left(i\right)$ for $T^{\left(i\right)}$ hospital admissions. For each
admission, we observe clinical states $\mathbf{S}_{t}^{\left(i\right)}$, sets
of prescribable medications $\mathbf{A}_{t}^{\left(i\right)}$, and treatment
outcomes $\mathbf{Y}_{t+1}^{\left(i\right)}$. The clinical states correspond
to the tabular data:
$\mathbf{S}_{t}=\left\\{\mathbf{B}_{t}^{1},\cdots,\mathbf{B}_{t}^{J},\mathbf{L}_{t}^{1},\cdots,\mathbf{L}_{t}^{G}\right\\}$,
where $\mathbf{B}^{j}$ is the $j^{th}$ column value of the baseline covariates
(e.g., gender, age, and presence of diagnosis for several diseases), and
$\mathbf{L}^{g}$ is the $g^{th}$ column value of the laboratory test results
(e.g., blood glucose, blood pressure, or certain protein concentrations in the
blood), measured on each admission. For simplicity, let the notation of the
tabular data denote baseline columns and laboratory test columns in an
integrated manner
$\left\\{\mathbf{S}_{t}^{1},\cdots,\mathbf{S}_{t}^{K}\right\\}$, where
$K=J+G$. The medication set,
$\mathbf{A}_{t}\in\mathcal{P}\left(\left\\{m^{1},\
\cdots,m^{D}\right\\}\right)=\left\\{\emptyset,\ \left\\{m_{1}\right\\},\
\left\\{m_{1},\ m_{2}\right\\},\cdots\right\\}$, is a power set of D
prescribable medications (e.g., metformin, insulin, or sulfonylurea). The
treatment outcome, $\mathbf{Y}_{t}\in\mathbb{R}$ is the range of a specific
covariate (e.g., A1c) that clinicians focus on to manage. Here we assume that
the laboratory test results contain missing values in some columns, and so do
the treatment outcomes. Furthermore, in this work, the treatment outcome is
set as a clinical variable, which is better when the value is relatively low
(e.g., tumor volume or A1c excluding extremely low values). For a simplified
explanation, we omit the patient index $\left(i\right)$ unless required.

Let
${\mathbf{\bar{H}}}_{t}^{t^{\prime}}=\left\\{{\mathbf{\bar{S}}}_{t}^{t^{\prime}},{\mathbf{\bar{A}}}_{t}^{t^{\prime}}\right\\}$,
where
${\mathbf{\bar{S}}}_{t}^{t^{\prime}}=(\mathbf{S}_{t},\cdots,\mathbf{S}_{t^{\prime}})$
and
${\mathbf{\bar{A}}}_{t}^{t^{\prime}}=\left(\mathbf{A}_{t},\cdots,\mathbf{A}_{t^{\prime}}\right)$,
represent the partial history of patient admissions. For goal-conditioned
sequence modeling, each patient’s sequence is divided into partial sequences
at admission points, followed by the lowest treatment outcome up to the last
admission:

$\mathbf{Y}_{goal}=\min{({\mathbf{\bar{Y}}}_{t+1}^{T})},$ (1)

where ${\mathbf{\bar{Y}}}_{t+1}^{T}=(\mathbf{Y}_{t+1},\cdots,\mathbf{Y}_{T})$
and $t$ is an admission point with the previous lowest outcome. These lowest
outcomes are set to be goals for each partial history. Thus, the goal-
conditioned partial history for training is as follows:

$\left\\{\mathbf{Y}_{goal},\
{\mathbf{\bar{H}}}_{t}^{t^{\prime}}\right\\}=\left\\{\mathbf{Y}_{{t^{\prime}}+1},\
{\mathbf{\bar{S}}}_{t}^{t^{\prime}},{\mathbf{\bar{A}}}_{t}^{t^{\prime}}\right\\}.$
(2)

Figure 2: Goal-conditioned sequencing process. A clinical history consisting
of several times of hospital admissions is sliced at the lowest treatment
outcome (if lower outcome values are desired), and these lowest outcomes are
prepended to each subsequence.

Figure 2 illustrates this goal-conditioned sequencing process. As $y_{5}$ is
the lowest outcome up to the last admission, the whole sequence with seven
admissions is sliced at the fourth admission. Subsequently, the second lowest
outcome is $y_{7}$, and the whole sequence is divided into two subsequences,
${\bar{h}}_{1}^{4}$ and ${\bar{h}}_{5}^{6}$. For goal-conditioned sequencing,
the two lowest outcomes, represented as filled red circles, are respectively
prepended to the previous subsequences as the outcome goals. Note that the
missing outcomes notated with red crosses are not considered target outcomes.
For simplicity in the notation of the preprocessed subsequences, the first
admission point is rephrased as 1. In this study, we proposed an intended
clinical recommendation through goal prompting and intended to estimate
medications conditioned on the assigned outcome goal, previous sequence, and
current states:

$\mathbb{E}(\mathbf{A}_{t}|\mathbf{Y}_{goal},\
{\mathbf{\bar{H}}}_{1}^{t-1},\mathbf{S}_{t}).$ (3)

##  4 Clinical Decision Transformer

Herein, we describe the architecture of the proposed model through which it
generates clinical recommendations according to assigned treatment goals and
individual EHR histories with missing values. An overview of the architecture
is shown in Figure 1.

###  4.1 Model architecture

Similar to the Decision Transformer and the Trajectory Transformer, CDT uses
the causal GPT architecture for a decision-making problem. Notably, both
studies optimize the action by modulating the return-to-go to be high as a
reinforcement learning framework. However, since the missing rate of
covariates at each admission is generally high in the EHR data, it is not easy
to calculate the return-to-go for those sparse features as an optimization
objective. Instead, CDT uses a goal-conditioned sequence generation framework,
which requires only one goal prompt per sequence. Thus, it is rarely affected
by the missing rate of intermediate covariates. The goal-conditioned sequence
generation aims to model the conditional probability of the past sequence
given the desired future treatment outcome. In the CDT, we set the lowest
outcome up to the last admission as an outcome goal, $y_{goal}$, by the
sequencing process explained in Section 3. Thereafter, a preprocessed goal-
conditioned subsequence $\left(y_{goal},\bar{h}^{T}_{1}\right)$ with $T$
admissions is rephrased into a conditional input sequence and the medication
set to be estimated:

$\left\\{\left(y_{goal},\bar{h}^{t-1}_{1},s_{t},a_{t}\right)\right\\},\quad\textrm{for}\
t=1,\cdots,T.$ (4)

With this input-output pair, the objective we intend to maximize during
training is:

$\mathcal{L}\left(\theta\right)=\displaystyle\sum_{t=1}^{T}log{P_{\theta}\left(a_{t}\mid
y_{goal},\bar{h}^{t-1}_{1},s_{t}\right)},$ (5)

denoting the parameters of the CDT as $\theta$.

###  4.2 Column embedding

When dealing with sequential EHR data in practice, we need to consider time-
series tabular data with a high missing rate. We adopted column embedding
(Huang et al., 2020; Gorishniy et al., 2021) for the CDT to distinguish each
column from others. Let $c_{\phi_{k}}$ be the column identifier embedding and
$v_{\phi_{k,t}}$ be the value embedding of the $k^{th}$ column at admission t.
For baseline columns that were categorical features, entire classes were
encoded to integers and comprehensively embedded. For laboratory test columns
that were continuous features, values were normalized column-wise and embedded
through separated embedding layers. The missing occurrence only corresponded
to the laboratory tests. We set special tokens,
$\left[\textrm{MISS}\right]^{k}$, to represent the omissions in each
laboratory test column. These special tokens were independent of each other
and separately embedded as value embeddings, $v_{\phi_{k}}$, when the
histories contained missing values. We derived the column embeddings of
clinical states by concatenating the corresponding column identifier
embeddings and value embeddings:

$e_{\phi}\left(s^{k}_{t}\right)=\left[c_{\phi_{k}},v_{\phi_{k,t}}\right],$ (6)

where $e_{\phi}$ represents the conprehensive embedding layers for
$t=1,\cdots,T$ and $k=1,\cdots,K$.

The power set of prescribable medications was categorized, followed by the
embedding process. For the prepended outcome goals, as they were generally
numerical features, we discretized the range and applied categorized
embeddings. As in the Decision Transformer, CDT did not use per-token
positional embedding. Instead, we added identical admission embedding to the
clinical state tokens and the medication token in the same admission.

Figure 3: Admission-wise attention mask. Modified from the causal mask of the
GPT, attention field for all state tokens are extended until the last state
tokens of the same admission.

###  4.3 Admission-wise attention mask

For the contextual embedding, we designed an admission-wise attention mask,
different from the token-wise causal mask of the GPT. Figure 3 shows a
simplified illustration of our admission-wise attention mask, where the
clinical states have five columns. We extended the attention field for all
state tokens of the same admission. Through the forward pass of the GPT
layers, this attention field allows each clinical state token to be
contextualized over not only inter-admissions but also intra-admissions. In
experiments, we examined how the admission-wise attention contextualized
missing values and how effective it is in the overall architecture.

##  5 Experiments

###  5.1 Diabetes EHR dataset

We extracted cohort samples from an EHR system. We screened 4788 patients
diagnosed with diabetes who entered a hospital between January 2015 and July
2021. After the goal-conditioned sequencing, the total batch size of
subsequences was 9406, and the maximum admission number was 34 times. The
dataset had 14 and 18 columns for the baseline and laboratory tests,
respectively. Notably, the average missing rate of the laboratory test columns
was high at 44.39% (median: 22.37%). There were 12 prescribable medications,
and of its power sets, 441 subset classes were used for training, excluding
the combination where the case was not in the dataset. As A1c is the primary
measure for diabetes management, we used the A1c as an outcome for goal
prompting. We categorized the A1c range from 4.0% to below 18.0% with a 0.1%
increment. The missing rate of the A1c feature was 37.30%. See Appendix D for
details of the dataset.

###  5.2 Training

We implemented the CDT based on the GPT-2 of the Huggingface Transformer
library (Wolf et al., 2019). Conditioned on an input sequence consisting of a
prepended goal prompt, previous admission history, and current clinical
states, the CDT was trained to predict the current medication set prescribed
by clinicians with cross-entropy loss. To prevent the attention operation from
overflowing, we adopted QKNorm (Henry et al., 2020), L2 normalization of
queries and keys before the dot product. We split the subsequences into
training, validation, and testing datasets in an 8:1:1 ratio. The
hyperparameters of the CDT were optimized on the validation set, as explained
in Appendix B. In the experiments, we evaluated the treatment effect of the
medications recommended by the CDT according to goal prompts. For this
purpose, we adopted an encoder part of the Counterfactual Recurrent Network
(Bica et al., 2020) as an evaluation model. We trained the model to estimate
the counterfactual A1c of the one-step subsequent admission according to the
recommended medications that could differ from the factual prescriptions. The
details of the evaluation model, candidate architectures, and their training
process are provided in Appendix A. As a baseline for our prompting-based
intended recommendation, we trained a behavior-cloning (BC) model, which
imitates the factual prescriptions of clinicians when patients’ histories are
given. This BC model was built upon long short-term memory (LSTM) network and
trained with a supervised loss.

###  5.3 Recommendation through a goal prompting

We evaluated whether the effects of the medications recommended by the CDT
conditioned on the goal prompt were as intended. We manipulated the prepended
goals of each goal-conditioned subsequence in three prompt ranges: NormalA1c,
LowerA1c, and HigherA1c. The NormalA1c prompt represents the range of A1c that
is considered normal by the American Diabetes Association, which is
4.0%–-5.6%. The LowerA1c prompt represents an A1c range between 4.0% and below
the pre-existing goals in the subsequences. Similarly, the HigherA1c prompt
corresponds to an A1c range between above the pre-existing goal and below 18%.
According to the goal-conditioned sequencing process, these pre-existing goals
are actual treatment outcomes that are achieved in the future after factual
prescriptions. We sequentially conditioned all A1c tokens in each prompt range
and selected a medication set with the highest probability for a
recommendation. We estimated the A1c outcomes of the next admission using the
trained evaluation model for the selected medication sets and the factual
prescriptions. The A1c-lowering effect of recommendation was measured as a
difference in each estimated next A1c value by factual prescription and
recommended medication set. The results are shown in Figure 4 and Table 1.

Figure 4: Distributions of estimated A1c values and recommendation effects according to the goal prompt ranges. Table 1: Average recommendation effects according to the goal prompt ranges. Prompt Range | A1c(%) Difference
---|---
Behavior Cloning | −0.0181
NormalA1c (4% – 5.6%) | −0.0914
LowerA1c (4% – pre-exiting goal) | −0.0968
HigherA1c (pre-exiting goal – 18%) | +0.0727
|

Figure 4 (a) shows that the distribution of A1c by behavior cloning is similar
to the distribution by factual prescription. On the other hand, with the
NormalA1c prompt, the distribution of A1c exhibited a downshift to the normal
range as intended. In Figure 4 (b), with relative goal promptings, the A1c
distributions by the LowerA1c and HigherA1c prompts were downshifted and
upshifted, respectively, compared to the distribution by factual prescription.
To highlight the recommendation effect, Figure 4 (c) shows how much each
regime lowers A1c compared to the factual prescription.

Unlike the marginal improvement in behavior cloning, the prompt-based
medications caused a relatively significant treatment effect according to the
conditioned goal. From the comparison results, we can conclude that the
clinical recommendations through the goal prompting had the intended treatment
effects.

Note that these comparisons covered the treatment effects of the very next
admission, not the long-term goal achievement. Although we did not design the
evaluation model for an autoregressive inference purpose to avoid unreliable
bias from the high missing rate of the EHR data, autoregressive counterfactual
estimation with missing values could be explored in future work.

###  5.4 Recommendation analysis

We investigated the performance of the CDT and the BC model compared with the
factual prescription by clinicians. As the outputs of the CDT were classes of
mediation sets, we factorized the recommended set into medication elements and
evaluated the item-wise performance in the analysis. We selected two metrics
for practical clinical recommendation: (1) the recall for examining how the
model pre-assists clinicians before they make decisions on-site and (2) the
false-positive rate (FPR) for less interfering with clinicians’ decisions,
avoiding misuse of medications.

$Recall=\small{\frac{True\ Positive}{True\ Positive\ +\ False\ Negative}}$ (7)
$FPR=\small{\frac{False\ Positive}{False\ Positive\ +\ True\ Negative}}$ (8)

We compared the output according to the input admission length to analyze the
utilization of inter-admission and inter-admission information.

Figure 5: Recommendation performance curves of the CDT and behavior cloning (BC). Along the length of the input admission, recalls and false positive rates are compared as item-wise evaluation metrics. Table 2: Recommendation metrics according to the length of input admissions. |  | Input Admission Length
---|---|---
|  |  Cold Start |  2$\leq$ |  11$\leq$
Recall(%) |  CDT(ours) |  32.9 |  88.5 |  94.4
BC |  44.1 |  83.2 |  77.4
FPR(%) |  CDT(ours) |  4.47 |  1.65 |  0.97
BC |  6.29 |  2.59 |  3.97
|  |  |  |

Results. In this analysis, cold start corresponds to the case where the input
admission length is one, and it includes both the first and non-first hospital
visits according to the goal-conditioned sequencing process. Figure 5 shows
the recall and FPR curves according to each input length, where 11 is the top
15% percentile admission length. We observed that recall of the CDT was
approximately 33% at the cold start and drastically increased to 94% as the
input admission length increased. In contrast, the FPR was around 4% at the
cold start case and decreased to 1% when more input admissions were given,
finding that the CDT utilized information from previous admissions. Although
the recall of the BC was higher than that of the CDT at the cold start, its
FPR was also higher, meaning the BC model tended to abuse recommendations. For
inputs with longer lengths, the CDT recommended medications better than the
BC, considering higher recall and lower FPR. Table 2 describes the detailed
metrics. These results support the potential of the CDT as a clinical
recommender system, which should be conservative.

Attention patterns. We visualized representative cases of attention patterns
to closely analyze how the CDT recommended medications and contextually
embedded sequential EHR data. Figure 6 (a) shows the attention patterns of the
first layer observed in the cold start case. The leftmost red column
corresponds to a goal, and the other blue columns represent the clinical
states of the baseline and laboratory tests. The gray-colored rows are
attention weights regarding the special tokens $\left[\textrm{MISS}\right]$
for missing values. In the first heads, the CDT contextualized each state
feature primarily utilizing age, hypertension, and A1c. It is noteworthy that
age and A1c are major features that clinicians pay attention to when
prescribing medications to patients with diabetes in practice. We could
observe that missing values were contextualized with attention to other
diagnoses and laboratory test information from the same admission. In the
second head, attention weights were high on the goal and A1c tokens. When the
given length of previous admissions became longer, the attention patterns were
characterized by a combination of two different head patterns: state-focused
head and medication-focused head. Figure 6 (b) shows the multi-head attention
patterns of the same layer, where the yellow columns correspond to previous
medication sets. In the first head, the model highly attended to the states of
the immediate last admission, focusing on recent information rather than old
ones. In the second head, the model attempted to utilize the prescription
information of the last admission for recommending current medications.

Figure 6: Representative attention patterns observed in inference of the CDT.
(a) Cold start case; age, hypertension (HTN), and A1c were priorly utilized in
contextualizing each state, even in attention weights for missing values
colored in gray scale. (b) Long input history; two kinds of states-focused and
medication-focused attention patterns were observed. In both patterns, recent
information was more highly weighted than previous ones.

Contextual embedding. To explicitly evaluate whether the CDT conducts
contextual embedding, in Figure 7, we illustrate each admission point in the
embedding space before and after passing the GPT layers. Each color represents
a set of factual prescriptions. Input features and embedded output vectors
corresponding to each admission are projected onto two dimensions through the
t-SNE method. As the plots show, we observe that the output embeddings are
clustered with respect to prescriptions, while the input admissions are
entangled before contextualized.

Considering these recommendation analyses, we can comprehensively conclude
that the model tends to utilize recent inter-admission information to
contextualize the clinical states of the current admission, while when
previous information was insufficient, intra-admission contextualization
became more active.

Figure 7: T-SNE embeddings of the admissions before and after contextual
embedding.

###  5.5 Ablation study

The CDT architecture is characterized by column embedding and the admission-
wise attention mask. We ablated these architectures from CDT and compared
effects on recommendation performance. As a single statistic metric, we used
the Youden index (Youden, 1950), defined as a gap between recall and FPR and
widely used for evaluating the performance of clinical tests and models
(Schisterman et al., 2005; Powers, 2020).

$Youden\ index=Recall-FPR.$ (9)

Table 3 shows the relative changes in the metric compared with the result of
CDT. Without the column embedding, recommendation performance decreased in all
lengths of input admissions because of the difficulty in representing tabular
data. On the other hand, the admission-wise attention mask was effective only
in the cold start case. This result matches the attention patterns illustrated
in Figure 6 (b), where the attention weights for the previous admissions were
higher than those for the current admission.

Table 3: Ablation study for CDT architecture. Compared to the Youden index of CDT, differences were calculated. (CE: column embedding, AwMask: admission-wise attention mask) | Youden Index on Input Length
---|---
| Cold Start |  2$\leq$
CDT | 0.2849 | 0.8688
w/o CE | −0.0170 | −0.0132
w/o AwMask | −0.0051 | 0.000
w/o CE & AwMask | −0.0322 | -0.0111
|  |

##  6 Conclusion

In this paper, we propose a Clinical Decision Transformer, a clinical
recommender system that suggests medications in intended directions according
to conditioned goal prompts. We extracted a dataset of 4788 patients with
diabetes from the EHR systems and conducted goal-conditioned sequencing to
generate goal-sequence pairs. We trained a GPT-based foundation model to
recommend a sequence of medication sets required to reach a prompted A1c goal
when a treatment history and current states were given. It is necessary to
contextualize inter-admission and intra-admission information to fully utilize
large-scale EHR data characterized by heterogeneity and high missing rates.
For this purpose, we applied the admission-wise attention mask to the GPT
architecture for contextual embedding and adopted the column embedding to
enable the model to capture structural information of tabular data. In the
experiment, we observed that the CDT achieved the intended treatment effects
according to given goal prompt ranges, such as NormalA1c, LowerA1c, and
HigherA1c. Furthermore, the CDT recommended medications by embedding the
context through inter-admission and intra-admission with characteristic
attention patterns. With the proposed approach of considering clinical
recommender systems as a goal-prompting problem through foundation models, the
scalable utilization of large-scale and disease-wide computerized EHR systems
could be a socially meaningful future research direction.

## Fundings

This work was supported by the National Research Foundation of Korea (NRF)
grant funded by the Korea government (MSIT) (No. 2020R1A2C2005385), the Basic
Science Research Program through the NRF funded by the Ministry of Education
(No. 2020R1A6A1A03047902), the Bio & Medical Technology Development Program of
the NRF (NRF-2019M3E5D3073102 and NRF-2019R1H1A2039682), and a National IT
Industry Promotion Agency (NIPA) grant (No. S0252-21-1001, Development of AI
Precision Medical Solution (Doctor Answer 2.0).

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##  Appendix A Evaluation Model Selection

In order to select the best evaluation model for counterfactual estimation, we
conducted comparison experiments with three candidate model structures. For
counterfactual estimation with neural networks, we aimed at a backbone model
to output a balanced representation that is non-predictive to a factual
prescription, reducing selection bias. Two prediction heads following the
backbone model estimate prescription and A1c, respectively, from this balanced
representation. As the EHR dataset covered in this paper has a high missing
rate, we trained the three models to estimate an A1c value of the very next
admission rather than autoregressive further estimation to avoid unreliable
bias. The candidate models are as follows:

• Encoder part of Counterfactual Recurrent Network (CRN-E)

• Transformer with gradient reversal layer (T-GRL)

• Transformer with domain confusion loss (T-DC)

CRN-E :  Counterfactual Recurrent Network (Bica et al., 2020) is an LSTM-based
model that learns balanced representation via domain adaptation with Gradient
Reversal Layer (GRL) (Ganin et al., 2016). It consists of encoder and decoder
parts; the encoder that infers the very next outcome from a balanced
representation of previous history and the decoder that autoregressively
predicts future outcomes. As the first candidate, CRN-E is the encoder part of
the Counterfactual Recurrent Network.

Let $\Phi\left(\bar{h}_{t}\right)$ be the balanced representation given at
timestep ${t}$, ${G}_{Y}$ the prediction head for A1c outcomes according to
the balanced representation $\Phi\left(\bar{h}_{t}\right)$ and a given
prescription ${a}_{t}$, and ${G}_{A}$ be the prediction head for prescriptions
as a domain classifier. $\theta_{Y}$ and $\theta_{A}$ denote the trainable
parameters in ${G}_{Y}$ and ${G}_{A}$, and $\theta_{R}$ denotes the trainable
parameters in the LSTM backbone. For time step ${t}$, let
$\mathcal{L}_{t,y}\left(\theta_{Y},\theta_{R}\right)$ be a loss for the A1c
outcome ${y}_{(t+1)}$ and
$\mathcal{L}_{t,a}\left(\theta_{A},\theta_{R}\right)$ be a loss for medication
set ${a}_{t}$, which are defined as follows:

$\mathcal{L}_{t,y}\left(\theta_{Y},\theta_{R}\right)={||y_{t+1}-G_{y}\left(\Phi\left(\bar{h}_{t};\theta_{R}\right),a_{t};\theta_{Y}\right)||}^{2}$
and

$\mathcal{L}_{t,a}\left(\theta_{A},\theta_{R}\right)=-\displaystyle\sum_{j=1}^{d_{a}}\mathbb{I}_{\\{a_{t}=a_{j}\\}}log\left(G_{A}\left(\Phi\left(\bar{h}_{t};\theta_{R}\right);\theta_{A}\right)\right)$,

where $\mathbb{I}_{\\{.\\}}$ is the indicator function and $d_{a}$ is the
number of medication sets. With these losses, for the non-predictive
representation with respect to prescriptions, we trained all parameters of the
CRN-E in the direction of minimizing $\mathcal{L}_{t,y}$ and maximizing
$\mathcal{L}_{t,a}$ by using the GRL architecture. The overall loss is as
follows:

$\mathcal{L}_{t}\left(\theta_{Y},\theta_{A},\theta_{R}\right)=\mathcal{L}_{t,y}\left(\theta_{Y},\theta_{R}\right)-\alpha\mathcal{L}_{t,a}\left(\theta_{A},\theta_{R}\right)$

where the hyperparameter $\alpha$ represents the trade-off between the two
predictions. Embedding layers for input features with missing values had the
same structures as in the CDT.

T-GRL :  The second model is Transformer with GRL, which uses the same
representation balancing approach as the CRN-E except that a Transformer
network replaces the LSTM. A backbone model of the T-GRL is GPT, and specific
backbone architectures are similar to that of the CDT except for goal-related
parts.

T-DC :  T-DC also applies a domain adaptation approach to GPT similar to
T-GRL. Instead of GRL architecture, it uses domain confusion (DC) loss (Tzeng
et al., 2015) for balanced representation. The notations for losses are the
same as in CRN-E, but let $\theta_{R}$ be the trainable parameters in the GPT
backbone in T-DC. Assuming that the parameters of the $G_{A}$ are optimized to
$\hat{\theta}_{A}$, $\theta_{R}$ is updated to prescription invariant
direction with the DC loss, a cross-entropy of the uniform distribution over a
categorial output of the $G_{A}$:

$\mathcal{L}_{t,conf}\left(\theta_{R}\right)=-\displaystyle\sum_{j=1}^{d_{a}}\frac{1}{d_{a}}\log\left(G_{A}\left(\Phi\left(\bar{h}_{t};\theta_{R}\right);\hat{\theta}_{A}\right)\right)$

On the other hand, the $G_{A}$ is trained, assuming that the parameters of the
GPT backbone are optimized to $\hat{\theta}_{R}$. In practical implementation,
overall training of the T-DC is performed by iteratively minimizing the
following losses:

$\mathcal{L}_{t}\left(\theta_{Y},\theta_{R}\right)=\mathcal{L}_{t,y}\left(\theta_{Y},\theta_{R}\right)+\alpha\mathcal{L}_{t,conf}\left(\theta_{R}\right)$
and

$\mathcal{L}_{t,a}\left(\theta_{A}\right)=-\displaystyle\sum_{j=1}^{d_{a}}\mathbb{I}_{\\{a_{t}=a_{j}\\}}log\left(G_{A}\left(\Phi\left(\bar{h}_{t};\hat{\theta}_{R}\right);\theta_{A}\right)\right)$,

where $\alpha$ is a balancing hyperparameter. Table 4 compares the structure
of the three candidate models.

Table 4: Feature difference between three candidate models. Feature | CRN-E | T-GRL | T-DC
---|---|---|---
Backbone Structure | LSTM | Transformer(GPT) | Transformer(GPT)
Representation Balancing Method | GRL | GRL | DC
|  |  |

We conducted hyperparameter tuning with training and validation datasets for
all candidate models. The hyperparameters optimized for each model are shown
in Table 5. The hyphen symbol(-) means that the model does not use the
corresponding hyperparameter. The lambda rate is a hyperparameter for reversed
gradients during backpropagation.

Table 5: Hyperparameter of each evaluation model. Hyperparameters |  CRN-E |  T-GRL |  T-DC
---|---|---|---
Iteration of Hyperparameter Search |  50 |  50 |  50
Early Stop Patience |  30 |  50 |  50
Minibatch Size |  64 |  128 |  128
Number of Layers |  4 |  3 |  4
Number of Medication Layers |  2 |  1 |  3
Number of Attention Heads |  - |  1 |  4
Activation Function of GPT |  - |  ReLU |  ReLU
Learning Rate |  0.0001 |  0.0001 |  0.0001
Warmup Steps |  500 |  500 |  500
Lambda Rate |  10 |  10 |  -
Balancing Alpha |  0.2 |  0.05 |  0.1
Dimension for Column Embedding |  - |  64 |  32
Dimension for Value Embedding |  128 |  64 |  96
Gradient Normalization Clip |  1.0 |  1.0 |  0.5
Weight Decay |  0.01 |  0.0001 |  0.001
|  |  |

As we do not have access to the ground truth values for counterfactual
estimation, we compared the estimation performance based on mean square errors
on factual test data. The result of a comparison experiment with the tunned
models is shown in Table 6, and we selected CRN-E as an evaluation model for
our proposed CDT.

Table 6: Comparison of the three candidate models. Model |  CRN-E |  T-GRL |  T-DC
---|---|---|---
Mean Squared Error |  0.3767 |  0.5907 |  0.6442
|  |  |

##  Appendix B Hyperparameter Tuning for Clinical Decision Transformer

We have established a group of hyperparameter candidates referring to the
hyperparameters used by the Decision Transformer (Chen et al., 2021a). Our
hyperparameters for the CDT model are shown below in Table 7. The Youden
index(Youden, 1950) on the validation set was used as a criterion for
selecting the best hyperparameter set. In each iteration, the model was
trained using the AdamW (Loshchilov & Hutter, 2017) optimizer with early
stopping conditioned on the cross-entropy loss.

Table 7: Hyperparameters of the CDT model. Hyperparameters |  Value
---|---
Iteration of Hyperparameter Search |  50
Early Stop Patience |  100
Minibatch Size |  128
Number of Layers |  4
Number of Attention Heads |  2
Activation Function of GPT |  ReLU
Learning Rate |  0.0001
Warmup Steps |  1000
Dimension for Column Embedding |  16
Dimension for Value Embedding |  112
Gradient Normalization Clip |  0.25
Weight Decay |  0.0001
|

##  Appendix C Behavior Cloning Model

We built a behavior cloning model upon LSTM networks. As in the CDT training,
the behavior cloning model was also trained by using a cross-entropy loss over
factual medication sets prescribed by clinicians. Unlike the CDT, this model
aims to imitate the clinician’s behavior and is not allowed to manipulate
recommendations with any intention. Hyperparameters are shown in Table 8.

Table 8: Hyperparameters of the behavior cloning model. Hyperparameters |  Value
---|---
Iteration of Hyperparameter Search |  50
Early Stop Patience |  30
Minibatch Size |  64
Number of Layers |  3
Learning Rate |  0.0001
Dimension for Embedding |  128
Gradient Normalization Clip |  0.25
|

##  Appendix D Diabetes Dataset

This data extraction from an EHR system was approved by an official review
committee of the National Health Insurance Corporation of Korea and the
Institutional Review Board of the Korea University Ansan Hospital
(2021AS0264). We screened 4788 patients diagnosed with diabetes who were
admitted between January 2015 and July 2021. Detailed information is
summarized in Tables 9, 10, 11, 12, 13 and 14.

Table 9: Baseline characteristics of the extracted dataset. Features |  Value
---|---
Number of Patients |  4788
Shortest Admission Length |  1
Longest Admission Length |  41
Mean Admission Length |  13.64
Median Admission Length |  13.0
Number of Baseline Features |  14
Number of Laboratory Features |  18
Number of Prescribable Medications |  12
|
Table 10: Characteristics of the dataset for CDT after the goal-conditioned sequencing process. Features |  Value
---|---
Number of Subsequences |  9046
Shortest Admission Length |  1
Longest Admission Length |  34
Mean Admission Length |  5.12
Median Admission Length |  3.0
|
Table 11: Characteristics of the dataset for the evaluation model after removing non-containing A1c data. Features |  Value
---|---
Number of Sequences |  4091
Shortest Admission Length |  1
Longest Admission Length |  38
Mean Admission Length |  11.78
Median Admission Length |  12.0
|
Table 12: Prescribable medications. Features |  Type of Feature
---|---
Metformin |  Binary
Sulfonylurea |  Binary
Thiazolidinedione |  Binary
DPP-4 Inhibitor |  Binary
Insulin |  Binary
SGLT2 inhibitor |  Binary
Glinide |  Binary
$\alpha$-Glucosidase Inhibitor |  Binary
GLP-1 Receptor Agonist |  Binary
Hypertension Medication |  Binary
Hyperlipidemia Medication |  Binary
Diabetes Mellitus Neuropathy Medication |  Binary
|
Table 13: Characteristics of the baseline columns. Features |  Type of Feature
---|---
Sex |  Binary
Age |  Categorical(4–120)
Ischemic Heart Disease |  Binary
Congestive Heart Failure |  Binary
Arrhythmia |  Binary
Ischemic Stroke |  Binary
Hemorrhagic Stroke |  Binary
Diabetes Mellitus Retinopathy |  Binary
Kidney Transplant |  Binary
Hemodialysis |  Binary
Peritoneal Dialysis |  Binary
Hypertension |  Binary
Dyslipidemia |  Binary
Diabetes Mellitus Neuropathy |  Binary
|
Table 14: Characteristics of the laboratory test columns. Features |  Missing Rate (%)
---|---
Glucose |  2.70
Hemoglobin A1c |  37.30
Cholesterol |  7.22
High-Density Lipoprotein Cholesterol |  7.35
Low-Density Lipoprotein Cholesterol |  99.59
Triglyceride |  7.15
Aspartate aminotransferase |  20.18
Alanine aminotransferase |  20.18
Albumin |  22.83
Microalbumin |  70.71
Microalbumin (Urine, CSF) |  100.00
Microalbumin/Creatinine in Urine |  70.71
Microalbumin (RIA) |  99.26
Blood Urea Nitrogen |  21.92
Creatinine (Serum) |  21.63
Creatinine (Urine) |  70.71
Creatinine (BF, Urine) |  98.69
Bilirubin |  20.94
|