=Paper=
{{Paper
|id=Vol-3831/paper16
|storemode=property
|title=Towards Explainable General Medication Planning
|pdfUrl=https://ceur-ws.org/Vol-3831/paper16.pdf
|volume=Vol-3831
|authors=Lee-or Alon,Hana Weitman,Alexander Shleyfman,Gal A. Kaminka
|dblpUrl=https://dblp.org/rec/conf/explimed/AlonWSK24
}}
==Towards Explainable General Medication Planning==
https://ceur-ws.org/Vol-3831/paper16.pdf
.
Towards Explainable General Medication Planning⋆
Lee-or Alon* , Hana Weitman, Alexander Shleyfman and Gal A. Kaminka
Computer Science Department, Bar-Ilan University, Israel
Abstract
The ability to produce explanations for automated systems in healthcare domains is crucial for establishing
trust between users and the system. Despite the growing demand for explainable artificial intelligence
in medical domains, to the best of our knowledge, there are no existing works on explainability for
medication planning. In this work, we propose a visualization method for medication planning domains
to make the automatic planning process transparent to users, thereby fostering the desired trust.
Keywords
Medication Planning, Explainable AI Planning, Personalized Medicine
1. Introduction
Personalized medication planning is the process of generating a plan of drug administrations
that meets a given set of medical goals that are specific to the individual patient. The planning
process must take into account general health safety constraints, helpful or harmful interactions
between drugs, and individual physiological differences in responses to medications. The
resulting personalized medication plan defines what drugs are administered, when, and at what
dosage: too little is ineffective; too much is toxic.
Medication planning is a complex process, manually carried out by healthcare professionals.
Its complexity is often encountered in mitigating harmful drug interactions in patients with
multiple diseases [1], or in combination therapy, where multiple medications are used to syner-
gistically improve therapeutic effects while minimizing side effects [2, 3]. Indeed, a combination
of drugs can result in effects no drug can achieve alone [4].
To demonstrate the difficulty of solving medication planning problems, let us examine a
relatively simple instance. Suppose the system is to consider only two types of medicine, both
affecting a specific property of interest 𝑝. Assume that for both types, the time it takes the
medicine to clear the body is 24 hours. Let us also assume there are five different available
dosages for each medicine. The medical objective is to have property 𝑝 reach a level of at
least 51 in the spleen, but not exceed 53.8. While this problem seems small, solving it could be
challenging due to the many possible treatment plans. Assuming each medicine is allowed to
EXPLIMED - First Workshop on Explainable Artificial Intelligence for the medical domain - 19-20 October 2024, Santiago
de Compostela, Spain
*
Corresponding author.
$ alonlee1@biu.ac.il (L. Alon); weitman@cs.biu.ac.il (H. Weitman); alexash@biu.ac.il (A. Shleyfman);
galk@cs.biu.ac.il (G. A. Kaminka)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
be administered at most once, there are 24 · 24 administration times and 5 · 5 dosage options,
resulting in 14,400 combinations.
Not only are there numerous treatment combinations, but each administration’s effects are
also non-linear and multidimensional, as they simultaneously impact several organs and change
over time. Furthermore, because each patient may require a different treatment plan tailored to
their specific needs and conditions, a new plan must be created for each individual. All of these
characteristics make the problem even more challenging to compute manually.
To address the complexities of personalized medication planning, researchers have turned to
artificial intelligence (AI) planning techniques. AI planning is a form of sequential decision-
making over time. It aims to find an ordered set of actions that leads from an initial state to
a goal state. In medication planning, the AI planning process aims to automatically find an
ordered set of drug administrations that progresses from the current health condition of a
patient to the desired medical condition. It must not violate any medical safety constraints
along the way.
The wide variety of treatment combinations, as well as the non-linearity and multidimension-
ality of the treatment effects, make it challenging for users to comprehend the implications of a
given plan. Moreover, as medication plans vary between patients, explanations or clarifications
of each plan must be generated anew.
The complexity and sensitivity of medical treatment problems result in a high demand for
explainability. Since mistakes in the treatment plan can have major irreversible consequences,
including death, users are not willing to follow automatically generated treatment plans without
understanding the reasoning and potential implications. Indeed, the use of explainable artificial
intelligence planning (XAIP) [5] is important here. Its goal is to make the planners transparent
to users, thereby establishing users trust.
We propose a framework for explainable general medication planning (XGMP). The approach
we suggest visualizes the personalized treatment plan’s effect on the patient’s body, thus
making the plan transparent and easy to understand, for healthcare professionals and even for
non-professional users. We evaluated this approach using the general medication planning
(GMP) representation by Alon et al., with medical data as reported in [6, 7]. To the best of our
knowledge, no steps were taken towards explainable medication planning.
2. Background
Explainability is an emerging and crucial field in artificial intelligence (AI). Its primary objective
is to make the automated decision-making processes of AI systems transparent and interpretable
to users by providing explanations that elucidate the underlying reasoning behind the system’s
outputs. These explanations aim to foster trust and confidence in AI systems, enabling users to
comprehend the rationale behind the decisions made by the system.
The demand for explainability becomes paramount in domains where human lives are at
stake, such as healthcare and the legal system. In the healthcare domains, the consequences of
erroneous decisions can be severe, e.g., misdiagnoses, and improper treatments. Consequently,
users in this field are more likely to demand comprehensive explanations for the AI system’s
outputs, as the implications of incorrect decisions can be far-reaching and potentially life-
altering.
Personalized medicine further increases the necessity for explainability. In personalized
medicine, there is no single protocol to treat a certain condition. The treatment is tailored to the
individual patient based on their background medical conditions, preferences, and factors such
as age. The variety of possible treatments under various conditions complicates the problem
and requires different explanations, as the treatment for one patient may not be relevant for
another, even if the same medical goal is required.
We begin with a short description of the medication planning process and basic terminology.
We then discuss the need for explainability in this domain.
2.1. Medication Planning
The general medication planning problem (GMP) is concerned with selecting drugs to be admin-
istered, as well as determining the dosage and schedule of the chosen medications [8, 9, 10]. It
is therefore, in some aspects at least, a generalization of dosing regimen planning [11], which
assumes drugs have already been selected, and deals with administration dosages and schedules.
Medication planning is also closely related to the process of planning treatments for patients
with multiple diseases, by merging available multiple single-disease clinical guidelines. The latter
process includes substituting drugs when adverse or redundant interactions occur, adjusting and
scheduling tests to monitor for such interactions, and other related tasks [12, 13, 14, 15, 16, 17, 18].
This process produces plans that span weeks or months, and involve selecting drugs from the
set recommended by guidelines. It may also recommend medical tests, and actions to take per
their results. In contrast, GMP is carried out from first principles, personalizing the dosage and
hourly medication schedules, using models of how medicines (drugs) spread through the body
and interact with it, and with each other (pharmacokinetic and pharmacodynamic models—see
below). However, it does not address testing, or chronic conditions.
Pharmacokinetics and Pharmacodynamics Medication planning is a model-based plan-
ning approach. It uses models that predict how drugs spread in the body (pharmacokinetic
models), and how they interact with in (pharmacodynamic models). These are explained below.
Once a drug is introduced into the body, it is generally absorbed, carried and circulating by
the bloodstream throughout the body. The drug reaches various biological sites (bio-sites) and
may accumulate for some time, before it is eventually cleared out of the body. The concentration
of a drug in various bio-sites, known as its biodistribution, undergoes changes over time, which
can be described by pharmacokinetic (PK) models of varying complexity. These models range
from simple 1-3 compartment exponential decay models [19, 20] to more advanced models
that account separately for multiple kinetic processes (see [21]). Alternatively, biodistribution
trajectories can also be represented by explicit curves [22, 6], obtained from clinical trials.
For example, in Figure 1, we see the biodistribution trajectories of a specific drug administered
to a mouse (nanoparticle #11, in [6]). Drug concentrations (percentage of initial dosage per
gram of tissue) were measured in four bio-sites (kidney, lung, spleen, liver), at several time points
(measured in hours since the administration at time 𝑡0 = 0). Such trajectories change between
medicines, but may also change between patients. The horizontal axis shows the time since
12 kidney spleen
lung liver
Dosage%/gm-tissue (%/gm) 10
8
6
4
2
0
1 12 24 48
time (h)
Figure 1: Biodistribution trajectories of nanoparticle #11 in mice (from [6]).
Figure 2: Illustration of the connection between pharmacokinetic models and pharmacodynamic
models. Scheme was taken from [23].
administration. The vertical axis shows the concentration per gram of tissue as a percentage of
the injection dosage. Each line shows the PK trajectory at a different bio-site.
When the drug reaches a target bio-site, it may affect the properties of that bio-site. These
effects can be characterized using pharmacodynamic (PD) models [23]. PD models describe the
relationship between the drug concentration at any given bio-site, or the body as a whole in
simple models, and the resulting therapeutic effect.
PK and PD models are combined to form PKPD models [23], which predict the expected
magnitude of drug therapeutic effect over time. Figure 2 illustrates the connection between PK
and PD models. The PK model yields the concentration of the administrated drug in the body
at a specific time post-administration. Subsequently, the PD model utilizes this data to calculate
the biochemical effect of that drug on the patient’s body. PKPD models (and their component
models) have been and continue to be an active area of research in medicine and pharmacology,
with entire journals devoted to their investigation.
Goals of Medication Planning Medication planning involves medical goals that are specified
in terms of properties of different bio-sites (or the body taken as a whole), taking into account
temporal pharmaceutical dynamics and kinematics. It combines information about the rate of
accumulation and clearance of drugs in different bio-sites (from PKPD models) with information
about toxicity and personal health constraints and patient activities to meet target levels of the
drug or its biological effects. The process then selects drugs, determines their dosage, and the
schedule of their administrations to a patient.
2.2. Explainable Medication Planning
Alon et al. [9, 10] presented the general personalized medication planning, and used a planning
representation using pddl+ [24] to plan using multiple drugs, affecting multiple bio-sites over
time (e.g., as in Fig. 1). This representation allows for an arbitrary number of medicines, each
may be administered repeatedly if needed. The interactions of the drugs are modeled, so that
the planner can avoid harmful interactions, and replace one drug with another (or with a
combination of drugs). This extends the work of Alaboud et al. [8], which introduced the use
of automated (AI) planning to address medication planning for maintenance goals, of a single
drug and its associated PK model. Unfortunately, these investigations has not addressed the
need for explaining the resulting plans.
There are various aspects of explainable artificial intelligence planning (XAIP), all of which
are missing in medication planning. One common XAIP technique is to intervene in the problem
representation, forcing the planner to execute the user’s suggestions [25, 26]. The user iteratively
asks questions, where each question yields a new plan. By comparing the original plan with
the new plan derived from the user questions, users infer the reasons that led to the original
plan. An XAIP investigation related to a medical domain is presented by Korikov et al. [27],
which considers the appointment scheduling problem. They use a counterfactual explanation
technique that explains to the user what should have been different in order to achieve the
user’s suggested outcome.
In this paper, we focus on visualization, as the basis for interaction with a user. In general,
visualization methods can also be utilized to explain the planner choices. Chakraborti et al. [28]
introduce a visualization of the top-𝑘 plans as a graph where nodes represent actions and edges
represent the transitions between actions. This visualization does not allow visualizing durative
actions (whose effects change over time), or actions taken simultaneously. Similarly, Kumar
et al. [29] present a visualization system for classical planning domains, i.e., all variables have
binary values. They allow for both changes in the domain (in action structures) and in the
problem (in initial state and goal states). As medication planning is not carried out in classical
planning domains (durative actions, simultaneous actions, constraints), it is not compatible
with with the proposed visualization method.
3. The General Medication Planning Representation
We will briefly describe the GMP pddl+ representation as was proposed by Alon et al. [9, 10].
A pddl+ planning problem [24] can be described by the following tuple:
⟨𝒱, 𝒮, 𝑠0 , 𝒞, 𝒢, 𝒜, ^ℰ , 𝒫
^ ⟩ where 𝒱 is a set of state variables either propositional or nu-
meric, 𝒮 is a set of states, where each state is a complete assignment of values to all variables
𝑣 ∈ 𝒱, 𝑠0 ∈ 𝒮 is an initial state, 𝒞 is a set of constraints on possible assignments of values, and
𝒢 is a goal description (a set of conditions over variables). 𝒜 is a set of instantaneous actions
that change the values of variables when selected by the agent, and ^ℰ , 𝒫 ^ sets of events and
processes (resp.) that change the values of variables instantaneously or overtime, outside of the
control of the agent. A plan is a timed sequence of (parallel) actions, which starts from the
initial state and reaches a goal state while not violating any constraint in 𝒞.
From a medical perspective, a patient’s body can be viewed as a set 𝐵 of bio-sites, such as
organs and blood. Basic pharmacological models often depict the entire body as a single bio-site
(|𝐵| = 1), but in more complex models multiple bio-sites are represented.
Their work allows for the representation of 𝐵 bio-sites, where each bio-site is represented as
a set of 𝑃 biochemical properties. Their values are numeric fluent indicating the concentration
levels or other measures of interest and generally vary between bio-sites.
Table 1 shows an example of 12 property variables, used in the experiments. The property
𝑚11 (first row) measures the concentration of nanoparticle #11 (Fig. 1) in six different bio-sites,
at a specific time (e.g., liver[𝑚11 ] = 2.97, and kidney[𝑚11 ] = 9.2). Data was taken from [6]. A
second property, measuring the mu-opioid receptor (MOR) activity, is shown in the second row.
Its values in this case are derived from PKPD model parameters reported elsewhere [7]. The
initial state 𝑠0 of a patient’s body may be represented by setting the values of properties, in
each bio-site, to current values. For properties measuring drug concentration, initial values in
all bio-sites are zero.
Organs 𝐵
Blood Heart Liver Spleen Lung Kidney
Properties 𝑃
𝑚11 1.6 0.73 2.97 2.34 1.81 9.2
MOR activity 26.4 20.28 30.003 28.79 27.27 33.55
Table 1
Illustration of the state of a patient’s body 24 hours post-administration. Columns represent bio-sites.
Rows represent property values.
The administration of dosage 𝑑 of a drug type 𝑚 at time 𝑡 is represented as a pddl action.
This representation allows for repetitive administrations of the same drug type as long as they
are not administered at the same time. Drugs from different types may be taken n parallel.
Each drug administration may affect several bio-sites and several properties simultaneously.
Note that several drug administrations may affect the same bio-site property simultaneously,
even if these administrations were not at the same time, since administrations have durative
effects.
The medicine effects over time are represented by pddl+ events. For every medicine 𝑚,
there is at least a single property 𝑚 in every bio-site 𝑏, i.e., 𝑏[𝑚] ∈ 𝒱, which represents the
concentration level of medicine 𝑚 in bio-site 𝑏. In this representation, the medicine levels are
estimated directly from biodistribution trajectories (e.g., Fig. 1) in the problem description in
pddl+.
As a drug is accumulated in a bio-site (measured by its concentration level), it causes changes
in other biochemical properties within the same bio-site. These changes can be predicted using
PD models. The combination of the PK and PD model types, known as a PKPD model, allows
for the estimation of how the accumulation and clearance of a drug change in biochemical
properties influence various bio-sites over time [19, 23, 30].
The PKPD drug effects are also represented using pddl+ events. This representation utilizes
the direct action (direct effect) model, a common PKPD model in medical literature [23, 30]. This
model describes the relationship between the time-dependent concentration, and the effects of
the drug, measured in relevant units varying between drugs.
Different drugs may affect the same property simultaneously. As these will be handled by
different events, their effects will increase the value of the property according to the PKPD effect
of the associated medicine type. This naturally follows the Loewe additive drug interaction
model [31, 23], whereby drugs can affect the same property, but at different “strength”. Contra-
indicated drugs (may not be taken together) are handled by constraints (see below).
Goals 𝒢 and Safety Constraints 𝒞 Given the definitions of states and actions above, it seems
a simple matter to define goal states in terms of target levels for properties of interest, at a
specific set of bio-sites (therapeutic sites). However, medically, the planner must also ensure
that the levels of all properties are maintained at safe levels, before the target levels are reached,
as well as after.
They use events to impose limits on the maximal and/or minimal values of a property at any
moment. These limits can come from medical defaults, or they may be personalized for specific
health conditions of a patient. For example, if a patient has diabetes, the glucose level must stay
below a given threshold ℎ at all times. Such a constraint on the property 𝑗 of bio-site 𝑏 can be
expressed as 𝑏[𝑗] ◁▷ ℎ, where ◁▷ ∈ {>, ≥, =, ≤, <}. Constraints can be also placed to prevent
interactions between drugs.
The goal description 𝒢 has two components in the pddl+ representation of GMP. The first
involves specifying target levels for properties in the set of therapeutic sites. These target
levels can be personalized and differ between patients. The second component ensures that
constraints are maintained after these target levels are achieved.
Once the goal conditions are first satisfied at time 𝑡𝑔 , safety constraints should be upheld not
only in the interval [0, 𝑡𝑔 ] but also in the extended interval [𝑡𝑔 , ∞), bearing in mind that action
effects have finite durations. Thus, a second subgoal introduced using pddl+ checks that all
administered medication had been eliminated from the patient’s body after the first component
has been achieved.
Personalization Patients, who seek treatment for the same goal, vary in their medical history
(e.g., background medical conditions leading to differences in safety constraints) and treatment
preferences (e.g., due to age, sex, levels of activity). Two patients with the same medical goal may
still require different treatment plans due to differences in background medical conditions, such
as diabetes, pregnancy, etc. Patient diversity may cause differences in their PKPD responses,
both in biodistribution trajectories, as well as 𝐸𝑚𝑎𝑥 and 𝐸𝐶50 parameters.
4. Explainable General Medication Planning
Using clinical data from mice and rats, we implemented medication planning problems in pddl+
as described. The PKPD models are taken from databases of possible nanoparticle-based drug
carriers [6] and pain relief drugs [7]. We use the ENHSP-20 numeric planner [32] to solve
medication problems (see [10] for more details).
4.1. Visualizing the Plan PKPD Effects
We propose visualizing the PK and PD effects to help users understand the treatment’s impact
on the patient body and easily compare between plans.
Consider a medication planning problem where the goal is to achieve a minimal value of 44
for mu-opioid receptor (MOR) activity in the heart and a maximal value of 46 (i.e., there is a
safety constraint of 46 for this property in the heart)1 . The planner suggested the following
plan (denoted as plan A; dosage is measured in units of nM (nano-molar)):
0: (administer_med m9 a1 d100)
0: —–waiting—- [2.0]
2.0: (administer_med m10 a1 d100)
2.0: —–waiting—- [28.0]
This plan suggests administering a dosage 100nM of medicine 𝑚9, waiting two hours and
then administering a dosage 100nM of medicine 𝑚10. Lastly, the planner waits another 26
hours until clearance occurs at time 28 from the beginning of the plan. While this very common
plan format is detailed enough to follow, the exact effect of the plan on the body remains
unclear. Understanding the pharmacokinetic (PK) and pharmacodynamic (PD) effects among
users will increase their trust in the system and the suggested treatment and making the process
transparent.
Figure 3 presents the visualization of the plan A (described above). The horizontal axis shows
the time (in hours) since the start of the plan (first administered drug). The vertical axis shows
MOR activity levels. The red line shows the PKPD effect of administering 100 nM of 𝑚9 alone
at time 0, while the blue line shows the PKPD effect of administering 100 nM of 𝑚10 alone two
hours after the plan start (i.e., had it been injected without 𝑚9 being present). The horizontal
line (light blue, a level of MOR activity of 46) describes the safety constraint. The total PKPD
effect of the two drugs is shown in purple. All of these are presented for the heart: the curves
would be different for other bio-sites.
The visualization also highlights limitations of the clinical data. The sudden drops to zero
towards the end of the plan span (i.e., 23–25 hours) signify that the different drugs have cleared
the bio-site and thus their effect is reduced. In reality, we expect such clearance to be more
gradual. However, the planning process is restricted to using the actual clinical data given in [6],
which provides measurements at a resolution of a few hours (i.e., approximately 3–10 data
points, depending on the drug and bio-site). The planner uses linear interpolation for points
in-between measured data. As the actual clearance time is not given, the planner extrapolates
1
MOR activity levels are associated with pain relief.
it from the last point given in the data, towards zero. The result is that the clearance time is
arbitrarily set as an hour following the last measured data point available from the database.
plan m10 m9
45
MOR activity 40
35
30
25
20
0 5 10 15 20 25
time (h)
Figure 3: Plan A. The PKPD effect (in the heart) of administering medicine 𝑚9 at time 0 with dosage
100nM (red) and medicine 𝑚10 with dosage 100nM at time 2 (blue) and in combination (purple).
4.2. Visualize Alternative Plans or Changes in Medical Conditions
The PK and PD effects of an alternative plan can be visualized side-by-side with the original
plan. This approach highlights the differences between the plans, allowing for a clear and
easy comparison of their respective effects. Such differences may be due to variantions in drug
administration timing or dosages, or due to differences in medical conditions or considerations.
Comparing plans with alternative schedules for the same drugs The visualization
can demonstrate differences in effects due to variations in administration timing and dosages.
Consider the plan effect described in Figure 3. One might ask: "What would be the effect of the
plan if both medicines were administered simultaneously?". The plan (denoted plan B) produced
by the planner in this case is two hours shorter than plan A:
0: (administer_med m9 a1 d100)
0: (administer_med m10 a1 d100)
0: —–waiting—- [26.0]
Figure 4 presents the PKPD effect of executing plan B. Contrasting it with plan A (Fig. 3), we
indeed see that while plan B is two hours shorter than plan A, it violates the medical safety
constraint.
plan m10 m9
45
40
MOR activity 35
30
25
20
0 5 10 15 20 25
time (h)
Figure 4: The effect of executing plan B (purple).
m5 m11
35
MOR activity
30
25
20
0 5 10 15 20 25 30 35 40 45
time (h)
Figure 5: The PKPD effect (in the heart) of administering 20nM of medicine 𝑚5 (orange) vs. the PKPD
effect in the heart of administering 40nM of medicine 𝑚11 (green). The horizontal cyan line shows the
safety constraint.
Comparing Plans with Alternative Medical Considerations Personalization of the medi-
cal safety constraints, or other medical considerations, may also alter the plan. It may help the
user visualize and contrast alternative treatment plans, resulting from such considerations.
Consider the medication planning problem of achieving MOR activity of 29 in the heart,
maintaining it lower than 37 in the same bio-site. The planner initially suggested administering
20nM of 𝑚5, which in our experiments is an opiod (specifically, Morphine). Due to its severe
potential side-effects (one of which is long-term formation of dependence), a medical professional
may ask for an alternative that excludes Morphine. When we pose this to the planner, it indeed
finds a plan using a different medicine (𝑚11) at a dosage of 40nM.
Figure 5 clearly shows the differences between the two suggested treatment plans. While
both plans achieve the medical goal without violating the safety constraint, the plan involving
medicine 𝑚5 has a shorter duration of 26 hours, compared to the plan using medicine 𝑚11 (50
hours). Additionally, the 𝑚5 plan has a lower maximal MOR activity in the heart compared to
the 𝑚11 plan. However, the 𝑚11 plan achieves the desired medical goal (MOR activity of 27)
more quickly.
5. Conclusion
We introduced a visualization method for the general medication planning (GMP), a relatively
new and underexplored area in personalized medical treatment planning. The experiments
conducted using real medical data showcased the effectiveness of this method in making unclear
treatment plans easily understandable, even for non-professional users.
While the visualization method offers simplicity and clarity, it currently is not able to present
other reason for using/not using certain medications, such as potential nausea, dizziness,
weakness, and resistance to the administered medicines. Modeling such effects remains a future
work.
Acknowledgements
Alexander Shleyfman’s work was partially supported by ISF grant #2443/23. G.K. thanks K. Ushi,
as always. Alon is grateful to the Azrieli Foundation for the award of an Azrieli Fellowship.
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