Temporal Conformance Analysis and Explanation on Comorbid Patients

Luca Piovesan, Paolo Terenziani and Daniele Theseider Dupr

DISIT, Computer Science Institute, Universit

a del Piemonte Orientale, Italy

Keywords:

Computer-interpretable Clinical Guidelines, Comorbidities, Conformance Analysis, Answer Set Program-

ming.

Abstract:

The treatment of comorbid patients is one of the main challenges of modern health care, and many Medical In-

formatics approaches have been devoted to it in the last years. In this paper, we propose the ﬁrst approach in the

literature that analyses the conformance of execution traces with multiple Computer-Interpretable Guidelines

(CIGs), as needed in the treatment of comorbid patients. This is a fundamental task, to support physicians

in an a-posteriori analysis of the treatments that have been provided. Notably, the conformance problem is

very complex in this context, since CIGs may have negative interactions, so that in speciﬁc circumstances

full conformance to individual CIGs may be dangerous for patients. We thus complement our conformance

analysis with an explanation approach, aimed at justifying deviations in case they can be explained in terms of

interaction management, e.g., some possible undesired interaction has been avoided. Our approach is based

on Answer Set Programming, and, to face realistic problems, devotes speciﬁc attention to the temporal dimen-

sion.

1 INTRODUCTION

Clinical practice guidelines are one of the major tools

that has been introduced to grant both the quality and

the standardization of healthcare services, on the ba-

sis of evidence-based recommendations. The adop-

tion of computerized approaches to acquire, repre-

sent, execute and reason with Computer-Interpretable

Guidelines (CIGs henceforth) can provide crucial ad-

ditional advantages. Therefore, in the last twenty

years, many different approaches and projects have

been developed to manage CIGs (consider, e.g., the

book (Ten Teije et al., 2008) and the survey (Pe-

leg, 2013)). By deﬁnition, clinical guidelines address

speciﬁc clinical circumstances (i.e., speciﬁc patholo-

gies). However, individual patients may be affected

by more than one pathology (comorbid patients). The

treatment of such patients is one of the main chal-

lenges for modern health care, also due to the aging of

population, and the increase of chronic pathologies.

In fact, in comorbid patients the treatments of sin-

gle pathologies may interact with each other, and the

approach of proposing an ad-hoc “combined” treat-

ment to cope with each possible comorbidity does

not scale up: “Developing Clinical Practice Guide-

lines that explicitly address all potential comorbid

diseases is not only difﬁcult, but also impractical, and

there is a need for formal methods that would allow

combining several disease-speciﬁc clinical practice

guidelines in order to customize them to a patient”

(Michalowski et al., 2013). Thus, new methodologies

are required to study the interactions between treat-

ments, and to combine treatments: “This sets up the

urgent need of developing ways of merging multiple

single-disease interventions to provide professionals’

assistance to comorbid patients” (Ria

no and Collado,

2013). In the last years, several computer-based ap-

proaches have started to face this problem, aiming at

providing physicians with different forms of support

for managing multiple CIGs and their interactions.

As part of the previous work in this area, we

devised several methodologies to support physicians

in the management of comorbid patients: physician-

driven navigation of CIGs at different levels of ab-

straction, to focus on the parts that are relevant

for potential interactions (Piovesan et al., 2015);

knowledge-based detection of interactions between

actions (Anselma et al., 2017); mixed-initiative man-

agement of detected interactions (Piovesan and Teren-

ziani, 2015); merging the CIGs into a unique treat-

ment for a given patient (Piovesan and Terenziani,

2016).

In this paper, we ground on the above men-

tioned previous work and on an approach to check a-

posteriori conformance of the treatment of a patient

with respect to one clinical guideline (Spiotta et al.,

Piovesan, L., Terenziani, P. and Dupré, D.

Temporal Conformance Analysis and Explanation on Comorbid Patients.

DOI: 10.5220/0006535400170026

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF, pages 17-26

ISBN: 978-989-758-281-3

2015; Spiotta et al., 2017), to face the comorbidity

problem from a new perspective, that, to the best of

our knowledge, has not been considered yet. We ex-

plore the interplay between CIGs from the viewpoint

of a posteriori conformance analysis (Groot et al.,

2009), intended as the adherence of an observed CG

execution trace to the CIGs executed on a (comor-

bid) patient. Our goal is not to provide an evalua-

tion of whether the treatment was appropriate or not;

rather, we identify actions that have been executed or

could have been executed, following the individual

CIGs, and may interact, according to the approach in

(Anselma et al., 2017). We then allow for interpreting

the actual trace according to possible ways for manag-

ing interactions (deﬁned in (Piovesan and Terenziani,

2015)). Therefore, the trace may deviate from a strict

application of the individual CIGs under execution.

Notably, we also identify and point out cases where

the execution log is conformant with all the CIGs, but

some interaction has not been avoided: in such cases,

the adherence to the CIGs might not have been the

best option. Reasoning on interactions in a-posteriori

conformance analysis may complement reasoning on

the same knowledge to support CIG execution: for

example, it may be used (Quaglini, 2008) at discharge

time to support documenting the patient care process,

while, at execution time, physicians may not have

time to interact with a support tool.

Signiﬁcant conclusions can be drawn from this

conformance analysis if the trace is reasonably cor-

rect and complete as regards patient data and exe-

cuted actions. Missing events in a trace, or missing

data in the description of an event (or even the in-

correct recording of events), can indeed be hypothe-

sized to have occurred or to hold, in order to make

conformance analysis more ﬂexible (Chesani et al.,

2016); however, a high number of incorrect or miss-

ing events in the trace with respect to actual events,

in combination with several ways (studied in (Spiotta

et al., 2015; Spiotta et al., 2017) and in the present

paper) for explaining discrepancies between CIG and

trace, would make the space of possible explanations

quite large. In this paper we concentrate on explain-

ing discrepancies in terms of management of possible

interactions; discrepancies that cannot be explained

in this way are considered cases of non-conformance,

which may be due to incompleteness or incorrectness

of the trace, even though, in general, there is no way

for distinguishing. On the other hand, it is not realistic

to assume that at execution time explicit information

is recorded on the fact that the actions that have actu-

ally been executed deviate from the recommendations

of an individual CIG: detecting this is part of confor-

mance analysis.

To make our approach more precise in the analy-

sis of non-conformances due to possible interactions,

we take into account the temporal dimension. Indeed,

patient data hold at speciﬁc (intervals of) time; the

timing of actions should respect temporal constraints

in the CIGs, and interactions occur (or do not occur)

in time. This makes conformance analysis more com-

plex but richer: for instance, the CIG constraints may

be violated in order to temporally avoid undesired in-

teractions. Temporal analysis requires that at least im-

precise temporal information on actions is provided;

imprecision could lead an interaction, and then an ex-

planation, to be considered possible (due to potential

overlap of effects in time) in cases where more precise

temporal information would not support that possibil-

ity.

In this paper, we propose a general methodology

to cope with the above issues, that is mapped to An-

swer Set Programming (ASP), which, as shown in

(Spiotta et al., 2015; Spiotta et al., 2017) for the

case of a single CIG, is quite useful to analyse con-

formance, since, on the one hand, it supports the

non-monotonic forms of reasoning naturally used by

physicians in this context and, on the other hand, it

naturally supports the search for alternative explana-

tions.

In the following, we ﬁrst describe (Section 2) the

state of the art in AI support for comorbidities man-

agement and in conformance veriﬁcation for CIGs.

Then, in Section 3, we present more in detail the

data/knowledge sources used in our analysis. Sec-

tions 4 and 5 are the core of the paper: we ﬁrst

introduce (Section 4) our general methodology to

perform conformance analysis for concurrently exe-

cuted CIGs, then (Section 5) we describe in detail the

modalities for interaction management (taken from

medical literature) that are considered in our approach

to explain deviations from a strict application of indi-

vidual CIGs. In Section 6 we demonstrate the poten-

tial of our approach on a relatively simple - but ex-

plicative - example. Finally, in Section 7, we summa-

rize the contributions of our approach and point out

possible future work.

2 BACKGROUND

Until now, the two main tasks that we homogeneously

deal with in our approach have been only pursued

in isolation by the approaches in the literature: (1)

there are approaches to conformance analysis for

CIGs, which only consider one CIG; (2) there are

approaches to manage multiple CIGs and their inter-

actions, to cope with comorbid patients, but none of

HEALTHINF 2018 - 11th International Conference on Health Informatics

such approaches face the conformance problem. In

the following, we separately consider the state of the

art on the two issues, only focusing on the work that

is more closely related to ours.

In the last years, several AI approaches have been

developed for supporting the treatment of comorbid

patients (see the survey (Fraccaro et al., 2015)). For

the detection of interactions between CIGs, the most

closely related approach is the one in (Zamborlini

et al., 2014; Zamborlini et al., 2017). It provides a

CIG-independent conceptual model for medical ac-

tions and reasoning forms operating on it. Moreover,

in such a work general rules are proposed in order to

identify different types of interactions on the basis of

such a knowledge.

Several approaches have been devoted to the gen-

eration of integrated CIGs. Some of them are not

“conservative”: adopting different techniques, they

use the input CIGs as a starting point to build a

mostly new CIG which has no undesired interac-

tions; this is the case for the work in (S

anchez-

Garz

on et al., 2013), which uses an agent-based ap-

proach and hierarchical planning. Other approaches,

including the one in GLARE (Piovesan and Teren-

ziani, 2016), adopt more conservative techniques:

since CIGs are evidence-based, physicians rely on

them, so that interactions are managed with the min-

imum possible deviations from the original CIGs.

Within such approaches, the one in (Wilk et al.,

2013), uses constraint logic programming to identify

and address adverse interactions, while (Wilk et al.,

2017) is based on ﬁrst-order logic and extends the

above work for dealing with more than two CIGs;

(Ria

no and Collado, 2013) proposes a model-based

approach for the combination of CIGs; (Jafarpour and

Abidi, 2013) a semantic-web framework and (Zhang

and Zhang, 2014; Merhej et al., 2016) ASP-based ap-

proaches.

A limited number of approaches have dealt with

verifying conformance of a trace of actions with rec-

ommendations in a CIG. In (Groot et al., 2009), dif-

ferences between actual actions and CG prescriptions

are detected and analyzed, e.g., by comparing, for

non-compliant actions, actual ﬁndings with ﬁndings

that support the action according to the CIG. (Bot-

trighi et al., 2011) focuses on the interaction be-

tween clinical guidelines (CGs) and the basic medi-

cal knowledge (BMK) in the light of the conformance

problem. The work in (Spiotta et al., 2015; Spiotta

et al., 2017) also focused on the interaction between

BMK and a CIG, using ASP, aiming at providing a

justiﬁcation for non-conformances.

3 PRELIMINARIES

At least four different types of data/knowledge

sources should be considered to analyze compliance:

patient data, traces of execution, CIG models, and

general knowledge about action effects, intentions

and interactions between such elements (henceforth,

called ontological knowledge).

By patient data we mean patients’ ﬁndings, i.e.,

data which are usually collected in patients’ electronic

health records (EHR). In particular, as discussed in

the introduction, we intend that available data repre-

sent all known information that is relevant for treating

the patient, and that such pieces of information are

temporally tagged (possibly with imprecision). Also,

the available execution trace (trace for short) is con-

sidered as all that is known on the clinical actions

that have been executed on the patient. The occur-

rence of actions is temporally tagged with its start and

end time. We also assume that, in the case of deci-

sions, the log explicitly indicates the alternative that

has been chosen.

As concerns the CIG model, our approach is not

biased towards any speciﬁc CIG formalism; however,

we will use the GLARE formalism (Terenziani et al.,

2001) as a concrete example, due to its speciﬁc atten-

tion to the temporal aspects. Indeed, we simply con-

sider the possibility of distinguishing between atomic

and composite actions, and of specifying (therapeu-

tic and diagnostic) decisions. CIGs specify the con-

trol ﬂow of actions and include temporal constraints

between them. Additionally, actions may have pre-

conditions, and temporal constraints between the time

when preconditions hold and the time when the re-

lated action must be executed can be speciﬁed. Ac-

tions are considered for execution as follows:

• When the control ﬂow indicates that an action a

will have to be executed (in a time window depen-

dent on the temporal constraints in the CIG), we

say that a becomes scheduled. This means that,

in case we are considering a sequence of actions

followed by a decision, all the actions in the se-

quence (and the decision) are scheduled, while t

he actions following the decision are not, since

they belong to alternative paths, and, at this point

of the analysis, the physician could not know a-

priori which alternative she will take.

• When an action is reached by the control ﬂow,

i.e., the previous action ends, it becomes candi-

date and its execution proceeds according with the

execution model described in (Spiotta et al., 2015;

Spiotta et al., 2017): a candidate action could be-

come active or discarded; if active, it could either

be completed or aborted. Here, we just remark

Temporal Conformance Analysis and Explanation on Comorbid Patients

that an action should start (become active) at a

time such that all preconditions, with their tem-

poral constraints, enable the action, if such a time

exists.

Ontological Knowledge. Possible interactions

between CIGs, and between CIG recommendations

and the patient status, have to be identiﬁed. To do

so, we have to explicitly consider also the effects and

intentions of actions, and the time window in which

such elements can occur. Additionally, we must rely

on a knowledge base that models the possible interac-

tions between action effects and intentions, which is

CIG and patient independent. To address such a need,

we take advantage of the temporal ontology provided

in (Anselma et al., 2017) to devise a decision support

system helping physicians in the treatment of comor-

bid patients.

4 A GENERAL APPROACH TO

CONFORMANCE ANALYSIS

AND EXPLANATION

A high-level view of our general methodology is

graphically shown in 1. For the sake of simplicity,

we assume the execution of two CIGs and binary in-

teractions (i.e., between pairs of actions), which is

the case explicitly considered in (Piovesan and Teren-

ziani, 2015). Our approach can be easily general-

ized to multiple CIGs as long as we only consider at

each time the interaction of two of them, while (Pi-

ovesan and Terenziani, 2015), and then the approach

in this paper, could be generalized to non-binary in-

teractions.

We perform conformance analysis for the times

when some change occurred, either in the state of ac-

tions (e.g., an action becomes candidate, or a candi-

date action becomes active) or in the state of the pa-

tient (e.g., a new value for a ﬁnding is detected). At

each such time (indicated as Reference Time - RT -

henceforth), we consider as input of our analysis:

1. The set of all actions that are scheduled, candi-

date or active, each one with its “execution win-

dow” (i.e., the range of time within which the ac-

tion must be started and/or completed, given the

constraints in the respective CIG);

2. The set of all the possible effects of such ac-

tions, each one with its “existence window” (i.e.,

the range of time in which the effect may start

and end, given knowledge in the ontology of

(Anselma et al., 2017));

3. The set of past actions with effects whose “exis-

tence window” includes RT;

4. The status of the patient at RT;

5. The trace of execution.

The sets (1) and (3) are the relevant actions for RT.

Considering (1), (2), and (3), and ontological knowl-

edge modeling possible interactions (Anselma et al.,

2017) we detect whether interactions are possible be-

tween an action in (1) from one CIG and an action in

(1) or (3) from the other CIG; i.e., at least one action

should not have started or not be completed, so that

some modiﬁcation can be applied in order to manage

interactions. In practice, for a given pair of actions,

it is enough to perform the detection at the earliest

RT for which they are both relevant. The interaction

detection follows the general methodology devised in

(Anselma et al., 2017) and takes into account the di-

rect and indirect effects of the actions and whether

some of such effects interact and may overlap in time.

We consider two cases:

• No interaction is possible; in such a case, no devi-

ation from the CIGs can be justiﬁed by the anal-

ysis of interactions. We check, slightly extend-

ing the methodology in (Spiotta et al., 2015; Spi-

otta et al., 2017), which copes with a single CIG,

whether the (proper part of the) execution trace

is conformant with the CIGs, and report, as non-

justiﬁed, any non-conformance.

• One interaction is possible (the case of multiple

interactions is signiﬁcantly more complex, and we

plan to address it as future work). In such a case,

deviations from the CIGs might be explained as a

way to manage the interaction (e.g., to avoid it).

In the previous work in this area, (Piovesan and

Terenziani, 2015) identiﬁed different modalities

used by physician to manage interactions. This

issue is described in more detail the next section.

5 EXPLAINING

NON-CONFORMANCE

Physicians adopt different methodologies to manage

interactions (e.g., avoid undesired interactions) be-

tween (the effects of) CIG actions. In (Piovesan and

Terenziani, 2015) a set of “modalities” to achieve

such a goal have been identiﬁed. Notably, such op-

tions are not mutually exclusive: indeed, in several

practical cases, many options are possible, and the

physicians have to choose between them. In the

following, we describe how we check whether one

of such modalities may be used to explain a non-

conformance to the original CIGs aimed at manag-

ing a possible interaction. The ﬁrst modalities aim at

avoiding an interaction.

HEALTHINF 2018 - 11th International Conference on Health Informatics

Figure 1: The possible interaction between two actions relevant for a reference time RT may be used to explain the rest of the

log.

Replanning. One of the interacting actions is sub-

stituted by a new plan (set of actions), achieving the

same goal, or a similar one, according to (Piovesan

and Terenziani, 2015), but avoiding the interaction.

Such an option explains cases of non-conformance

in which an action in a CIG is not executed (while

it should have been, given the conditions and con-

straints in the CIG), and one or more actions, not

present in the CIGs, are executed (they are present in

the trace). In order to justify such a non-conformance

with the application of the replanning modality, our

approach ﬁrst detects whether an interaction would

have occurred (following the CIG), and then uses on-

tological knowledge to check whether the actions in

the trace but not in the CIGs reach the same goals (in-

tentions) of the “substituted” action.

Temporal Avoidance. Interactions can be temporally

avoided. In order to do so, interacting actions can be

executed at times such that the interaction cannot ac-

tually occur (i.e., their effects do not overlap in time).

Such a modality can be used in order to explain cases

of non-conformance due to the violation of some of

the temporal constraints in one or more CIGs. To do

it, our approach checks whether, in case the CIG con-

straints had been respected, an interaction would have

possibly occurred, while such interaction was not pos-

sible given the execution times in the trace.

Medical knowledge indicates that not all unde-

sired interactions strictly need to be avoided. In some

cases, CIGs can be adjusted to manage the situations

in which the interactions arise. We support three main

management options to this purpose: dosage adjust-

ment (for drug interactions), effect monitoring, and

interaction mitigation.

Dosage Adjustment. Interactions can be mitigated

through a variation of dosage with respect to the ones

recommended in the CIGs. Such a pattern can be eas-

ily identiﬁed (by comparing the dosage in the trace

with the one in the CIG) and explained (by identify-

ing the potential interaction, and using the ontology

to check whether the sign of the dosage variation is

the proper one for mitigating the interaction).

Effect Monitoring. In some cases, monitoring the

effects of the interaction is enough. In particular, if

an interaction causes a change of some parameters of

the patient, they have to be monitored and evaluated

by the physicians during the span of time in which

the interaction occurs. Obviously, if a serious risk is

detected, other management options can be ﬁnally ap-

plied. The effect monitoring modality explains traces

in which (i) interacting actions present in the CIGs are

indeed executed, but (ii) they are followed by a mon-

itoring action (not present in the original CIGs) and

a decision action (not present in the original CIG) to

evaluate the state of the patient and decide whether

to continue the current therapy or not. In the latter

case, the trace must contain another management or

the CIG must be suspended.

Interaction Mitigation. Some interactions cause un-

desired but tolerable side effects. In such cases, a new

action (or set of actions) that mitigates such effects

can be added to the interacting CIGs. Of course, the

new action is a deviation with respect to the two CIGs.

To explain such a deviation as an application of the

interaction mitigation modality, the additional action

must mitigate the effects of an occurring interaction.

Not all interactions between CIGs are negative or

undesired. This is the case when two actions in the

two CIGs pursue the same or similar goals. In such

a case, intention alignment can be applied by physi-

cians.

Intention Alignment. In the case of intention align-

ment, the physician may want to “merge” two actions

of two different CIGs into a single one, executing it at

a time which respects the temporal constraints of both

CIGs, or to substitute them with a new action, which

Temporal Conformance Analysis and Explanation on Comorbid Patients

pursues the same (or similar) goals of the two actions.

This modality can be used to explain the occurrence

in the trace of an action which is not present in any of

the two CIGs, instead of two CIG actions. The onto-

logical knowledge is used to check whether the new

action can achieve both the goals of the actions it sub-

stitutes.

Besides the above modalities, other modalities

have been identiﬁed in (Piovesan and Terenziani,

2015). Such modalities are practically useful, but de-

tecting them is less useful in an a-posteriori analysis.

Safe alternative and interaction alignment. Such

modalities consist in the avoidance (safe alternative)

or enforcement (interaction alignment) of an inter-

action through the choice of alternative paths in the

CIGs, when alternative therapeutic actions or paths of

actions are speciﬁed. Given the trace, we can just rec-

ognize the paths chosen by the physicians. In prin-

ciple, it could be hypothesized that, at the time of

some decision, other paths have been disregarded to

avoid or enforce interactions, but this is not useful to

explain any deviation from the CIGs (since, indeed,

paths in the CIGs have been carried on). Notably,

in our current approach, we do not even try to detect

whether some interaction (which has motivated some

deviation from the CIGs) could have been avoided

through the application of the safe alternative option

(i.e., by selecting, a-priori, different paths from the

CIGs). Such an analysis would be quite complex, and

scarcely useful in an a-posteriori conformance anal-

ysis, because, in the clinical practice, it is not realis-

tic to expect that physicians consider all the possible

future consequences of their therapeutic choices, ex-

ploring in the CIGs all the paths stemming from each

decision, and analysing all possible interactions be-

tween them; notably, such an analysis, though com-

plex, may be very useful in the context of decision

support.

We brieﬂy explain in the following how the anal-

ysis is performed in ASP. A choice rule:

1 {management(Cg1,A,Cg2,B, no_management);

management(Cg1,A,Cg2,B, replanning);

management(Cg1,A,Cg2,B, temporal_avoidance);

... } 1 :-

possiblyInteractScheduled(Cg1,A,Cg2,B,_,_,S),

relevant(Cg1,A,S), relevant(Cg2,B,S),

{ended(A,S1) : S1<=S; ended(B,S2) : S2<=S} 1.

where different management modalities are consid-

ered in the conclusion, allows the ASP solver to con-

sider a candidate answer set for each such modal-

ity. In general, the rule applies at a reference time

S if: the actions are scheduled, they are not both

completed, and they possibly interact, given the state

of the execution at S and the temporal constraints

in the CIGs. This is veriﬁed with the predicate

possiblyInteractScheduled, which is deﬁned based

on the knowledge about effects and actions exported

by the knowledge server in 1, and temporal rea-

soning implemented in ASP. From the knowledge

server we export the fact that the ﬁfth and sixth argu-

ments of possiblyInteractScheduled are effects of

actions A and B, and they may potentially interact; in

possiblyInteractScheduled we check that they may

actually overlap in time, considering temporal inde-

terminacy (at S) of the execution time of actions,

which have not necessarily started, and of effects with

respect to the actions.

For each of the modalities described earlier in this

section and considered for a-posteriori conformance

analysis, there are rules to deﬁne:

• necessary conditions for their applicability in a

speciﬁc log, in order to prune the candidate ex-

planations not supported by the log;

• how the CIG execution can be modiﬁed according

to such modality.

Among the resulting answer sets, as in (Spiotta

et al., 2015; Spiotta et al., 2017), optimization state-

ments are used to select the answer set(s) with a mini-

mum number of discrepancies with respect to the log.

In the following section, we provide more details of

our approach on speciﬁc examples.

6 A CONCRETE EXAMPLE

We consider the concurrent execution of a CIG for

peptic ulcer (PU) and a CIG for venous thromboem-

bolism (VT). 2 shows the two simpliﬁed CIGs at a

high level of detail, using the GLARE representation.

In the CIGs, the action “Amoxicillin therapy” (AT),

belonging to PU, interacts with the action “Warfarin

therapy” (WT, belonging to VT), which has the inten-

tion of avoiding the development of clots. Such an

interaction is usually avoided in the medical practice,

since it increases the anticoagulant effect of warfarin,

raising the risk of bleedings. In 2 some temporal con-

straints are reported on delays between actions, and

on action duration.

We applied our approach to three different logs for

the two CIGs above. First, we describe a log in which

no management has been applied, then we consider

a log in which warfarin has been replaced with hep-

arin, and ﬁnally we consider a situation in which the

beginning of the warfarin therapy has been postponed

until the end of the amoxicillin one. 3 represents the

HEALTHINF 2018 - 11th International Conference on Health Informatics

Figure 2: CIGs for peptic ulcer (PU) and venous thromboembolism (VT). Circles are atomic actions, hexagonal nodes are

composite actions, diamond nodes are decisions.

three execution logs: the ﬁrst row shows the log of PU

(common to the three executions), while the rows 3-

5 represents the different executions of VT. The sec-

ond row represents the timeline, and an arrow from

an action in a log to the timeline indicates that such

an action has been executed (or started/ended) in that

particular timepoint (e.g., action PUst has been exe-

cuted at day 0). For durative actions, we indicate with

Act s and Act e the starting and ending points of the

action Act.

In all the examples, in the execution of the VT

CIG, the anticoagulant therapy is selected (IntD); at

the time of IntD, WT becomes scheduled, and its in-

teraction is detected with AT, which is being executed

(i.e., it is active).

Henceforth, we focus only on three options:

no management, replanning and temporal avoidance.

The most relevant rules for such options are described

below. The following rule recognizes scenarios in

which an interaction was possible and no manage-

ment for it has been carried on.

info(possibly_interacting_actions_executed,A,B)

possiblyInteract(Cg1,A,Cg2,B,_,_),

started(A,_),started(B,_),

{management(Cg1,A,Cg2,B,M): M<>no_management}0.

The ﬁrst three conditions in the premise re-

quire that two actions A and B, which possibly in-

teract considering temporal constraints, have started.

The predicate possiblyInteract is analogous to

possiblyInteractScheduled, except that it uses the

actual execution time of the actions.

The following set of rules are relative to the re-

planning modality.

1: 1{substitute(Cg,A,C):hasEffect(C,E,_,_,_,_),

causes(E,I),C<>A}1 :-

management(Cg,A,_,_, replanning),

aimsTo(Cg,A,I,I_s,I_e).

2: :- substitute(Cg,A,C), started(A,_).

3: :- substitute(Cg,A,C),

hasEffect(C,E,E_si,E_sd,E_ed,E_ei),

started(C,T_s), aimsTo(Cg,A,I,I_s,I_e),

causes(E,I), ended(C,T_e),

1{T_s+E_sd>I_s ; T_e+E_ed<I_e}.

4: block(A,S) :-

substitute(_,A,_), relevantS(_,A,S).

5: succ(Cg,C,Anext) :-

substitute(Cg,A,C), succ(Cg,A,Anext).

6: succ(Cg,Apred,C) :-

substitute(Cg,A,C), succ(Cg,Apred,A).

The predicates hasEffect(Act,E,E si,E sd,E ed,

E ei), causes(A,B), aimsTo(Cg,A,I,I s,I e) are ex-

ported from the knowledge server and model, respec-

tively, the facts that an action Act has a particular ef-

fect E, starting between E si and E sd time units after

E, and ending between E ed and E ei time units after

E; that the effect/intention A causes B; that the action

A in the CIG Cg has the intention I that must occur

between I s and I e.

Basically, the ﬁrst rule creates a candidate answer

set for each possible action C having an effect achiev-

ing the intention of one of the interacting actions.

Then, candidate answer sets considering the replace-

ment of an action whose execution is reported in the

log are discarded (rule 2). On the other hand, rule 3

discards candidates in which the replacing action C is

executed in a time not compatible with the temporal

constraints (if any) of the intention I of the original

action A in the CIG. Finally, rules 4-6 replace the orig-

inal action A with C in the CIG.

The following rule is relative to temporal avoid-

ance.

:- management(Cg1,A,Cg2,B, temporal_avoidance),

1{not started(A,_); not started(B,_);

possiblyInteract(Cg1,A,Cg2,B,_,_)}.

The rule discards the temporal avoidance option

if, in the execution log, one of the two actions has not

been executed or they have been executed in times in

which the interaction is still temporally possible. Fur-

ther rules model the fact that, when justifying tem-

poral avoidance, deviations from the temporal con-

straints in the CIGs are allowed.

Temporal Conformance Analysis and Explanation on Comorbid Patients

Figure 3: Graphic representation of the three execution logs considered. Repeated arcs for the actions VTst and IntD are

drawn only for the ﬁrst execution of VT.

Example 1. Consider the log of VT in the third

row of 3, in which (i) WT starts during AT and

(ii) no deviations with respect the original CIGs

are present. Given (ii), replanning is ruled out,

while (i) discards temporal avoidance. The only re-

maining alternative is no management, which is re-

ported as output together with the warning that

a possible interaction could have occurred (i.e.,

info(possibly interacting actions executed,

wt,at)).

Example 2. The log in the fourth row of 3 shows

an example in which “Heparin therapy” (HT), which

is not present in the original CIGs, has been exe-

cuted, while WT is not present. This last fact dis-

cards the option of temporal avoidance, while the

no management option does not match the log and is

then discarded by minimization, since the log can be

explained by the replanning option. In fact, in the

ontological model of (Piovesan and Terenziani, 2015;

Anselma et al., 2017), HT has the effect of decreasing

the blood coagulation, which avoids the development

of clots (i.e., the intention of WT). The resulting an-

swer set contains the facts management(vt,wt,pu,at,

replanning), substitute(vt,wt,ht).

Example 3. In the log in the last row of 3, the

beginning of WT is delayed after the end of AT.

In our knowledge model, the two actions interact

due to the interaction between the “Anticoagulation”

effect of WT and the “Platelet aggregation Inhibi-

tion” effect of AT, where the latter ends with the

ending of AT. Thus, administering WT after AT e

is “safe” and the predicate possiblyInteract does

not hold considering this log scenario, so that tem-

poral avoidance is supported (and the resulting an-

swer set contains the fact management(vt,wt,pu,at,

temporal avoidance)). On the other hand, replanning

is not supported because WT and AT are present in

the log, while no management does not explain the

fact that the log violates the temporal constraint stat-

ing a delay of [0,3] days between actions IntD and

WT.

7 CONCLUSIONS

In this paper, we proposed the ﬁrst approach that ad-

dresses the problem of analysing the conformance of

execution traces with multiple CIGs, as needed in the

treatment of comorbid patients. The importance of

this task stems from the fact that full conformance to

individual CIGs may be dangerous for comorbid pa-

tients: deviations are sometimes necessary to avoid

undesired interactions between the CIGs. We thus

identify cases in which traces are conformant, but

undesirable interactions have not been avoided, and

complement our conformance analysis with an ex-

planation approach, aimed at justifying deviations in

case they had avoided some possible undesired inter-

action. Additionally, two other main features of our

approach, distinguishing it from the others in the lit-

erature, are:

(i) our attention to the temporal dimension and

(ii) our adoption of ASP to model and reason with

the problem.

While in this paper we exploit the model of CIG

action execution developed in (Spiotta et al., 2015;

Spiotta et al., 2017), the rest of the approach is dif-

ferent. First of all, in (Spiotta et al., 2015; Spi-

otta et al., 2017) only one CIG is considered, and

non-conformance can be explained on the basis of a

“general” basic medical knowledge, which may trig-

ger new actions in case problems not considered in

the original CIG arise in the patient. In this pa-

per two or more CIGs are considered, and more

speciﬁc rules (the modalities) are used to explain

non-conformances (in a context in which interactions

are possible). Additionally, a central point of the

current approach is the analysis of interactions be-

tween CIGs, while interactions (not even between the

CIG and the actions suggested by the basic medical

knowledge) were not taken into account in (Spiotta

et al., 2015; Spiotta et al., 2017). As a consequence,

the overall process of detecting and analysing non-

HEALTHINF 2018 - 11th International Conference on Health Informatics

conformances, as outlined in 1, is completely differ-

ent from the conformance analysis carried on in (Spi-

otta et al., 2015; Spiotta et al., 2017). Some of the

results of the analysis in (Spiotta et al., 2015; Spi-

otta et al., 2017) can be improved by the approach

in this paper, which only considers deviations that

can be justiﬁed by some form of interaction man-

agement. A more complete approach can however

be obtained also considering general medical knowl-

edge for dealing with relatively minor health prob-

lems whose treatment does not deserve developing a

proper CIG, but can nevertheless be modeled in the

same formal language as CIGs and possibly executed

concurrently with the CIG actions. For this reason

we plan, as a future work, to integrate the two ap-

proaches to provide a more comprehensive methodol-

ogy. Moreover, we aim at demonstrating the cover-

age of the conformance approach in dealing with ad-

ditional management modalities from (Piovesan and

Terenziani, 2015).

Finally, we plan to experiment the approach also

considering realistic traces containing also impre-

cise (and possibly missing) data, through a mixed-

initiative approach.

ACKNOWLEDGMENTS

This research is original and has a ﬁnancial support of

the Universit

a del Piemonte Orientale.

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