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https://ceur-ws.org/Vol-2237/medracer-abstract-1.pdf
Argumentation-based clinical decision support system in ROAD2H
Francesca Toni
Imperial College London
Abstract excluded treatments, if they are locally available. Further-
more, LMICs often suffer from limited resource availability,
The ROAD2H project aims to build a clinical decision sup- e.g. dialysis machines and oxygen machines.
port system integrating argumentation and optimisation tech- Given that there are so many factors contributing to the
niques to reconcile guidelines providing conflicting recom-
mendations for patients with comorbidities, and taking into
decision on treatments in the settings of interest, it is impor-
account national and regional specificities and constraints im- tant that any recommendation is explained to the (relevant)
posed by local health ensurance schemes. Here I provide a users, so as to allow users to interact with the recommenda-
high-level overview of the project. tion. For example, if the recommendation is that a particu-
lar patient uses a specific dialysis machine but the machine
ROAD2H1 (standing for Resource Optimisation, breaks down, it should be “easy” for the user (e.g. nurse or
Argumentation, Decision support and knowledge transfer clinician) to feedback into the system so as to have an up-
to create value via learning Health systems) is an inter- dated, reasoned recommendation (for this and possibly other
national project funded by EPSRC (the Engineering and patients).
Physical Sciences Research Council) in the UK, bringing Within ROAD2H we are addressing these challenges by
together computer scientists, health informaticians, clini- integrating argumentation, as understood in AI (e.g. see
cians, health economists, data scientists and policy makers (Simari and Rahwan 2009) for an overview) and mathemat-
to deliver integrated solutions for a class of decision support ical optimisation to identify candidate treatments that fol-
systems using standardised representations of clinical low guidelines, modulo resolution of conflicts between them
guidelines and of billing information to provide explained as necessitated for specific patients, and minimising out-of-
recommendations to users (notably clinicians and patients), pocket payments while maximising resource usage. On the
taking their preferences into account, while also being able argumentation front, we are using a mixture of abstract ar-
to accommodate user feedback and improve with use, in gumentation (Dung 1995) and assumption-based argumen-
the spirit of the Learning Health Systems vision (McGinnis tation (Dung, Kowalski, and Toni 2009; Toni 2014; Čyras et
2010). al. 2018), possibly with preferences (Čyras and Toni 2016)
The project is focusing on two classes of diseases – CKD and probabilities (Thang, Dung, and Hung 2012).
(Chronic Kidney Disease) and COPD (Chronic Obstructive Finally, ROAD2H aims at integrate decision support with
Pulmonary Disease): these are challenging diseases to treat, available electronic data, notably electronic health records,
as patients affected by these diseases often suffer or several billing data and standardised representations of guidelines,
comorbidities (e.g. hypertension and diabetes), with con- e.g. as in (Zamborlini et al. 2017).
flicting treatments suggested by the different, relevant guide-
lines; moreover, being chronic, these diseases require pro- References
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