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PhD Position on AI techniques for clinical pathways. Fondazione Bruno Kessler, Italy

From: Mauro Dragoni <dragoni@fbk.eu>
Date: Thu, 7 May 2015 22:15:34 +0200
Message-ID: <CAFvSjQtaJmL8ELS1MXCR62028i0cKnV3YnY8gQbBAO5WM8-nGA@mail.gmail.com>
To: Semantic Web <semantic-web@w3.org>, public-lod@w3.org, public-ontolex@w3.org, CHI-ANNOUNCEMENTS@listserv.acm.org, confs-submit@hri.org, aisworld@lists.aisnet.org, planetkr@kr.org, Community@sti2.org, semanticweb@yahoogroups.com, events_calendar@acm.org, linguist@linguistlist.org, dbpedia-discussion@lists.sourceforge.net, dbpedia-developers@lists.sourceforge.net, public-ldp@w3.org, semantic_web_doktorandennetzwerk@lists.spline.inf.fu-berlin.de, lod2@lists.okfn.org, public-vocabs@w3.org
Position: 1 PhD position
Duration: 3 years
Close Date: May 27 2015.
Formal application at:
http://ict.unitn.it/application/project_specific_grants#D3

A PhD position is open at the Fondazione Bruno Kessler, Trento Italy on
“combining planning and machine learning techniques for the re-planning of
clinical pathways”. The research focuses on the development and usage of AI
techniques in a medical domain and will be carried out jointly between the
research groups of e-HEALTH (ehealth.fbk.eu) and SHELL (shell.fbk.eu).

ABSTRACT: In the last decades, the use of IT systems for supporting
business activities has notably increased, thus opening to the possibility
of monitoring business processes and performing on top of them a number of
useful analysis. This has brought to a large diffusion of tools that offer
business analysts the possibility to observe the current process execution,
identify deviations from the model, perform individual and aggregated
analysis on current and past executions, thus supporting process model
re-design and improvement. Unfortunately, a number of difficulties may
arise when exploiting information system data for monitoring and analysis
purposes. Among these, on-line monitoring of data may identify deviations
from the model that need to be handled and solved while the process is
running so that the process execution can continue in an acceptable way,
ideally by returning to a compliant execution as fast as possible. An
example of this scenario happens in the medical domain, where clinical
paths for certain conditions (e.g., a pregnancy) can deviate from the
suggested medical path due to e.g., unavailability of facilities or
exceptions not captured in the model, but need to go back and follow the
``normal path’’ after the local deviation is performed. The aim of this
thesis is investigating how to exploit, adapt and combine techniques and
approaches borrowed from different research fields, in particular planning
and machine learning, to advance the existing services for process
(re-)planning. Planning techniques will make usage of the procedural
knowledge encoded in the medical guidelines (following the approach of
e.g., [1]), while machine learning approaches will exploit the presence of
monitored similar data (starting from frameworks such as [2]).  To this
purpose, several are the challenges to be faced in the work as, for
example, (i) the capability to represent and reason about secondary aspects
for business processes such as data, time, resources; (ii) the capability
to align execution information with models; (iii) the capability to realize
the above-mentioned analyses at run-time. The work will put together
theoretical and methodological aspects, including for example the problem
conceptualization and representation, as well as implementation and
optimization ones, aimed at the development of process analysis services
and tools.

SKILLS REQUIRED: good knowledge of artificial intelligence and/or knowledge
representation techniques.


About Us

FBK-ICT (www.fbk.eu) conducts research in information technology. Research
units such as SHELL (shell.fbk.eu) and e-HEALTH (ehealth.fbk.eu) aim at
addressing important research challenges by exploiting and joining the
different scientific competences that are at the base of the
internationally well-known scientific excellence of FBK.


For further infos please contact:

Dr. Chiara Ghidini
SHELL, Fondazione Bruno Kessler
Email: ghidini@fbk.eu
Web: shell-static.fbk.eu/people/ghidini

Dr. Claudio Eccher
e-HEALTH, Fondazione Bruno Kessler
Email: cleccher@fbk.eu
Web: ehealth.fbk.eu/people/profile/cleccher


-- 
Dr. Mauro Dragoni
Post-Doc Researcher at Fondazione Bruno Kessler (FBK-IRST)
Via Sommarive 18, 38123, Povo, Trento, Italy
Tel. 0461-314053
Received on Thursday, 7 May 2015 20:16:22 UTC

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