=Paper=
{{Paper
|id=None
|storemode=property
|title=Learning from medical data streams: an introduction
|pdfUrl=https://ceur-ws.org/Vol-765/paper1.pdf
|volume=Vol-765
}}
==Learning from medical data streams: an introduction==
https://ceur-ws.org/Vol-765/paper1.pdf
Learning from medical data streams:
an introduction
Pedro Pereira Rodrigues1 , Mykola Pechenizkiy2 ,
Mohamed Medhat Gaber3 , and João Gama1
1
LIAAD - INESC Porto, L.A. & University of Porto, Portugal
2
Eindhoven University of Technology, The Netherlands
3
University of Portsmouth, United Kingdom
pprodrigues@med.up.pt,m.pechenizkiy@tue.nl
mohamed.gaber@port.ac.uk,jgama@fep.up.pt
Abstract. Clinical practice and research are facing a new challenge cre-
ated by the rapid growth of health information science and technology,
and the complexity and volume of biomedical data. Machine learning
from medical data streams is a recent area of research that aims to
provide better knowledge extraction and evidence-based clinical deci-
sion support in scenarios where data are produced as a continuous flow.
This year’s edition of AIME, the Conference on Artificial Intelligence in
Medicine, enabled the sound discussion of this area of research, mainly
by the inclusion of a dedicated workshop. This paper is an introduction
to LEMEDS, the Learning from Medical Data Streams workshop, which
highlights the contributed papers, the invited talk and expert panel dis-
cussion, as well as related papers accepted to the main conference.
1 Introduction
Artificial Intelligence in Medicine is facing a new challenge, created by the rapid
growth in information science and technology in general and the complexity and
volume of data in particular. Medical settings are using sensors and networks
of health information systems to integrate data from patients from which it is
necessary to extract some sort of knowledge. The main issue is that this data
production often takes the form of continuous flows of data.
Medical domains include several settings where data is produced in a stream-
ing fashion, such as anatomical and physiological sensors, or incidence records
and health information systems. New services like Google Health1 appear allow-
ing users to store and track information about their medical history, to connect to
and stream data from medical devices. Medical data streams become widespread
and call for development of intelligent tool for making use of these data. Decision
support, alerting services, ambient intelligence, assisted leaving and personaliza-
tion services are just few examples of expected uses of actionable knowledge
extracted from medical data streams. All of them are characterized by the high-
speed at which huge amounts of data are produced, and often require fast and
1
http://www.google.com/health/
accurate information retrieval and analysis, that can effectively support clinical
decisions.
Dealing with continuous, and possibly infinite, flows of data require differ-
ent approaches for machine learning and knowledge discovery. Particular issues
to address include summarization of infinite data, incremental and decremental
learning, resource-awareness, real-time monitoring of changes and recurrences,
etc. This is an incremental task that requires incremental learning algorithms
that integrate artificial intelligence in medical domains. Streaming artificial intel-
ligence is increasingly important in the research community, as new algorithms
are needed to process medical data in reasonable time.
Furthermore, medical domains introduce extra peculiarities to the learning
problem. For example, health information systems now deal with heterogeneous
data sources, possibly distributed across healthcare institutions. Moreover, this
data integration requirement yields possibly privacy-preserving issues, the same
time it forces the system to take time, resources, and costs into consideration.
Currently, generic techniques for intelligent analysis and learning from stream-
ing data are widely spread in the machine learning research community. Also, in
the medical domain technological issues of data collection and storage, access,
integration, information fusion, etc are also widely studied in the health in-
formatics research community. However, adoption and development of tailored
techniques for medical stream mining and clinical decision support is still to
come.
2 Learning from Medical Data Streams
The artificial intelligence community has long identified machine learning as a
prospective branch suited to address medical data [5]. However, its application
to medical streams presents several issues that need to be solved. In this section
we present an introduction to the Learning from Medical Data Streams workshop
(LEMEDS 2011), organized in conjunction with 13th Conference on Artificial
Intelligence in Medicine (AIME 2011), highlighting the most recent works pro-
posed in the field of learning from medical data streams.
2.1 LEMEDS 2011 Contributed Papers
The first edition of the Learning from Medical Data Streams workshop has in-
cluded contributions from diverse fields of research that address medical data
streams [3, 7, 9–11].
The fact that more and more medical data is being produced by sensors
that measure physiological parameters [6] includes new challenges to artificial
intelligence in general, and machine learning in particular. Jones et al. [3] pro-
posed to interpret biosignals to improve mobile health monitoring for clinical
decision support, using body sensor networks. The paper presents two possible
applications and discusses the possibilities of applying machine learning in this
ubiquitous streaming scenario, yielding a sound discussion in the learning from
data streams forum. Biomedical signals have also been addressed by the two
following works.
Rodrigues et al. [10] propose to improve cardiotocography monitoring using
streaming statistics of both the fetal heart rate and the uterine contractions
signals. The statistics will then be used to early detect changes in the monitored
signals, and help in the prediction of birth outcome. It is an interesting position
paper that has not experimentation yet, but should foster a sound discussion on
the subject.
Sebastião et al. [11] developed a learning-based advisory system for detecting
changes in depth of anesthesia signals. The paper addresses an important prob-
lem in the operational settings that can help to adapt administered doses for
patients. The problem is formulated and addressed as the problem of handling
concept drift in online settings, with the obtained experimental results, based
on real data collected at one of the hospitals, being very promising.
Considering a higher-level approach to stream processing, McGregor et al. [7]
presented a process mining framework to improve clinical guidelines in clinical
care. The proposal is based on an extension of the CRISP-DM model, which con-
siders temporal abstractions and multiple dimensions (CRISP-TDMn) and Pa-
JMa to model the temporal abstractions as patient journeys. The paper presents
a very interesting approach to knowledge discovery in a challenging scenario
where data is produced as several heterogeneous streams.
Intensive care units are, undoubtedly in current healthcare services, the main
clinical setting where data streams are being produced. But other medical data
streams exist which differ from biomedical signals. An example is presented by
Rodrigues et al. [9], where the authors describe a setting of integrated electronic
health records, trying to improve the visualization mechanism of the increasing
amount of clinical documents available in a central hospital. This is performed
through the proposition of new bayesian approaches. As a position paper, this
paper clearly presents the problem spaces and the research presents a valid and
realistic problem that exists within healthcare today.
2.2 LEMEDS 2011 Invited Talk
The workshop chairs are honoured to include an invited talk by Peter Lucas
(Radboud University Nijmegen, The Netherlands), one of the most knowledgeable
researchers in the field of Artificial Intelligence in Medicine, with an emphasis
on Bayesian techniques for clinical decision support. The talk, entitled “Disease
Monitoring and Clinical Decision Support”, focus on the properties of biomedical
data streams (e.g. from sensors) that impose constraints on how collected data
can be exploited, and the new opportunities to monitor the progress of diseases in
patients, reviewing some of these requirements and illustrating them by various
real-world applications [6].
2.3 LEMEDS 2011 Panel Discussion
Given that it is still a young research area, the LEMEDS workshop aims at
convening researchers from related fields in order to find and consolidate a net-
work of interests. This way, the workshop will promote a panel discussion on
the “Challenges and roadmap for machine learning from medical data streams”,
with the participation of three scholar experts:
– Carlo Combi (University of Verona, Italy), an expert on temporal informa-
tion systems, with an emphasis on the management of clinical information;
– Carolyn McGregor (University of Ontario Institute of Technology, Canada),
an expert on health informatics, with an emphasis on data streams processing
in critical care settings; and
– João Gama (University of Porto, Portugal), an expert on machine learning,
with an emphasis on learning from ubiquitous data streams.
Topics that are suggested to be discussed include: main domains where medical
data is produced as a stream; applications for LEMEDS; related fields of re-
search; issues that differentiate this research area from other related fields; and
best forums/venues for researchers to publish and discuss LEMEDS.
2.4 AIME 2011 Contributed Papers
This year’s edition of AIME included six papers which address stream-related
medical data [1, 2, 4, 8, 12, 13]. Although they might not directly include stream-
ing machine learning techniques, they present scenarios and approaches which
are relevant for discussion here.
Clinical time series are often produced in a stream. Enright et al. [1] proposed
to analyze clinical time series using mathematical models and dynamic Bayesian
networks. One type of medical data that is usually produced in a stream are
physiological readings (e.g. heart rate). Garcı́a-Garcı́a et al. [2] used statistical
machine learning to assess physical activity based on accelometry and heart rate
readings. Wieringa et al. [12] also addressed physical activity, by defining an
ontology-based dynamic feedback to the users. On a related topic, Jovic and
Bogunovic [4] presented a Java-based framework to extrat features from cardiac
rhythm. Rees et al. [8] presented the intelligent ventilator project, where physio-
logical models are used in decision support, while Williams and Stanculescu [13]
proposed to automate the calibration of a neonatal condition monitoring system.
These are clearly related with intensive care units, a usual setting where data
are produced as streams. The adaptation and application of such methods to
streaming settings is a relevant path of research that should be considered.
3 Future Paths
LEMEDS is a recent trend of research that is yet to be consolidated. Thus, the
corpus of contributions that has already been produced for AIME and LEMEDS,
in 2011, supports the idea that, not only the involved research questions are both
relevant and timely, but also knowledge in this domain is expanding and a small
community is emerging, coming from related areas such as health informat-
ics, machine learning, and clinical decision support. Given this, we believe that
further activity is definitely going to exist and the field will produce valuable
applications to improve healthcare.
Acknowledgments
The workshop chairs would like to thank all the participants that made this event
possible. First, the authors of contributed papers are acknowledged for their par-
ticipation. Then, we kindly thank the participation of our invited speaker, Peter
Lucas, and our experts panel: Carlo Combi, Carolyn McGregor and João Gama.
Also, we would like to thank the other Program Committee members for their
help in peer-reviewing the contributed papers; thanks Miguel Coimbra, Antoine
Cornuéjols, Matjaz Kukar, Mark Last, Florent Masseglia, Ernestina Menasalvas,
Josep Roure Alcobé, Cristina Santos, Alexey Tsymbal and Indre Zliobaite. Ulti-
mately, the chairs thank the attendants of the workshop, to whom the event is
intended after all.
References
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