[ F.Y.I. Announcement of Conference on AI in Medicine]

[sims@pdesds1.scra.org]

 This seemed relevant. Please excuse the bandwidth if it's not.

 jim

From:	IN%"DFP10 at ALBANY.ALBANY.EDU"  "Donald F. Parsons MD"  3-AUG-1993 05:38:36.51
To:	IN%"hspnet-l at albnydh2.bitnet"  "Rural Hospital Consulting Network"
CC:	
Subj:	Call for Papers: AIM-94 Spring Symposium

----------------------------Original message----------------------------
                             Call for Papers

                       AAAI 1994 Spring Symposium:
       Artificial Intelligence in Medicine:  Interpreting Clinical Data

           (March 21-23, 1994, Stanford University, Stanford, CA)


The deployment of on-line clinical databases, many supplanting the
traditional role of the paper patient chart, has increased rapidly over the
past decade. The consequent explosion in the quality and volume of
available clinical data, along with an ever more stringent medicolegal
obligation to remain aware of all implications of these data, has created a
substantial burden for the clinician. The challenge of providing intelligent
tools to help clinicians monitor patient clinical courses, forecast likely
prognoses, and discover new relational knowledge, is at least as large as
that generated by the knowledge explosion which motivated earlier efforts
in Artificial Intelligence in Medicine (AIM).  Whereas many of the
pioneering programs worked on small data sets which were entered
interactively by knowledge engineers or clinicians, the current generation
of programs have to act on raw data, unfiltered and unmediated by human
beings.  Interaction with human users typically only occurs on demand or on
detection of clinically significant events.  The emphasis of this symposium
will be on methodologies that provide robust autonomous performance in
data-rich clinical environments ranging from busy outpatient practices to
operating rooms and intensive care units. Relevant topics include
intelligent alarming (including anticipation and prevention of adverse
clinical events), data abstraction, sensor validation, preliminary event
classification, therapy advice, critiquing, and assistance in the
establishment and execution of clinical treatment protocols. Detection of
temporal and geographical patterns of disease manifestations and machine
learning of clinical patterns are also of interest.


Organizing committee

Serdar Uckun, Co-chair (Stanford University)
Isaac Kohane, Co-chair (Harvard Medical School)
Enrico Coiera (Hewlett-Packard Laboratories/Bristol)
Ramesh Patil (USC/Information Sciences Institute)
Mario Stefanelli (Universita di Pavia)


Format

A large data sample will be made available to participants to serve as
training and test sets for various approaches to information management and
to provide a common domain of discourse.  The sample will consist of two
data sets:

* A dense, high volume data set typical of a critical care environment.
This data set will consist of hemodynamic measurements, mechanical
ventilator settings, laboratory values including arterial blood gas
measurements, and treatment information covering a 12-hour period of a
patient with severe respiratory distress.  Monitored parameters (10-15
channels of data) will be sampled and recorded at rates up to 1/10 Hz.
The data set will be annotated with other clinically relevant data,
physician's interpretations, and established diagnoses.

* A large number of sparse data sets representative of outpatient
environments.  The data will include laboratory measurements, treatment
information, and physical findings on a large sample of patients (50 to
100 patients) taken from the same disorder population. Each patient record
will consist of several weeks' or months' worth of clinical information
sampled at irregular intervals.  Most of the cases will be made available
to interested researchers to be used as training cases.  For interested
parties, a small percentage of cases will be made available two weeks
prior to the symposium to be used as an optional testing set for various
approaches.

The data samples and accompanying clinical information will be available
via ftp or e-mail server around August 15, 1993.  Please contact the
organizers at the addresses below for further information.  The
data will also be made available on diskettes to participants who do not
have Internet access.  It will be left to the discretion of the participants
to use any subset of these samples to help focus their approaches and
presentations.  The data can also be used as test vehicles for their own
research and to create sample programs for demonstration at the symposium.
Participants do not have to use the data in order to participate.  However,
the program committee will favor presentations which exploit the provided
data sets in their analyses.


Submission process

Potential participants are invited to submit abstracts no longer than
2 pages (< 1200 words) by October 15, 1993.  The abstracts should outline
methodology and indicate, if applicable, how the provided data may be used
as a proof-of-principle for the discussed methodology.  Electronic submissions
are encouraged.  The abstracts may be sent to <aim-94 at camis.stanford.edu>
in ASCII, RTF, or PostScript formats.  Authors of accepted abstracts will
be asked to submit a working paper by January 31, 1994.  They will also be
asked to prepare either a poster or an oral presentation.


Submissions by mail

Use this method ONLY IF you cannot submit an abstract electronically.  Fax
submissions will not be accepted. Send 6 copies of the abstract to:

Serdar Uckun, MD, PhD
Co-chair, AIM-94
Knowledge Systems Laboratory
Stanford University
701 Welch Road, Bldg. C
Palo Alto, CA 94304
U.S.A.
Phone: [+1] (415) 723-1915


Calendar

      Abstracts due:                                    October 15, 1993
      Notification of authors by:                       November 15, 1993
      Working papers due:                               January 31, 1994
      Spring Symposium:                                 March 21-23, 1994


Information

For further information, please contact the co-chairs at the address above
or (preferably) via e-mail at:
        <aim-94 at camis.stanford.edu>