[Haskell] Journal of Biomedical Informatics - Call for Papers: Special issue on learning from multiple data sources for decision making in health care

[Francesco Calimeri]

[APOLOGIZE FOR MULTIPLE POSTINGS]

=

It's a pleasure to announce the OPEN special issue of the *Journal of
Biomedical Informatics*, published by *ELSEVIER *(*ISSN: 1532-0480*)
entitled: "*learning from multiple data sources for decision making in
health care*".

Deadline is *15 October, 2024*.

For your convenience, some details are reported below; you can find the
full call here, along with directions, here:
    https://www.sciencedirect.com/science/article/pii/S1532046424000637

Please forward this to your colleagues and collaborators, and anyone
potentially interested; this issue is going to build a road towards one of
the main goals of the HC at AIxIA working group, namely fostering an effective
application of AI to medicine and the healthcare domain.

Feel free to contact us at
    hc-aixia at googlegroups.com
and visit
    https://aixia.it/en/gruppi/hc/
for further information about the group activities and initiatives.

Sincerely,
Francesco Calimeri, Mauro Dragoni, Fabio Stella
Coordinators of the Working Group on Artificial Intelligence for Healthcare



=== === === === === === === === ===


Journal of Biomedical Informatics
<https://www.sciencedirect.com/journal/journal-of-biomedical-informatics>Special
issue on learning from multiple data sources for decision making in health
care
The increasing availability of digital data, along with recent developments
in Artificial Intelligence, especially in the Machine Learning and Deep
Learning fields, led the scientific community to debate whether data alone
is sufficient for decision making and scientific exploration. We focus the
attention on the healthcare domain, where peculiar issues affect data:
indeed, data are usually collected under heterogeneous conditions (i.e.,
different populations, regimes, and sampling methods), suffer missingness –
very often not at random – and their use is strongly constrained by privacy
issues. In such a complex setting, this special issue challenges computer
scientists to contribute to the above debate by designing and developing
innovative methodological approaches, for solving complex decision-making
problems in health care, leveraging on observational data.

Topics of interest include, but are not limited to, the following with an
emphasis on novel generalizable methods applied to the healthcare domain:

   - •

   Causal discovery from multiple data sets.
   - •

   Federated causal discovery.
   - •

   Causal discovery from heterogeneous data sets.
   - •

   Transportability of causal models and inference.
   - •

   Neuro-symbolic approaches to learn from heterogeneous data sources.
   - •

   Continual learning on streams from multiple data sources.
   - •

   Computational intelligent strategies to support causal inference.
   - •

   Edge computing for decision making in healthcare.
   - •

   Integrative AI methodologies.
   - •

   Distributed inference methods.
   - •

   Continual Learning.
   - •

   Knowledge Discovery and Integration.
   - •

   Combination of deductive approaches with ML models.
   - •

   Combination of ontologies and/or knowledge-bases with ML to support
   decision making.

Peer Review Process:

All submitted papers will undergo a rigorous peer-review process featuring
at least two reviewers. All submissions should follow the guidelines for
authors available at the Journal of Biomedical Informatics website (
http://www.elsevier.com/locate/yjbin). JBI’s editorial policy outlined on
that page will be strictly enforced by special issue reviewers.

Note that JBI emphasizes the publication of papers that introduce
innovative and generalizable methods of interest to the informatics
community. Specific applications can be described to motivate the
methodology being introduced, but papers that focus solely on a specific
application are not suitable. A few examples of papers focused on methods
previously published in JBI include: Kyrimi, et al. [1]
<https://www.sciencedirect.com/science/article/pii/S1532046424000637#b0005>,
Huang, et al. [2]
<https://www.sciencedirect.com/science/article/pii/S1532046424000637#b0010>,
Kocbek et al. [3]
<https://www.sciencedirect.com/science/article/pii/S1532046424000637#b0015>,
Houston et al. [4]
<https://www.sciencedirect.com/science/article/pii/S1532046424000637#b0020>,
García Del Valle et al. [5]
<https://www.sciencedirect.com/science/article/pii/S1532046424000637#b0025>,
Graudenzi et al. [6]
<https://www.sciencedirect.com/science/article/pii/S1532046424000637#b0030> and
Sims et al. [7]
<https://www.sciencedirect.com/science/article/pii/S1532046424000637#b0035>.

In particular, the authors of [1]
<https://www.sciencedirect.com/science/article/pii/S1532046424000637#b0005>
showed
the relevance of causal models and expert knowledge to develop credible
models, i.e., capable of achieving good predictive performances when
transported from the study cohort to the target population. Furthermore, [2]
<https://www.sciencedirect.com/science/article/pii/S1532046424000637#b0010>
tackles
the relevant issue of partially overlapping variables when data are
collected from multiple data sources. This problem is extremely relevant
both in theoretical and practical terms for decision making in the
healthcare sector.

The contribution provided in [3]
<https://www.sciencedirect.com/science/article/pii/S1532046424000637#b0015>
stressed
the importance of working in a multi-source context by demonstrating how
the linking of different repositories can improve the overall understanding
of patients' conditions. Similarly, in [4]
<https://www.sciencedirect.com/science/article/pii/S1532046424000637#b0020> the
authors extended this concept by introducing a methodology to evaluate to
audit the data quality of the sources exploited by healthcare information
systems. Then, in [5]
<https://www.sciencedirect.com/science/article/pii/S1532046424000637#b0025> the
multi-source concept is transferred within the multi-modal environment and
the authors surveyed the importance of considering different modalities to
obtain a better disease understanding.

The works in [6]
<https://www.sciencedirect.com/science/article/pii/S1532046424000637#b0030>
 and [7]
<https://www.sciencedirect.com/science/article/pii/S1532046424000637#b0035>
focuses
on the importance of data. In [6]
<https://www.sciencedirect.com/science/article/pii/S1532046424000637#b0030> a
data integration framework is defined for characterizing the metabolic
deregulations that distinguish cancer phenotypes, by projecting RNA-seq
data onto metabolic networks without the need for metabolic measurements;
in [7]
<https://www.sciencedirect.com/science/article/pii/S1532046424000637#b0035> a
biomedical informatics method is introduced that uses multiple public
health data sources to perform surveillance of methadone-related adverse
drug events. Interestingly, even if patient data are not linked between
different data sources, results show that the integration of multiple
public data sources can capture more cases and provide more clinical
details than individual data sources alone.

Key requirements for JBI ML papers in addition to presenting novel methods
(not simply application of existing methods to a new healthcare domain) are
as follows: 1) projects must have clinicians involved in research
question/problem formulation, defining input data, and assessing the
results. 2) An explanation (with clinicians) of how the proposed method
would fit into the clinical workflow is expected. It must be translational
to practice. 3) Data sets should preferably be collected from hospitals
after the research question was formulated, thus avoiding the use of
available data (MIMIC) to define a very wide research problem that could
potentially be answered with available open datasets (as an example:
detecting if someone has COVID from Chest X-Rays would not be acceptable,
as the gold standard test is the laboratory test). 4) As for
explainability, SHAP values and related diagrams would not be enough: the
paper should clearly describe and explain how clinicians use the
visualization to make decisions. For further details please refer to
https://www.sciencedirect.com/journal/journal-of-biomedical-informatics/publish/guide-for-authors
.

Submission process:

Authors must submit their paper via the online Elsevier Editorial System
(EES) at http://ees.elsevier.com/jbi by October 15th, 2024. Authors can
register and upload their text, tables, and figures as well as subsequent
revisions through this website. Potential authors may contact the
Publishing Services Coordinator in the journal’s editorial office (jbi
@elsevier.com <http://jbi%40elsevier.com/>) for questions regarding this
process. When asked for the category of their submission, they should
indicate that it is for the special issue on Learning from multiple data
sources for decision making in health care.
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