Dear All,

We will have the following seminar by Franklin Parales, from Complutense
University of Madrid, on November 8th.

Title: Data processing methodologies in the area of E-Health for categorizing
therapeutic responses in patients with migraine

Keywords: classification algorithms, MOEAs, prediction, data mining, migraine

Date: 2018-Nov-08   Time: 11:00
Room: INESC-ID 336

Abstrat: Migraine is a chronic disease that affects the daily development of
activities of people around the world. To alleviate the symptoms,
OnabotulinumtoxinA (BoNT-A) has solid proven evidence for their use according
to various works and clinical trials. Nowadays, it is known that 70-80% of
patients with chronic migraine show an improvement with this treatment
(improvement defined as a reduction in migraine attack frequency or days with
attacks by at least 50% within 3 months, leading to a significantly improved
functioning of the patients and their overall quality of life). As has been
mentioned by [1], it is very important to predict if the BoNT-A treatment will
be effective in a patient. Knowing the phenotype-response relationship may
help in the development of new treatments for the 20-30% of patients that do
not respond to the treatment.

This talk will describe two approaches for addressing the prediction of the
therapeutic response to BoNT-A: panoramic and feedback prediction [2].
Panoramic prediction makes it possible to decide whether the treatment will
be beneficial without using previous knowledge and without involving
unnecessary treatments. Feedback prediction can be more accurate prediction
since it considers the results of previous stages of the treatment. With
the purpose of unveiling the medical attributes that make treatment
effective for patients, consensus models are applied to the prediction
models found through the proposed approaches. The following attributes have
been found to be relevant when predicting the treatment response to BoNT-A:
migraine time evolution, unilateral pain, analgesic abuse, headache days
and the retroocular component. According to doctors, these factors are also
medically relevant and in alignment with the medical literature.

When training the prediction models, an attribute weighting task is
considered. It is performed with the purpose of finding those weights that
improve the representation of the numeric labels encoded by doctors for
each stage of BoNT-A treatment. In the panoramic prediction, the attribute
weighting is multiobjective because we need to find the optimal weights
that improve the prediction accuracy for all stages, simultaneously. In
this sense, multiobjective evolutionary algorithms (MOEAs) that support
parallelization have been considered for improving the training time of
predictive models [3].

The obtained results show accuracies close to 85% and 90% for panoramic and
feedback prediction approaches, respectively. Moreover, the training time
of the panoramic prediction models is decreased from 8 to less than 2 hours
when using 8 threads.

[1] Lovati, C., Giani, L. Action mechanisms of Onabotulinum toxin-A: hints
for selection of eligible patients. Neurological Sciences, vol. 38(1),
pages131–140, 2017.

[2] Parrales, F.,Del Barrio, A. A.,Gago, A. B.,Gallego, M. M.,Ruiz,
M.,Peral, A. G.,Dzeroski, S.&Ayala, J. L.SMURF:Systematic Methodology for
Unveiling Relevant Factors in retrospectivedata on chronic disease
treatments.IEEE Access, pages 1–1, 2019. ISSN2169-3536

[3] Parrales, F.,Del Barrio, A. A.&Ayala, J. L.A study on the
parallelization of MOEAs to predict the patient’s response to the
OnabotulinumtoxinA treatment.  In Proceedings of the Summer Simulation
Multi-Conference. Society for Computer Simulation International, 2019

Best regards,

Alexandre Francisco