GOML Graph-Oriented Machine Learning for Stroke Diagnosis and Rehabilitation

Improving Stroke diagnosis, treatment, and rehabilitation with graph-oriented machine learning on multimodal data.

Leaders: José Krieger and Zhao Liang

The recent advances of machine learning in medicine have been remarkable. However, there are still important issues that need to be addressed. Here we deal with two important questions:

1) How to integrate and select relevant medical features (biomarkers) from large-scale heterogeneous and dynamical sources?

In applications of machine learning in medicine, we often have to deal with large-scale heterogeneous and dynamical data sets. For example, in the case of applications and scientific research related to stroke, or cerebrovascular accident (CVA), various kinds of data accumulated for long period of time, such as texts, images, genetic biomarkers, electric signals, patient's symptoms, and geographic information are often available even for a single patient. Information integration is essential to correctly address health problems, as healthcare professionals rarely use only one type of information when solving a medical problem. Another important aspect when dealing with a large amount of features is to properly select the most relevant ones: understanding which features are most relevant for the classification of a stroke provides important information for quick and accurate diagnosis and treatment.

2) How to interpret decisions made by machine learning algorithms and how to integrate human and artificial intelligence?

Currently, successful machine learning techniques do not provide an explicit mechanism to satisfactorily explain how a given result is achieved. Such a logical explanation is necessary in many medical applications, for example, in disease diagnosis. The lack of interpretability deeply impacts the possibilities of integrating human and artificial intelligence in Medicine. In the majority of the cases, healthcare professionals still consider machine learning algorithms as black-box machines. Again, this is highly influenced by the lack of interpretability of machine learning strategies.

Our approaches primarily deal with Cerebrovascular Accident (CVA) as the application domain. According to the WHO, more than one billion people in the world have some disability; among chronic diseases, stroke stands out because it is the main cause of disability and the second cause of death in the world. Much progress has been made in understanding the risk factors, mortality and rehabilitation of stroke; however, incidence continues to increase as a result of an aging population and other risk factors. The identification of more precise and sensitive stroke biomarkers can help to modify this worrying situation. Furthermore, developing diagnostic approaches with high accuracy and prediction of individualized outcomes is one of the main ambitions and is one of the strategies of the WHO 2014-2021 global action plan (ODS - objective 3, best health for all at all ages).

Goals

The objective here is two-fold. On the one hand, we wish to contribute to machine learning by developing new techniques to handle situations described above. On the other hand, we wish to apply new graph- oriented machine learning techniques (GOML), to be developed in this project, to obtain a better understanding of stroke (causes, impact, ways to improve decision, and rehabilitation). It is also important to investigate ways to mitigate the impact of stroke in Brazilian population, a major social contribution. For the proposed study, we will use datasets from ATLAS (Anatomical Tracings of Lesions After Stroke), InCor (Heart Institute of Medical School of USP) stroke dataset (200 T1-weighted MRIs and Reports), and the data sets of IMREA - Instituto de Medicina Física e Reabilitação do Hospital das Clínicas FMUSP.

Equipe

  • Nome

    Afiliação

  • Adilson Vitar Jr.
    ICMC-USP
  • Alexandre D. P. Chiavegatto Filho
    Faculdade de Saúde Pública / USP
  • Ana Caroline Medeiros Brito
    ICMC-USP
  • Angélica Abadia Paulista Ribeiro
    ICMC-USP
  • Carlos Henrique Costa Ribeiro
    ITA
  • Cíntia Carvalho Oliveira
    ICMC-USP
  • Claudia Maria Cabral Moro Barra
    PUCPR
  • Clever Ricardo Guareis de Farias
    FFCLRP-USP
  • Daniele Carvalho Oliveira
    ICMC-USP
  • Diego Pereira Dedize
    EACH-USP
  • Diego Raphael Amancio
    ICMC-USP
  • Esteban Wilfredo Vilca Zuñiga
    FFCLRP-USP
  • Fátima de Lourdes dos Santos Nunes Marques
    EACH/USP
  • Fernando Henrique Carvalho Silva
    ICMC-USP
  • Fernando Soares de Aguiar Neto
    ICMC-USP
  • Flavio Pinto de Almeida Filho
    ICMC-USP
  • João Luís Garcia Rosa
    ICMC-USP
  • João Paulo Papa
    UNESP Bauru
  • Joaquim Cezar Felipe
    FFCLRP-USP
  • Jorge Valverde-Rebaza
    Visibilia
  • José Eduardo Krieger
    Faculdade de Medicina and INCOR/USP
  • Josimar Edinson Chire Saire
    ICMC-USP
  • Linamara Rizzo Battistella
    Faculdade de Medicina/USP
  • Luan Vinicius de Carvalho Martins
    ICMC-USP
  • Lucas Mateus Martins Araujo e Castro
    ICMC-USP
  • Luiz Otavio Murta Junior
    FFCLRP/USP
  • Marcel Simis
    Faculdade de Medicina/USP
  • Marcela Prince Antunes
    ICMC-USP
  • Márcia Ito
    Ministério da Saude
  • Marco Antonio Gutierrez
    INCOR/USP
  • Marcos Vinicius Naves Bedo
    FMRP-USP
  • Mateus Roder
    UNESP-Br
  • Octávio Marques Pontes Neto
    FMRP/USP
  • Paul Augusto Bustíos Belizario
    ICMC-USP
  • Paulo Mazzoncini de Azevedo Marques
    FMRP/USP
  • Paulo Roberto Domingues dos Santos
    FFCLRP-USP
  • Pedro Augusto Baldini de Carvalho
    FFCLRP-USP
  • Rafael Delalibera Rodrigues
    ICMC-USP
  • Renato Tinós
    FFCLRP/USP
  • Sérgio Baldo Junior
    FFCLRP-USP
  • Vinicius Pavanelli Vianna
    FFCLRP-USP
  • Zhao Liang
    FFCLRP/USP