AI for Medicine

 

AI for Medicine

    Introduction:

    Explainable artificial intelligence has played a key feature in medical developing in recent years, in this blog I'm going to present examples where AI has been used to accomplish major goal in this domain.

1. Using AI to find the best antibiotic treatments for specific patients. 

This is a 3 step project:

• Generate a tabular dataset from the ontology, containing features defined on various domains and n-ary features
• A preference model was then learned from patient profiles, antibiotic features and expert recommendations found in clinical practice guidelines.
• Then visualize the preference model and its application to all antibiotics available on the market for a given clinical situation, using rainbow boxes, a recently developed technique for set visualization. 

2. Using AI to improve cancer diagnostic from histopathological images

The way this works is by getting specific tissue images overtime, the idea here being that as a cancer tumor develops, successive pictures of the same are will be able to highlight the differences caused by the development of the tumor. We feed those images into a convolutional neural network and we use a Cumulative Fuzzy Class Membership Criterion Classifier to figure out whether or not the tissue picture contains or not cancerous tumors.

    Finally we have a human double check the prediction, the power of this model is that we can detect tumors that might be hard to spot to humans, however at the same time we need someone to make sure because the treatment for cancer is very dangerous and we need to be absolutely sure before we start it.

    The algorithm consists of two phases: the initialization and the learning phase. The initialization phase consists of three processes: data splitting, clustering, and parameters initialization. First, input data are divided into three sets: training sets, validation sets, and testing sets.

3. Predict or draw blood: An integrated method to reduce lab tests

    Serial Lab testing can be harmful to patients especially those in ICUs, in the following pattern a model which can reduce the amount of testing has been proposed, this is done by predicting those tests that can be skipped. Those test would most likely would offer inconclusive results which wouldn't lead to reaching an accurate diagnostic. The model has the ability to cut unnecessary testing by up to 15%, this leads to a betterment of the patient who doesn't need to give that much blood to be tested.

    The reduction comes mostly from future lab tests, in general the same test will be done over a period

in order to check the evolution of the body under supervision, by relying on the evolution of those results up to a certain point we can figure out whether or not future tests are necessary.

Bibliography

[1]    Jean-Baptiste Lamy, Karima Sedki, Rosy Tsopra, “Explainable decision support through the learning and visualization of preferences from a formal ontology of antibiotic treatments.” Journal of Biomedical Informatics 8th March 2020

[2]    Patrik Sabol a, Peter Sinčák a, Pitoyo Hartono b, Pavel Kočan c, Zuzana Benetinová c, Alžbeta Blichárová c, Ľudmila Verbóová c, Erika Štammová c, Antónia Sabolová-Fabianová d, Anna Jašková d, “Explainable classifier for improving the accountability in decision-making for colorectal cancer diagnosis from histopathological images” Journal of Biomedical Informatics 3rd August 2020

[3]    Predict or draw blood: An integrated method to reduce lab tests Author links open overlay panelLishan Yu, Qiuchen                  Zhang, Elmer V. Bernstam Xiaoqian Jiang in Journal of Biomedical Informatics Volume 104, April 2020, 103394