New Horizons
Do artificial neural networks love sex? How the combination of artificial neural networks with evolutionary algorithms may help to identify gender influence in rheumatic diseases
E. Grossi1
- Villa Santa Maria Foundation, Tavernerio, Italy. enzo.grossi@bracco.com
CER16456
2023 Vol.41, N°1
PI 0001, PF 0005
New Horizons
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PMID: 36689207 [PubMed]
Received: 14/12/2022
Accepted : 02/01/2023
In Press: 23/01/2023
Published: 23/01/2023
Abstract
Although medical research has been performed predominantly on men both in preclinical and clinical studies, continuous efforts have been made to overcome this gender bias. Examining retrospectively 21 data sets containing sex as one of the descriptive variables, it was possible to verify how many times our AI protocol decided to keep gender information in the predictive model. The data sets pertained a vast array of diseases such as dyspeptic syndrome, atrophic gastritis, venous thrombosis, gastroesophageal reflux disease, irritable bowel syndrome, Alzheimer diseases and mild cognitive impairment, myocardial infarction, gastrointestinal bleeding, gastric cancer, hypercortisolism, AIDS, COVID diagnosis, extracorporeal membrane oxygenation in intensive therapy, among others. The sample size of these data sets ranged between 80 and 3147 (average 600). The number of variables ranged from 19 to 101 (average 41). Gender resulted to be part of the heuristic predictive model 19 out of 21 times. This means that also for highly adaptive and potent tools like Artificial Neural Networks, information on sex carries a specific value. In the field of rheumatology, there is a specific example in psoriatic arthritis that shows that the presence of gender information allows a significantly better accuracy of ANNs in predicting diagnosis from clinical data (from 87.7% to 94.47%). The results of this study confirm the importance of gender information in building high performance predictive model in the field of Artificial Intelligence (AI). Therefore, also for AI, sex counts.
