scholarly journals Detection of risk factors of PCOS patients with Local Interpretable Model-agnostic Explanations (LIME) Method that an explainable artificial intelligence model

2021 ◽  
Author(s):  
İpek BALIKÇI ÇİÇEK ◽  
Zeynep KÜÇÜKAKÇALI ◽  
Fatma Hilal YAĞIN
Keyword(s):  
Artificial Intelligence ◽  
Risk Factors ◽  
Explainable Artificial Intelligence ◽  
Interpretable Model

scholarly journals Explainable artificial intelligence (XAI) for exploring spatial variability of lung and bronchus cancer (LBC) mortality rates in the contiguous USA

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zia U. Ahmed ◽  
Kang Sun ◽  
Michael Shelly ◽  
Lina Mu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Factors ◽  
Mortality Rates ◽  
Gradient Boosting ◽  
Ensemble Machine Learning ◽  
Gradient Boosting Machine ◽  
Explainable Artificial Intelligence ◽  
The Impact ◽  
Bronchus Cancer

AbstractMachine learning (ML) has demonstrated promise in predicting mortality; however, understanding spatial variation in risk factor contributions to mortality rate requires explainability. We applied explainable artificial intelligence (XAI) on a stack-ensemble machine learning model framework to explore and visualize the spatial distribution of the contributions of known risk factors to lung and bronchus cancer (LBC) mortality rates in the conterminous United States. We used five base-learners—generalized linear model (GLM), random forest (RF), Gradient boosting machine (GBM), extreme Gradient boosting machine (XGBoost), and Deep Neural Network (DNN) for developing stack-ensemble models. Then we applied several model-agnostic approaches to interpret and visualize the stack ensemble model's output in global and local scales (at the county level). The stack ensemble generally performs better than all the base learners and three spatial regression models. A permutation-based feature importance technique ranked smoking prevalence as the most important predictor, followed by poverty and elevation. However, the impact of these risk factors on LBC mortality rates varies spatially. This is the first study to use ensemble machine learning with explainable algorithms to explore and visualize the spatial heterogeneity of the relationships between LBC mortality and risk factors in the contiguous USA.

scholarly journals Artificial Intelligence to Get Insights of Multi-Drug Resistance Risk Factors during the First 48 Hours from ICU Admission

Antibiotics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 239
Author(s):  
Inmaculada Mora-Jiménez ◽  
Jorge Tarancón-Rey ◽  
Joaquín Álvarez-Rodríguez ◽  
Cristina Soguero-Ruiz
Keyword(s):  
Artificial Intelligence ◽  
Risk Factors ◽  
Drug Resistance ◽  
Multi Drug Resistance ◽  
Time Interval ◽  
Health Records ◽  
Resistance Risk ◽  
Icu Admission ◽  
Selection Strategies ◽  
Patient Will

Multi-drug resistance (MDR) is one of the most current and greatest threats to the global health system nowadays. This situation is especially relevant in Intensive Care Units (ICUs), where the critical health status of these patients makes them more vulnerable. Since MDR confirmation by the microbiology laboratory usually takes 48 h, we propose several artificial intelligence approaches to get insights of MDR risk factors during the first 48 h from the ICU admission. We considered clinical and demographic features, mechanical ventilation and the antibiotics taken by the patients during this time interval. Three feature selection strategies were applied to identify statistically significant differences between MDR and non-MDR patient episodes, ending up in 24 selected features. Among them, SAPS III and Apache II scores, the age and the department of origin were identified. Considering these features, we analyzed the potential of machine learning methods for predicting whether a patient will develop a MDR germ during the first 48 h from the ICU admission. Though the results presented here are just a first incursion into this problem, artificial intelligence approaches have a great impact in this scenario, especially when enriching the set of features from the electronic health records.

An explainable Artificial Intelligence approach to study MCI to AD conversion via HD-EEG processing

2021 ◽  
pp. 155005942110636
Author(s):  
Francesco Carlo Morabito ◽  
Cosimo Ieracitano ◽  
Nadia Mammone
Keyword(s):  
Artificial Intelligence ◽  
Frontal Lobe ◽  
Parietal Lobe ◽  
Classification Performance ◽  
Head Region ◽  
Left Frontal Lobe ◽  
Performance Accuracy ◽  
Power Spectral ◽  
Explainable Artificial Intelligence

An explainable Artificial Intelligence (xAI) approach is proposed to longitudinally monitor subjects affected by Mild Cognitive Impairment (MCI) by using high-density electroencephalography (HD-EEG). To this end, a group of MCI patients was enrolled at IRCCS Centro Neurolesi Bonino Pulejo of Messina (Italy) within a follow-up protocol that included two evaluations steps: T0 (first evaluation) and T1 (three months later). At T1, four MCI patients resulted converted to Alzheimer’s Disease (AD) and were included in the analysis as the goal of this work was to use xAI to detect individual changes in EEGs possibly related to the degeneration from MCI to AD. The proposed methodology consists in mapping segments of HD-EEG into channel-frequency maps by means of the power spectral density. Such maps are used as input to a Convolutional Neural Network (CNN), trained to label the maps as “T0” (MCI state) or “T1” (AD state). Experimental results reported high intra-subject classification performance (accuracy rate up to 98.97% (95% confidence interval: 98.68–99.26)). Subsequently, the explainability of the proposed CNN is explored via a Grad-CAM approach. The procedure allowed to detect which EEG-channels (i.e., head region) and range of frequencies (i.e., sub-bands) resulted more active in the progression to AD. The xAI analysis showed that the main information is included in the delta sub-band and that, limited to the analyzed dataset, the highest relevant areas are: the left-temporal and central-frontal lobe for Sb01, the parietal lobe for Sb02, the left-frontal lobe for Sb03 and the left-frontotemporal region for Sb04.

Sign in / Sign up

Export Citation Format

Share Document