Explainable Machine-Learning Models for COVID-19 Prognosis Prediction Using Clinical, Laboratory and Radiomic Features DOI Creative Commons
Francesco Prinzi, Carmelo Militello, Nicola Scichilone

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 121492 - 121510

Published: Jan. 1, 2023

The SARS-CoV-2 virus pandemic had devastating effects on various aspects of life: clinical cases, ranging from mild to severe, can lead lung failure and death. Due the high incidence, data-driven models support physicians in patient management. explainability interpretability machine-learning are mandatory scenarios. In this work, clinical, laboratory radiomic features were used train for COVID-19 prognosis prediction. Using Explainable AI algorithms, a multi-level explainable method was proposed taking into account developer involved stakeholder (physician, patient) perspectives. A total 1023 extracted 1589 Chest X-Ray images (CXR), combined with 38 clinical/laboratory features. After pre-processing selection phases, 40 CXR 23 Support Vector Machine Random Forest classifiers exploring three feature strategies. combination both radiomic, enabled higher performance resulting models. intelligibility allowed us validate models' findings. According medical literature, LDH, PaO2 CRP most predictive Instead, ZoneEntropy HighGrayLevelZoneEmphasis - indicative heterogeneity/uniformity texture discriminating Our best model, exploiting classifier signature composed features, achieved AUC=0.819, accuracy=0.733, specificity=0.705, sensitivity=0.761 test set. including explainability, allows make strong assumptions, confirmed by literature insights.

Language: Английский

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions DOI
Arash Heidari, Nima Jafari Navimipour, Mehmet Ünal

et al.

Computers in Biology and Medicine, Journal Year: 2021, Volume and Issue: 141, P. 105141 - 105141

Published: Dec. 14, 2021

Language: Английский

Citations

77
A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest DOI Creative Commons
Mehrdad Rostami, Mourad Oussalah

Informatics in Medicine Unlocked, Journal Year: 2022, Volume and Issue: 30, P. 100941 - 100941

Published: Jan. 1, 2022

Several Artificial Intelligence-based models have been developed for COVID-19 disease diagnosis. In spite of the promise artificial intelligence, there are very few which bridge gap between traditional human-centered diagnosis and potential future machine-centered Under concept human-computer interaction design, this study proposes a new explainable intelligence method that exploits graph analysis feature visualization optimization purpose from blood test samples. model, an decision forest classifier is employed to classification based on routinely available patient data. The approach enables clinician use tree guide explainability interpretability prediction model. By utilizing novel selection phase, proposed model will not only improve accuracy but decrease execution time as well.

Language: Английский

Citations

56
Exploring the role of artificial intelligence in building production resilience: learnings from the COVID-19 pandemic DOI
Vishwas Dohale, Milind Akarte,

Angappa Gunasekaran

et al.

International Journal of Production Research, Journal Year: 2022, Volume and Issue: 62(15), P. 5472 - 5488

Published: Oct. 9, 2022

The ever-happening disruptive events interrupt the operationalisation of manufacturing organisations resulting in stalling production flow and depleting societies with products. Advancements cutting-edge technologies, viz. blockchain, artificial intelligence, virtual reality, digital twin, etc. have attracted practitioners' attention to overcome such saddled conditions. This study attempts explore role intelligence (AI) building resilience function at during a COVID-19 pandemic. In this regard, decision support system comprising an integrated voting analytical hierarchy process (VAHP) Bayesian network (BN) method is developed. Initially, through comprehensive literature review, critical success factors (CSFs) for implementing AI are determined. Further, using multi-criteria decision-making (MCDM) based VAHP, CSFs prioritised determine prominent ones. Finally, machine learning BN adopted predict understand influential that help achieve highest resilience. present research one early know essence bridge interplay between COVID-19. can academicians, practitioners, decision-makers assessing adoption evaluate impact different on

Language: Английский

Citations

55
An integrated framework of data-driven, metaheuristic, and mechanistic modeling approach for biomass pyrolysis DOI
Zahid Ullah, Muzammil Khan, Salman Raza Naqvi

et al.

Process Safety and Environmental Protection, Journal Year: 2022, Volume and Issue: 162, P. 337 - 345

Published: April 12, 2022

Language: Английский

Citations

43
Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction DOI
Zeeshan Haq, Hafeez Ullah, Muhammad Nouman Aslam Khan

et al.

Bioresource Technology, Journal Year: 2022, Volume and Issue: 363, P. 128008 - 128008

Published: Sept. 22, 2022

Language: Английский

Citations

42
Prediction of hydrogen yield from supercritical gasification process of sewage sludge using machine learning and particle swarm hybrid strategy DOI
Muhammad Nouman Aslam Khan, Zeeshan Haq, Hafeez Ullah

et al.

International Journal of Hydrogen Energy, Journal Year: 2023, Volume and Issue: 54, P. 512 - 525

Published: Jan. 25, 2023

Language: Английский

Citations

39
Optimization based comparative study of machine learning methods for the prediction of bio-oil produced from microalgae via pyrolysis DOI
Hafeez Ullah, Zeeshan Haq, Salman Raza Naqvi

et al.

Journal of Analytical and Applied Pyrolysis, Journal Year: 2023, Volume and Issue: 170, P. 105879 - 105879

Published: Jan. 20, 2023

Language: Английский

Citations

34
Recent Advances of Artificial Intelligence in Healthcare: A Systematic Literature Review DOI Creative Commons
Fotis Kitsios, Maria Kamariotou, Aristomenis Syngelakis

et al.

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(13), P. 7479 - 7479

Published: June 25, 2023

The implementation of artificial intelligence (AI) is driving significant transformation inside the administrative and clinical workflows healthcare organizations at an accelerated rate. This modification highlights impact that AI has on a variety tasks, especially in health procedures relating to early detection diagnosis. Papers done past imply potential increase overall quality services provided industry. There have been reports technology based can improve human existence by making life simpler, safer, more productive. A comprehensive analysis previous scholarly research use area this form literature review. In order propose classification framework, review took into consideration 132 academic publications sourced from sources. presentation covers both benefits issues capabilities provide for individuals, medical professionals, corporations, addition, social ethical implications are examined context output value-added decision-making processes healthcare, privacy security measures patient data, monitoring capabilities.

Language: Английский

Citations

33
COVID-19 detection and analysis from lung CT images using novel channel boosted CNNs DOI Creative Commons
Saddam Hussain Khan, Javed Iqbal,

Syed Agha Hassnain

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 229, P. 120477 - 120477

Published: May 17, 2023

In December 2019, the global pandemic COVID-19 in Wuhan, China, affected human life and worldwide economy. Therefore, an efficient diagnostic system is required to control its spread. However, automatic poses challenges with a limited amount of labeled data, minor contrast variation, high structural similarity between infection background. this regard, new two-phase deep convolutional neural network (CNN) based proposed detect minute irregularities analyze infection. first phase, novel SB-STM-BRNet CNN developed, incorporating channel Squeezed Boosted (SB) dilated convolutional-based Split-Transform-Merge (STM) block infected lung CT images. The STM blocks performed multi-path region-smoothing boundary operations, which helped learn variation specific patterns. Furthermore, diverse boosted channels are achieved using SB Transfer Learning concepts texture COVID-19-specific healthy second images provided COVID-CB-RESeg segmentation identify infectious regions. methodically employed region-homogeneity heterogeneity operations each encoder-decoder boosted-decoder auxiliary simultaneously low illumination boundaries region. yields good performance terms accuracy: 98.21 %, F-score: 98.24%, Dice Similarity: 96.40 IOU: 98.85 % for would reduce burden strengthen radiologist's decision fast accurate diagnosis.

Language: Английский

Citations

31
Application of artificial intelligence (AI) to control COVID-19 pandemic: Current status and future prospects DOI Creative Commons
Sumel Ashique, Neeraj Mishra, Sourav Mohanto

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(4), P. e25754 - e25754

Published: Feb. 1, 2024

The impact of the coronavirus disease 2019 (COVID-19) pandemic on everyday livelihood people has been monumental and unparalleled. Although vastly affected global healthcare system, it also a platform to promote develop pioneering applications based autonomic artificial intelligence (AI) technology with therapeutic significance in combating pandemic. Artificial successfully demonstrated that can reduce probability human-to-human infectivity virus through evaluation, analysis, triangulation existing data spread virus. This review talks about modern robotic automated systems may assist spreading In addition, this study discusses intelligent wearable devices how they could be helpful throughout COVID-19

Language: Английский

Citations

9