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Wang Y, Liu S, Spiteri AG, Huynh ALH, Chu C, Masters CL, Goudey B, Pan Y, Jin L. Understanding machine learning applications in dementia research and clinical practice: a review for biomedical scientists and clinicians. Alzheimers Res Ther 2024; 16:175. [PMID: 39085973 PMCID: PMC11293066 DOI: 10.1186/s13195-024-01540-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 07/21/2024] [Indexed: 08/02/2024]
Abstract
Several (inter)national longitudinal dementia observational datasets encompassing demographic information, neuroimaging, biomarkers, neuropsychological evaluations, and muti-omics data, have ushered in a new era of potential for integrating machine learning (ML) into dementia research and clinical practice. ML, with its proficiency in handling multi-modal and high-dimensional data, has emerged as an innovative technique to facilitate early diagnosis, differential diagnosis, and to predict onset and progression of mild cognitive impairment and dementia. In this review, we evaluate current and potential applications of ML, including its history in dementia research, how it compares to traditional statistics, the types of datasets it uses and the general workflow. Moreover, we identify the technical barriers and challenges of ML implementations in clinical practice. Overall, this review provides a comprehensive understanding of ML with non-technical explanations for broader accessibility to biomedical scientists and clinicians.
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Affiliation(s)
- Yihan Wang
- The Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville, VIC, 3052, Australia
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, 30 Royal Parade, Parkville, VIC, 3052, Australia
| | - Shu Liu
- The Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville, VIC, 3052, Australia
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, 30 Royal Parade, Parkville, VIC, 3052, Australia
- The ARC Training Centre in Cognitive Computing for Medical Technologies, The University of Melbourne, Carlton, VIC, 3010, Australia
| | - Alanna G Spiteri
- The Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville, VIC, 3052, Australia
| | - Andrew Liem Hieu Huynh
- Department of Aged Care, Austin Health, Heidelberg, VIC, 3084, Australia
- Department of Medicine, Austin Health, University of Melbourne, Heidelberg, VIC, 3084, Australia
| | - Chenyin Chu
- The Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville, VIC, 3052, Australia
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, 30 Royal Parade, Parkville, VIC, 3052, Australia
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville, VIC, 3052, Australia
| | - Benjamin Goudey
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, 30 Royal Parade, Parkville, VIC, 3052, Australia
- The ARC Training Centre in Cognitive Computing for Medical Technologies, The University of Melbourne, Carlton, VIC, 3010, Australia
| | - Yijun Pan
- The Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville, VIC, 3052, Australia.
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, 30 Royal Parade, Parkville, VIC, 3052, Australia.
| | - Liang Jin
- The Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville, VIC, 3052, Australia
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, 30 Royal Parade, Parkville, VIC, 3052, Australia
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Poonia RC, Al-Alshaikh HA. Ensemble approach of transfer learning and vision transformer leveraging explainable AI for disease diagnosis: An advancement towards smart healthcare 5.0. Comput Biol Med 2024; 179:108874. [PMID: 39013343 DOI: 10.1016/j.compbiomed.2024.108874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 05/29/2024] [Accepted: 06/18/2024] [Indexed: 07/18/2024]
Abstract
Smart healthcare has advanced the medical industry with the integration of data-driven approaches. Artificial intelligence and machine learning provided remarkable progress, but there is a lack of transparency and interpretability in such applications. To overcome such limitations, explainable AI (EXAI) provided a promising result. This paper applied the EXAI for disease diagnosis in the advancement of smart healthcare. The paper combined the approach of transfer learning, vision transformer, and explainable AI and designed an ensemble approach for prediction of disease and its severity. The result is evaluated on a dataset of Alzheimer's disease. The result analysis compared the performance of transfer learning models with the ensemble model of transfer learning and vision transformer. For training, InceptionV3, VGG19, Resnet50, and Densenet121 transfer learning models were selected for ensembling with vision transformer. The result compares the performance of two models: a transfer learning (TL) model and an ensemble transfer learning (Ensemble TL) model combined with vision transformer (ViT) on ADNI dataset. For the TL model, the accuracy is 58 %, precision is 52 %, recall is 42 %, and the F1-score is 44 %. Whereas, the Ensemble TL model with ViT shows significantly improved performance i.e., 96 % of accuracy, 94 % of precision, 90 % of recall and 92 % of F1-score on ADNI dataset. This shows the efficacy of the ensemble model over transfer learning models.
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Affiliation(s)
- Ramesh Chandra Poonia
- Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, 201003, India.
| | - Halah A Al-Alshaikh
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), 11432, Riyadh, Saudi Arabia.
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Zhang H, Yang YF, Song XL, Hu HJ, Yang YY, Zhu X, Yang C. An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study. BMC Med Imaging 2024; 24:170. [PMID: 38982357 PMCID: PMC11234657 DOI: 10.1186/s12880-024-01352-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Accepted: 07/01/2024] [Indexed: 07/11/2024] Open
Abstract
OBJECTIVES To develop and validate a novel interpretable artificial intelligence (AI) model that integrates radiomic features, deep learning features, and imaging features at multiple semantic levels to predict the prognosis of intracerebral hemorrhage (ICH) patients at 6 months post-onset. MATERIALS AND METHODS Retrospectively enrolled 222 patients with ICH for Non-contrast Computed Tomography (NCCT) images and clinical data, who were divided into a training cohort (n = 186, medical center 1) and an external testing cohort (n = 36, medical center 2). Following image preprocessing, the entire hematoma region was segmented by two radiologists as the volume of interest (VOI). Pyradiomics algorithm library was utilized to extract 1762 radiomics features, while a deep convolutional neural network (EfficientnetV2-L) was employed to extract 1000 deep learning features. Additionally, radiologists evaluated imaging features. Based on the three different modalities of features mentioned above, the Random Forest (RF) model was trained, resulting in three models (Radiomics Model, Radiomics-Clinical Model, and DL-Radiomics-Clinical Model). The performance and clinical utility of the models were assessed using the Area Under the Receiver Operating Characteristic Curve (AUC), calibration curve, and Decision Curve Analysis (DCA), with AUC compared using the DeLong test. Furthermore, this study employs three methods, Shapley Additive Explanations (SHAP), Grad-CAM, and Guided Grad-CAM, to conduct a multidimensional interpretability analysis of model decisions. RESULTS The Radiomics-Clinical Model and DL-Radiomics-Clinical Model exhibited relatively good predictive performance, with an AUC of 0.86 [95% Confidence Intervals (CI): 0.71, 0.95; P < 0.01] and 0.89 (95% CI: 0.74, 0.97; P < 0.01), respectively, in the external testing cohort. CONCLUSION The multimodal explainable AI model proposed in this study can accurately predict the prognosis of ICH. Interpretability methods such as SHAP, Grad-CAM, and Guided Grad-Cam partially address the interpretability limitations of AI models. Integrating multimodal imaging features can effectively improve the performance of the model. CLINICAL RELEVANCE STATEMENT Predicting the prognosis of patients with ICH is a key objective in emergency care. Accurate and efficient prognostic tools can effectively prevent, manage, and monitor adverse events in ICH patients, maximizing treatment outcomes.
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Affiliation(s)
- Hao Zhang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, 116000, Liaoning, China
| | - Yun-Feng Yang
- Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
- Laboratory for Medical Imaging Informatics, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xue-Lin Song
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, 116027, Liaoning, China
| | - Hai-Jian Hu
- Department of Hemato-oncology, The First Hospital of Changsha, Changsha, 410005, Hunan, China
| | - Yuan-Yuan Yang
- Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
- Laboratory for Medical Imaging Informatics, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xia Zhu
- Department of Gynecology, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, 410028, Hunan, China
| | - Chao Yang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, 116000, Liaoning, China.
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Zhao E, Yang YF, Bai M, Zhang H, Yang YY, Song X, Lou S, Yu Y, Yang C. MRI radiomics-based interpretable model and nomogram for preoperative prediction of Ki-67 expression status in primary central nervous system lymphoma. Front Med (Lausanne) 2024; 11:1345162. [PMID: 38994341 PMCID: PMC11236568 DOI: 10.3389/fmed.2024.1345162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 06/11/2024] [Indexed: 07/13/2024] Open
Abstract
Objectives To investigate the value of interpretable machine learning model and nomogram based on clinical factors, MRI imaging features, and radiomic features to predict Ki-67 expression in primary central nervous system lymphomas (PCNSL). Materials and methods MRI images and clinical information of 92 PCNSL patients were retrospectively collected, which were divided into 53 cases in the training set and 39 cases in the external validation set according to different medical centers. A 3D brain tumor segmentation model was trained based on nnU-NetV2, and two prediction models, interpretable Random Forest (RF) incorporating the SHapley Additive exPlanations (SHAP) method and nomogram based on multivariate logistic regression, were proposed for the task of Ki-67 expression status prediction. Results The mean dice Similarity Coefficient (DSC) score of the 3D segmentation model on the validation set was 0.85. On the Ki-67 expression prediction task, the AUC of the interpretable RF model on the validation set was 0.84 (95% CI:0.81, 0.86; p < 0.001), which was a 3% improvement compared to the AUC of the nomogram. The Delong test showed that the z statistic for the difference between the two models was 1.901, corresponding to a p value of 0.057. In addition, SHAP analysis showed that the Rad-Score made a significant contribution to the model decision. Conclusion In this study, we developed a 3D brain tumor segmentation model and used an interpretable machine learning model and nomogram for preoperative prediction of Ki-67 expression status in PCNSL patients, which improved the prediction of this medical task. Clinical relevance statement Ki-67 represents the degree of active cell proliferation and is an important prognostic parameter associated with clinical outcomes. Non-invasive and accurate prediction of Ki-67 expression level preoperatively plays an important role in targeting treatment selection and patient stratification management for PCNSL thereby improving prognosis.
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Affiliation(s)
- Endong Zhao
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Yun-Feng Yang
- Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
- Laboratory for Medical Imaging Informatics, University of Chinese Academy of Sciences, Beijing, China
| | - Miaomiao Bai
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Hao Zhang
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Yuan-Yuan Yang
- Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
- Laboratory for Medical Imaging Informatics, University of Chinese Academy of Sciences, Beijing, China
| | - Xuelin Song
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Shiyun Lou
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Yunxuan Yu
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Chao Yang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
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Yang YF, Zhang H, Song XL, Yang C, Hu HJ, Fang TS, Zhang ZH, Zhu X, Yang YY. Predicting Outcome of Patients With Cerebral Hemorrhage Using a Computed Tomography-Based Interpretable Radiomics Model: A Multicenter Study. J Comput Assist Tomogr 2024:00004728-990000000-00331. [PMID: 38924426 DOI: 10.1097/rct.0000000000001627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Abstract
OBJECTIVE The aim of this study was to develop and validate an interpretable and highly generalizable multimodal radiomics model for predicting the prognosis of patients with cerebral hemorrhage. METHODS This retrospective study involved 237 patients with cerebral hemorrhage from 3 medical centers, of which a training cohort of 186 patients (medical center 1) was selected and 51 patients from medical center 2 and medical center 3 were used as an external testing cohort. A total of 1762 radiomics features were extracted from nonenhanced computed tomography using Pyradiomics, and the relevant macroscopic imaging features and clinical factors were evaluated by 2 experienced radiologists. A radiomics model was established based on radiomics features using the random forest algorithm, and a radiomics-clinical model was further trained by combining radiomics features, clinical factors, and macroscopic imaging features. The performance of the models was evaluated using area under the curve (AUC), sensitivity, specificity, and calibration curves. Additionally, a novel SHAP (SHAPley Additive exPlanations) method was used to provide quantitative interpretability analysis for the optimal model. RESULTS The radiomics-clinical model demonstrated superior predictive performance overall, with an AUC of 0.88 (95% confidence interval, 0.76-0.95; P < 0.01). Compared with the radiomics model (AUC, 0.85; 95% confidence interval, 0.72-0.94; P < 0.01), there was a 0.03 improvement in AUC. Furthermore, SHAP analysis revealed that the fusion features, rad score and clinical rad score, made significant contributions to the model's decision-making process. CONCLUSION Both proposed prognostic models for cerebral hemorrhage demonstrated high predictive levels, and the addition of macroscopic imaging features effectively improved the prognostic ability of the radiomics-clinical model. The radiomics-clinical model provides a higher level of predictive performance and model decision-making basis for the risk prognosis of cerebral hemorrhage.
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Affiliation(s)
| | - Hao Zhang
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, Shanghai
| | - Xue-Lin Song
- Department of Radiology, the Second Affiliated Hospital of Dalian Medical University
| | - Chao Yang
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning
| | - Hai-Jian Hu
- Department of Hemato-oncology, the First Hospital of Changsha
| | | | | | - Xia Zhu
- Department of Gynecology, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, Hunan, China
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Zhang H, Yang YF, Yang C, Yang YY, He XH, Chen C, Song XL, Ying LL, Wang Y, Xu LC, Li WT. A Novel Interpretable Radiomics Model to Distinguish Nodular Goiter From Malignant Thyroid Nodules. J Comput Assist Tomogr 2024; 48:334-342. [PMID: 37757802 DOI: 10.1097/rct.0000000000001544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
OBJECTIVES The purpose of this study is to inquire about the potential association between radiomics features and the pathological nature of thyroid nodules (TNs), and to propose an interpretable radiomics-based model for predicting the risk of malignant TN. METHODS In this retrospective study, computed tomography (CT) imaging and pathological data from 141 patients with TN were collected. The data were randomly stratified into a training group (n = 112) and a validation group (n = 29) at a ratio of 4:1. A total of 1316 radiomics features were extracted by using the pyradiomics tool. The redundant features were removed through correlation testing, and the least absolute shrinkage and selection operator (LASSO) or the minimum redundancy maximum relevance standard was used to select features. Finally, 4 different machine learning models (RF Hybrid Feature, SVM Hybrid Feature, RF, and LASSO) were constructed. The performance of the 4 models was evaluated using the receiver operating characteristic curve. The calibration curve, decision curve analysis, and SHapley Additive exPlanations method were used to evaluate or explain the best radiomics machine learning model. RESULTS The optimal radiomics model (RF Hybrid Feature model) demonstrated a relatively high degree of discrimination with an area under the receiver operating characteristic curve (AUC) of 0.87 (95% CI, 0.70-0.97; P < 0.001) for the validation cohort. Compared with the commonly used LASSO model (AUC, 0.78; 95% CI, 0.60-0.91; P < 0.01), there is a significant improvement in AUC in the validation set, net reclassification improvement, 0.79 (95% CI, 0.13-1.46; P < 0.05), and integrated discrimination improvement, 0. 20 (95% CI, 0.10-0.30; P < 0.001). CONCLUSION The interpretable radiomics model based on CT performs well in predicting benign and malignant TNs by using quantitative radiomics features of the unilateral total thyroid. In addition, the data preprocessing method incorporating different layers of features has achieved excellent experimental results. CLINICAL RELEVANCE STATEMENT As the detection rate of TNs continues to increase, so does the diagnostic burden on radiologists. This study establishes a noninvasive, interpretable and accurate machine learning model to rapidly identify the nature of TN found in CT.
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Affiliation(s)
- Hao Zhang
- From the Department of Interventional Radiology, Fudan University Shanghai Cancer Center
| | | | - Chao Yang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University
| | | | | | | | - Xue-Lin Song
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
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Fan Y, Lu X, Sun G. IHCP: interpretable hepatitis C prediction system based on black-box machine learning models. BMC Bioinformatics 2023; 24:333. [PMID: 37674125 PMCID: PMC10481489 DOI: 10.1186/s12859-023-05456-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 08/29/2023] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND Hepatitis C is a prevalent disease that poses a high risk to the human liver. Early diagnosis of hepatitis C is crucial for treatment and prognosis. Therefore, developing an effective medical decision system is essential. In recent years, many computational methods have been proposed to identify hepatitis C patients. Although existing hepatitis prediction models have achieved good results in terms of accuracy, most of them are black-box models and cannot gain the trust of doctors and patients in clinical practice. As a result, this study aims to use various Machine Learning (ML) models to predict whether a patient has hepatitis C, while also using explainable models to elucidate the prediction process of the ML models, thus making the prediction process more transparent. RESULT We conducted a study on the prediction of hepatitis C based on serological testing and provided comprehensive explanations for the prediction process. Throughout the experiment, we modeled the benchmark dataset, and evaluated model performance using fivefold cross-validation and independent testing experiments. After evaluating three types of black-box machine learning models, Random Forest (RF), Support Vector Machine (SVM), and AdaBoost, we adopted Bayesian-optimized RF as the classification algorithm. In terms of model interpretation, in addition to using common SHapley Additive exPlanations (SHAP) to provide global explanations for the model, we also utilized the Local Interpretable Model-Agnostic Explanations with stability (LIME_stabilitly) to provide local explanations for the model. CONCLUSION Both the fivefold cross-validation and independent testing show that our proposed method significantly outperforms the state-of-the-art method. IHCP maintains excellent model interpretability while obtaining excellent predictive performance. This helps uncover potential predictive patterns of the model and enables clinicians to better understand the model's decision-making process.
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Affiliation(s)
- Yongxian Fan
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China.
| | - Xiqian Lu
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Guicong Sun
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
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Lalithadevi B, Krishnaveni S, Gnanadurai JSC. A Feasibility Study of Diabetic Retinopathy Detection in Type II Diabetic Patients Based on Explainable Artificial Intelligence. J Med Syst 2023; 47:85. [PMID: 37552340 DOI: 10.1007/s10916-023-01976-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 07/12/2023] [Indexed: 08/09/2023]
Abstract
Diabetic retinopathy (DR) is vision impairment and a life-threatening condition for diabetic patients. Especially type II diabetic people have higher chances of getting retinal problems. Hence, early prediction of DR is necessary for preventing the diabetic patients from vision impairment. The main aim of this feasibility study is to identify the most critical risk features that could lead to diabetic retinopathy. This study investigated type II diabetic patients' socio-analytical, diabetes, behavioral, and clinical risk factors. We conducted a self-individual questionnaire session for all participants. Our questionnaire asked about the reliability of results, feeling comfortable during the screening test, willingness to participate in future screenings, overall perspective, and satisfaction with the DR screening test. We proposed a random forest model for predicting the prevalence of DR risk among diabetics. Further explanations of the model were conducted using more robust SHAP eXplainable Artificial Intelligence (XAI) tools. The SHAP method makes it possible to understand how input variables interact with their representative output records, as well as how input variables are ranked. In addition, various descriptive and inferential statistical analyses were performed on the data and evaluated the significant relationship between the factors discussed above via hypothesis testing. This feasibility study involved 172 type II diabetic patients (73 males and 99 females). Therefore, we found that 81 (47.09%) out of 172 participants had referable DR. The average age of the patients was determined as 55.08, with a standard deviation of ± 9.770 (ranging from 40 to 79). Type II patients were affected by mild, moderate, severe, and advanced proliferative diabetic retinopathy (PDR) stages with 23.83%, 13.95%, 5.81%, and 3.48%, respectively, of the total samples. The developed RF model obtained high accuracy of 94.9% using clinical dataset. Our results showed that the formation of tiny microminiature lesions was noticeable in type II diabetic patients with aged people, abnormal blood glucose levels, and prolonged diabetes duration.
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Affiliation(s)
- B Lalithadevi
- Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, Chennai, TN, India.
| | - S Krishnaveni
- Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, Chennai, TN, India
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Nayebi A, Tipirneni S, Reddy CK, Foreman B, Subbian V. WindowSHAP: An efficient framework for explaining time-series classifiers based on Shapley values. J Biomed Inform 2023; 144:104438. [PMID: 37414368 PMCID: PMC10552726 DOI: 10.1016/j.jbi.2023.104438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 06/29/2023] [Accepted: 07/03/2023] [Indexed: 07/08/2023]
Abstract
Unpacking and comprehending how black-box machine learning algorithms (such as deep learning models) make decisions has been a persistent challenge for researchers and end-users. Explaining time-series predictive models is useful for clinical applications with high stakes to understand the behavior of prediction models, e.g., to determine how different variables and time points influence the clinical outcome. However, existing approaches to explain such models are frequently unique to architectures and data where the features do not have a time-varying component. In this paper, we introduce WindowSHAP, a model-agnostic framework for explaining time-series classifiers using Shapley values. We intend for WindowSHAP to mitigate the computational complexity of calculating Shapley values for long time-series data as well as improve the quality of explanations. WindowSHAP is based on partitioning a sequence into time windows. Under this framework, we present three distinct algorithms of Stationary, Sliding and Dynamic WindowSHAP, each evaluated against baseline approaches, KernelSHAP and TimeSHAP, using perturbation and sequence analyses metrics. We applied our framework to clinical time-series data from both a specialized clinical domain (Traumatic Brain Injury - TBI) as well as a broad clinical domain (critical care medicine). The experimental results demonstrate that, based on the two quantitative metrics, our framework is superior at explaining clinical time-series classifiers, while also reducing the complexity of computations. We show that for time-series data with 120 time steps (hours), merging 10 adjacent time points can reduce the CPU time of WindowSHAP by 80 % compared to KernelSHAP. We also show that our Dynamic WindowSHAP algorithm focuses more on the most important time steps and provides more understandable explanations. As a result, WindowSHAP not only accelerates the calculation of Shapley values for time-series data, but also delivers more understandable explanations with higher quality.
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Affiliation(s)
- Amin Nayebi
- Department of Systems and Industrial Engineering, University of Arizona, AZ, USA.
| | | | | | | | - Vignesh Subbian
- Department of Systems and Industrial Engineering, University of Arizona, AZ, USA; Department of Biomedical Engineering, University of Arizona, AZ, USA
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Soria C, Arroyo Y, Torres AM, Redondo MÁ, Basar C, Mateo J. Method for Classifying Schizophrenia Patients Based on Machine Learning. J Clin Med 2023; 12:4375. [PMID: 37445410 DOI: 10.3390/jcm12134375] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/21/2023] [Accepted: 06/27/2023] [Indexed: 07/15/2023] Open
Abstract
Schizophrenia is a chronic and severe mental disorder that affects individuals in various ways, particularly in their ability to perceive, process, and respond to stimuli. This condition has a significant impact on a considerable number of individuals. Consequently, the study, analysis, and characterization of this pathology are of paramount importance. Electroencephalography (EEG) is frequently utilized in the diagnostic assessment of various brain disorders due to its non-intrusiveness, excellent resolution and ease of placement. However, the manual analysis of electroencephalogram (EEG) recordings can be a complex and time-consuming task for healthcare professionals. Therefore, the automated analysis of EEG recordings can help alleviate the burden on doctors and provide valuable insights to support clinical diagnosis. Many studies are working along these lines. In this research paper, the authors propose a machine learning (ML) method based on the eXtreme Gradient Boosting (XGB) algorithm for analyzing EEG signals. The study compares the performance of the proposed XGB-based approach with four other supervised ML systems. According to the results, the proposed XGB-based method demonstrates superior performance, with an AUC value of 0.94 and an accuracy value of 0.94, surpassing the other compared methods. The implemented system exhibits high accuracy and robustness in accurately classifying schizophrenia patients based on EEG recordings. This method holds the potential to be implemented as a valuable complementary tool for clinical use in hospitals, supporting clinicians in their clinical diagnosis of schizophrenia.
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Affiliation(s)
- Carmen Soria
- Institute of Technology, University of Castilla-La Mancha, 16071 Cuenca, Spain
- Clinical Neurophysiology Service, Virgen de la Luz Hospital, 16002 Cuenca, Spain
| | - Yoel Arroyo
- Faculty of Social Sciences and Information Technology, University of Castilla-La Mancha, 45600 Talavera de la Reina, Spain
| | - Ana María Torres
- Institute of Technology, University of Castilla-La Mancha, 16071 Cuenca, Spain
| | - Miguel Ángel Redondo
- School of Informatics, University of Castilla-La Mancha, 13071 Ciudad Real, Spain
| | - Christoph Basar
- Faculty of Human and Health Sciences, University of Bremen, 28359 Bremen, Germany
| | - Jorge Mateo
- Institute of Technology, University of Castilla-La Mancha, 16071 Cuenca, Spain
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Wang L, Zhang W. A qualitatively analyzable two-stage ensemble model based on machine learning for credit risk early warning: Evidence from Chinese manufacturing companies. Inf Process Manag 2023. [DOI: 10.1016/j.ipm.2023.103267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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Li F, Chen A, Li Z, Gu L, Pan Q, Wang P, Fan Y, Feng J. Machine learning-based prediction of cerebral hemorrhage in patients with hemodialysis: A multicenter, retrospective study. Front Neurol 2023; 14:1139096. [PMID: 37077571 PMCID: PMC10109449 DOI: 10.3389/fneur.2023.1139096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 03/08/2023] [Indexed: 04/05/2023] Open
Abstract
BackgroundIntracerebral hemorrhage (ICH) is one of the most serious complications in patients with chronic kidney disease undergoing long-term hemodialysis. It has high mortality and disability rates and imposes a serious economic burden on the patient's family and society. An early prediction of ICH is essential for timely intervention and improving prognosis. This study aims to build an interpretable machine learning-based model to predict the risk of ICH in patients undergoing hemodialysis.MethodsThe clinical data of 393 patients with end-stage kidney disease undergoing hemodialysis at three different centers between August 2014 and August 2022 were retrospectively analyzed. A total of 70% of the samples were randomly selected as the training set, and the remaining 30% were used as the validation set. Five machine learning (ML) algorithms, namely, support vector machine (SVM), extreme gradient boosting (XGB), complement Naïve Bayes (CNB), K-nearest neighbor (KNN), and logistic regression (LR), were used to develop a model to predict the risk of ICH in patients with uremia undergoing long-term hemodialysis. In addition, the area under the curve (AUC) values were evaluated to compare the performance of each algorithmic model. Global and individual interpretive analyses of the model were performed using importance ranking and Shapley additive explanations (SHAP) in the training set.ResultsA total of 73 patients undergoing hemodialysis developed spontaneous ICH among the 393 patients included in the study. The AUC of SVM, CNB, KNN, LR, and XGB models in the validation dataset were 0.725 (95% CI: 0.610 ~ 0.841), 0.797 (95% CI: 0.690 ~ 0.905), 0.675 (95% CI: 0.560 ~ 0.789), 0.922 (95% CI: 0.862 ~ 0.981), and 0.979 (95% CI: 0.953 ~ 1.000), respectively. Therefore, the XGBoost model had the best performance among the five algorithms. SHAP analysis revealed that the levels of LDL, HDL, CRP, and HGB and pre-hemodialysis blood pressure were the most important factors.ConclusionThe XGB model developed in this study can efficiently predict the risk of a cerebral hemorrhage in patients with uremia undergoing long-term hemodialysis and can help clinicians to make more individualized and rational clinical decisions. ICH events in patients undergoing maintenance hemodialysis (MHD) are associated with serum LDL, HDL, CRP, HGB, and pre-hemodialysis SBP levels.
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Affiliation(s)
- Fengda Li
- Department of Neurosurgery, Changshu Hospital Affiliated to Soochow University, Changshu, China
| | - Anmin Chen
- Department of Nephrology, The First People's Hospital of Jintan, Changzhou, China
| | - Zeyi Li
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Longyuan Gu
- Department of Neurosurgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Qiyang Pan
- Faculty of Informatics, Università della Svizzera italiana, Lugano, Ticino, Switzerland
| | - Pan Wang
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Yuechao Fan
- Department of Neurosurgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
- *Correspondence: Yuechao Fan
| | - Jinhong Feng
- Department of Nephrology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
- Jinhong Feng
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Alkhalaf S, Alturise F, Bahaddad AA, Elnaim BME, Shabana S, Abdel-Khalek S, Mansour RF. Adaptive Aquila Optimizer with Explainable Artificial Intelligence-Enabled Cancer Diagnosis on Medical Imaging. Cancers (Basel) 2023; 15:cancers15051492. [PMID: 36900283 PMCID: PMC10001070 DOI: 10.3390/cancers15051492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 02/19/2023] [Accepted: 02/20/2023] [Indexed: 03/08/2023] Open
Abstract
Explainable Artificial Intelligence (XAI) is a branch of AI that mainly focuses on developing systems that provide understandable and clear explanations for their decisions. In the context of cancer diagnoses on medical imaging, an XAI technology uses advanced image analysis methods like deep learning (DL) to make a diagnosis and analyze medical images, as well as provide a clear explanation for how it arrived at its diagnoses. This includes highlighting specific areas of the image that the system recognized as indicative of cancer while also providing data on the fundamental AI algorithm and decision-making process used. The objective of XAI is to provide patients and doctors with a better understanding of the system's decision-making process and to increase transparency and trust in the diagnosis method. Therefore, this study develops an Adaptive Aquila Optimizer with Explainable Artificial Intelligence Enabled Cancer Diagnosis (AAOXAI-CD) technique on Medical Imaging. The proposed AAOXAI-CD technique intends to accomplish the effectual colorectal and osteosarcoma cancer classification process. To achieve this, the AAOXAI-CD technique initially employs the Faster SqueezeNet model for feature vector generation. As well, the hyperparameter tuning of the Faster SqueezeNet model takes place with the use of the AAO algorithm. For cancer classification, the majority weighted voting ensemble model with three DL classifiers, namely recurrent neural network (RNN), gated recurrent unit (GRU), and bidirectional long short-term memory (BiLSTM). Furthermore, the AAOXAI-CD technique combines the XAI approach LIME for better understanding and explainability of the black-box method for accurate cancer detection. The simulation evaluation of the AAOXAI-CD methodology can be tested on medical cancer imaging databases, and the outcomes ensured the auspicious outcome of the AAOXAI-CD methodology than other current approaches.
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Affiliation(s)
- Salem Alkhalaf
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass 58892, Saudi Arabia
- Correspondence:
| | - Fahad Alturise
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass 58892, Saudi Arabia
| | - Adel Aboud Bahaddad
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Bushra M. Elamin Elnaim
- Department of Computer Science, College of Science and Humanities in Al-Sulail, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia
| | - Samah Shabana
- Pharmacognosy Department, Faculty of Pharmaceutical Sciences and Drug Manufacturing, Misr University for Science and Technology (MUST), Giza 3236101, Egypt
| | - Sayed Abdel-Khalek
- Department of Mathematics, College of Science, Taif University, Taif 21944, Saudi Arabia
| | - Romany F. Mansour
- Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 1064188, Egypt
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Sachdeva RK, Bathla P, Rani P, Solanki V, Ahuja R. A systematic method for diagnosis of hepatitis disease using machine learning. INNOVATIONS IN SYSTEMS AND SOFTWARE ENGINEERING 2023; 19:71-80. [PMID: 36628173 PMCID: PMC9818056 DOI: 10.1007/s11334-022-00509-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
Hepatitis is among the deadliest diseases on the planet. Machine learning approaches can contribute toward diagnosing hepatitis disease based on a few characteristics. On the UCI dataset, authors assessed distinct classifiers' performance in order to develop a systematic strategy for hepatitis disease diagnosis. The classifiers used are support vector machine, logistic regression (LR), K-nearest neighbor, and random forest. The classifiers were employed without class balancing and in conjunction with class balancing using SMOTE strategy. Both studies, classification without class balancing and with class balancing, were compared in terms of different performance parameters. After adopting class balancing, the efficiency of classifiers improved significantly. LR with SMOTE provided the highest level of accuracy (93.18%).
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Affiliation(s)
- Ravi Kumar Sachdeva
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab India
| | | | - Pooja Rani
- MMICTBM, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana India
| | - Vikas Solanki
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab India
| | - Rakesh Ahuja
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab India
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15
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Nordin N, Zainol Z, Mohd Noor MH, Chan LF. An explainable predictive model for suicide attempt risk using an ensemble learning and Shapley Additive Explanations (SHAP) approach. Asian J Psychiatr 2023; 79:103316. [PMID: 36395702 DOI: 10.1016/j.ajp.2022.103316] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 10/20/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022]
Abstract
Machine learning approaches have been used to develop suicide attempt predictive models recently and have been shown to have a good performance. However, those proposed models have difficulty interpreting and understanding why an individual has suicidal attempts. To overcome this issue, the identification of features such as risk factors in predicting suicide attempts is important for clinicians to make decisions. Therefore, the aim of this study is to propose an explainable predictive model to predict and analyse the importance of features for suicide attempts. This model can also provide explanations to improve the clinical understanding of suicide attempts. Two complex ensemble learning models, namely Random Forest and Gradient Boosting with an explanatory model (SHapley Additive exPlanations (SHAP)) have been constructed. The models are used for predictive interpretation and understanding of the importance of the features. The experiment shows that both models with SHAP are able to interpret and understand the nature of an individual's predictions with suicide attempts. However, compared with Random Forest, the results show that Gradient Boosting with SHAP achieves higher accuracy and the analyses found that history of suicide attempts, suicidal ideation, and ethnicity as the main predictors for suicide attempts.
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Affiliation(s)
- Noratikah Nordin
- School of Computer Sciences, Universiti Sains Malaysia, 11800 USM, Pulau Pinang, Malaysia.
| | - Zurinahni Zainol
- School of Computer Sciences, Universiti Sains Malaysia, 11800 USM, Pulau Pinang, Malaysia.
| | - Mohd Halim Mohd Noor
- School of Computer Sciences, Universiti Sains Malaysia, 11800 USM, Pulau Pinang, Malaysia.
| | - Lai Fong Chan
- Department of Psychiatry, Faculty of Medicine, National University of Malaysia (UKM), 56000 Cheras, Wilayah Persekutuan Kuala Lumpur, Malaysia.
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Loh HW, Ooi CP, Seoni S, Barua PD, Molinari F, Acharya UR. Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011-2022). COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107161. [PMID: 36228495 DOI: 10.1016/j.cmpb.2022.107161] [Citation(s) in RCA: 88] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/16/2022] [Accepted: 09/25/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVES Artificial intelligence (AI) has branched out to various applications in healthcare, such as health services management, predictive medicine, clinical decision-making, and patient data and diagnostics. Although AI models have achieved human-like performance, their use is still limited because they are seen as a black box. This lack of trust remains the main reason for their low use in practice, especially in healthcare. Hence, explainable artificial intelligence (XAI) has been introduced as a technique that can provide confidence in the model's prediction by explaining how the prediction is derived, thereby encouraging the use of AI systems in healthcare. The primary goal of this review is to provide areas of healthcare that require more attention from the XAI research community. METHODS Multiple journal databases were thoroughly searched using PRISMA guidelines 2020. Studies that do not appear in Q1 journals, which are highly credible, were excluded. RESULTS In this review, we surveyed 99 Q1 articles covering the following XAI techniques: SHAP, LIME, GradCAM, LRP, Fuzzy classifier, EBM, CBR, rule-based systems, and others. CONCLUSION We discovered that detecting abnormalities in 1D biosignals and identifying key text in clinical notes are areas that require more attention from the XAI research community. We hope this is review will encourage the development of a holistic cloud system for a smart city.
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Affiliation(s)
- Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Silvia Seoni
- Department of Electronics and Telecommunications, Biolab, Politecnico di Torino, Torino 10129, Italy
| | - Prabal Datta Barua
- Faculty of Engineering and Information Technology, University of Technology Sydney, Australia; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Australia
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Biolab, Politecnico di Torino, Torino 10129, Italy
| | - U Rajendra Acharya
- School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Australia; School of Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan.
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Rahman A, Hossain MS, Muhammad G, Kundu D, Debnath T, Rahman M, Khan MSI, Tiwari P, Band SS. Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues. CLUSTER COMPUTING 2022; 26:1-41. [PMID: 35996680 PMCID: PMC9385101 DOI: 10.1007/s10586-022-03658-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 05/10/2022] [Accepted: 06/17/2022] [Indexed: 06/15/2023]
Abstract
Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.
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Affiliation(s)
- Anichur Rahman
- Present Address: Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka Bangladesh
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Md. Sazzad Hossain
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Ghulam Muhammad
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Dipanjali Kundu
- Present Address: Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka Bangladesh
| | - Tanoy Debnath
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Muaz Rahman
- Present Address: Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka Bangladesh
| | - Md. Saikat Islam Khan
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Prayag Tiwari
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Shahab S. Band
- Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002 Taiwan
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18
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An explainable artificial intelligence approach for financial distress prediction. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.102988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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19
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Zhang W, Ji L, Zhong X, Zhu S, Zhang Y, Ge M, Kang Y, Bi Q. Two Novel Nomograms Predicting the Risk and Prognosis of Pancreatic Cancer Patients With Lung Metastases: A Population-Based Study. Front Public Health 2022; 10:884349. [PMID: 35712294 PMCID: PMC9194823 DOI: 10.3389/fpubh.2022.884349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 05/16/2022] [Indexed: 11/19/2022] Open
Abstract
Background Pancreatic cancer (PC) is one of the most common malignant types of cancer, with the lung being the frequent distant metastatic site. Currently, no population-based studies have been done on the risk and prognosis of pancreatic cancer with lung metastases (PCLM). As a result, we intend to create two novel nomograms to predict the risk and prognosis of PCLM. Methods PC patients were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database from 2010 to 2016. A multivariable logistic regression analysis was used to identify risk factors for PCLM at the time of diagnosis. The multivariate Cox regression analysis was carried out to assess PCLM patient's prognostic factors for overall survival (OS). Following that, we used area under curve (AUC), time-dependent receiver operating characteristics (ROC) curves, calibration plots, consistency index (C-index), time-dependent C-index, and decision curve analysis (DCA) to evaluate the effectiveness and accuracy of the two nomograms. Finally, we compared differences in survival outcomes using Kaplan-Meier curves. Results A total of 803 (4.22%) out of 19,067 pathologically diagnosed PC patients with complete baseline information screened from SEER database had pulmonary metastasis at diagnosis. A multivariable logistic regression analysis revealed that age, histological subtype, primary site, N staging, surgery, radiotherapy, tumor size, bone metastasis, brain metastasis, and liver metastasis were risk factors for the occurrence of PCLM. According to multivariate Cox regression analysis, age, grade, tumor size, histological subtype, surgery, chemotherapy, liver metastasis, and bone metastasis were independent prognostic factors for PCLM patients' OS. Nomograms were constructed based on these factors to predict 6-, 12-, and 18-months OS of patients with PCLM. AUC, C-index, calibration curves, and DCA revealed that the two novel nomograms had good predictive power. Conclusion We developed two reliable predictive models for clinical practice to assist clinicians in developing individualized treatment plans for patients.
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Affiliation(s)
- Wei Zhang
- Department of Orthopedics, Zhejiang Provincial People's Hospital, Qingdao University, Qingdao, China
- Department of Orthopedics, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Lichen Ji
- Department of Orthopedics, Zhejiang Provincial People's Hospital, Hangzhou, China
- Department of Orthopedics, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xugang Zhong
- Department of Orthopedics, Zhejiang Provincial People's Hospital, Qingdao University, Qingdao, China
- Department of Orthopedics, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Senbo Zhu
- Department of Orthopedics, Zhejiang Provincial People's Hospital, Hangzhou, China
- Department of Orthopedics, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yi Zhang
- Department of Orthopedics, Zhejiang Provincial People's Hospital, Qingdao University, Qingdao, China
- Department of Hepatobiliary and Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China
| | - Meng Ge
- Department of Orthopedics, Zhejiang Provincial People's Hospital, Hangzhou, China
- Graduate Department, Bengbu Medical College, Bengbu, China
| | - Yao Kang
- Department of Orthopedics, Zhejiang Provincial People's Hospital, Hangzhou, China
- Department of Orthopedics, Hangzhou Medical College People's Hospital, Hangzhou, China
| | - Qing Bi
- Department of Orthopedics, Zhejiang Provincial People's Hospital, Hangzhou, China
- Department of Orthopedics, Hangzhou Medical College People's Hospital, Hangzhou, China
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20
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Comparison and Determination of Optimal Machine Learning Model for Predicting Generation of Coal Fly Ash. CRYSTALS 2022. [DOI: 10.3390/cryst12040556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The rapid development of industry keeps increasing the demand for energy. Coal, as the main energy source, has a huge level of consumption, resulting in the continuous generation of its combustion byproduct coal fly ash (CFA). The accumulated CFA will occupy a large amount of land, but also cause serious environmental pollution and personal injury, which makes the resource utilization of CFA gradually to be attached importance. However, given the variability of the amount of CFA generation, predicting it in advance is the basis to ensure effective disposal and rational utilization. In this study, CFA generation was taken as the target variable, three machine learning (ML) algorithms were used to construct the model, and four evaluation indices were used to evaluate its performance. The results showed that the DNN model with the R = 0.89, R2 = 0.77 on the testing set performed better than the traditional multiple linear regression equation and other ML algorithms, and the feasibility of DNN as the optimal model framework was demonstrated. Applying this model framework to the engineering field enables managers to identify the next step of the disposal method in advance, so as to rationally allocate ways of recycling and utilization to maximize the use and sales benefits of CFA while minimizing its disposal costs. In addition, sensitivity analysis further explains ML’s internal decisions and verifies that coal consumption is more important than installed capacity, which provides a certain reference for ensuring the rational utilization of CFA.
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Zhang Y, Weng Y, Lund J. Applications of Explainable Artificial Intelligence in Diagnosis and Surgery. Diagnostics (Basel) 2022; 12:diagnostics12020237. [PMID: 35204328 PMCID: PMC8870992 DOI: 10.3390/diagnostics12020237] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/11/2022] [Accepted: 01/13/2022] [Indexed: 02/06/2023] Open
Abstract
In recent years, artificial intelligence (AI) has shown great promise in medicine. However, explainability issues make AI applications in clinical usages difficult. Some research has been conducted into explainable artificial intelligence (XAI) to overcome the limitation of the black-box nature of AI methods. Compared with AI techniques such as deep learning, XAI can provide both decision-making and explanations of the model. In this review, we conducted a survey of the recent trends in medical diagnosis and surgical applications using XAI. We have searched articles published between 2019 and 2021 from PubMed, IEEE Xplore, Association for Computing Machinery, and Google Scholar. We included articles which met the selection criteria in the review and then extracted and analyzed relevant information from the studies. Additionally, we provide an experimental showcase on breast cancer diagnosis, and illustrate how XAI can be applied in medical XAI applications. Finally, we summarize the XAI methods utilized in the medical XAI applications, the challenges that the researchers have met, and discuss the future research directions. The survey result indicates that medical XAI is a promising research direction, and this study aims to serve as a reference to medical experts and AI scientists when designing medical XAI applications.
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Affiliation(s)
- Yiming Zhang
- School of Computer Science, Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China;
- School of Medicine, University of Nottingham, Nottingham NG7 2RD, UK;
| | - Ying Weng
- School of Computer Science, Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China;
- Correspondence:
| | - Jonathan Lund
- School of Medicine, University of Nottingham, Nottingham NG7 2RD, UK;
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22
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Rostami M, Oussalah M. A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest. INFORMATICS IN MEDICINE UNLOCKED 2022; 30:100941. [PMID: 35399333 PMCID: PMC8985417 DOI: 10.1016/j.imu.2022.100941] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 04/01/2022] [Accepted: 04/01/2022] [Indexed: 12/12/2022] Open
Abstract
Several Artificial Intelligence-based models have been developed for COVID-19 disease diagnosis. In spite of the promise of artificial intelligence, there are very few models which bridge the gap between traditional human-centered diagnosis and the potential future of machine-centered disease diagnosis. Under the concept of human-computer interaction design, this study proposes a new explainable artificial intelligence method that exploits graph analysis for feature visualization and optimization for the purpose of COVID-19 diagnosis from blood test samples. In this developed model, an explainable decision forest classifier is employed to COVID-19 classification based on routinely available patient blood test data. The approach enables the clinician to use the decision tree and feature visualization to guide the explainability and interpretability of the prediction model. By utilizing this novel feature selection phase, the proposed diagnosis model will not only improve diagnosis accuracy but decrease the execution time as well.
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Affiliation(s)
- Mehrdad Rostami
- Centre for Machine Vision and Signal Processing, Faculty of Information Technology, University of Oulu, Oulu, Finland
| | - Mourad Oussalah
- Centre for Machine Vision and Signal Processing, Faculty of Information Technology, University of Oulu, Oulu, Finland
- Research Unit of Medical Imaging, Physics, and Technology, Faculty of Medicine, University of Oulu, Finland
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Guo T, Fang Z, Yang G, Zhou Y, Ding N, Peng W, Gong X, He H, Pan X, Chai X. Machine Learning Models for Predicting In-Hospital Mortality in Acute Aortic Dissection Patients. Front Cardiovasc Med 2021; 8:727773. [PMID: 34604356 PMCID: PMC8484712 DOI: 10.3389/fcvm.2021.727773] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 08/24/2021] [Indexed: 01/01/2023] Open
Abstract
Background: Acute aortic dissection is a potentially fatal cardiovascular disorder associated with high mortality. However, current predictive models show a limited ability to efficiently and flexibly detect this mortality risk, and have been unable to discover a relationship between the mortality rate and certain variables. Thus, this study takes an artificial intelligence approach, whereby clinical data-driven machine learning was utilized to predict the in-hospital mortality of acute aortic dissection. Methods: Patients diagnosed with acute aortic dissection between January 2015 to December 2018 were voluntarily enrolled from the Second Xiangya Hospital of Central South University in the study. The diagnosis was defined by magnetic resonance angiography or computed tomography angiography, with an onset time of the symptoms being within 14 days. The analytical variables included demographic characteristics, physical examination, symptoms, clinical condition, laboratory results, and treatment strategies. The machine learning algorithms included logistic regression, decision tree, K nearest neighbor, Gaussian naive bayes, and extreme gradient boost (XGBoost). Evaluation of the predictive performance of the models was mainly achieved using the area under the receiver operating characteristic curve. SHapley Additive exPlanation was also implemented to interpret the final prediction model. Results: A total of 1,344 acute aortic dissection patients were recruited, including 1,071 (79.7%) patients in the survivor group and 273 (20.3%) patients in non-survivor group. The extreme gradient boost model was found to be the most effective model with the greatest area under the receiver operating characteristic curve (0.927, 95% CI: 0.860-0.968). The three most significant aspects of the extreme gradient boost importance matrix plot were treatment, type of acute aortic dissection, and ischemia-modified albumin levels. In the SHapley Additive exPlanation summary plot, medical treatment, type A acute aortic dissection, and higher ischemia-modified albumin level were shown to increase the risk of hospital-based mortality.
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Affiliation(s)
- Tuo Guo
- Department of Emergency Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China.,Trauma Center, Changsha, China
| | - Zhuo Fang
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Guifang Yang
- Department of Emergency Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China.,Trauma Center, Changsha, China
| | - Yang Zhou
- Department of Emergency Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China.,Trauma Center, Changsha, China
| | - Ning Ding
- Department of Emergency Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China.,Trauma Center, Changsha, China
| | - Wen Peng
- Department of Emergency Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China.,Trauma Center, Changsha, China
| | - Xun Gong
- Department of Emergency Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China.,Trauma Center, Changsha, China
| | - Huaping He
- Department of Emergency Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China.,Trauma Center, Changsha, China
| | - Xiaogao Pan
- Department of Emergency Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China.,Trauma Center, Changsha, China
| | - Xiangping Chai
- Department of Emergency Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China.,Trauma Center, Changsha, China
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