Explainable Machine Learning System for Predicting Chronic Kidney Disease in High-Risk Cardiovascular Patients

• URL: http://arxiv.org/abs/2404.11148v1
• Date: Wed, 17 Apr 2024 07:59:33 GMT
• Title: Explainable Machine Learning System for Predicting Chronic Kidney Disease in High-Risk Cardiovascular Patients
• Authors: Nantika Nguycharoen,
• Abstract summary: This research developed an explainable machine learning system for predicting Chronic Kidney Disease (CKD) in patients with cardiovascular risks. The Random Forest model achieved the highest sensitivity of 88.2%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As the global population ages, the incidence of Chronic Kidney Disease (CKD) is rising. CKD often remains asymptomatic until advanced stages, which significantly burdens both the healthcare system and patient quality of life. This research developed an explainable machine learning system for predicting CKD in patients with cardiovascular risks, utilizing medical history and laboratory data. The Random Forest model achieved the highest sensitivity of 88.2%. The study introduces a comprehensive explainability framework that extends beyond traditional feature importance methods, incorporating global and local interpretations, bias inspection, biomedical relevance, and safety assessments. Key predictive features identified in global interpretation were the use of diabetic and ACEI/ARB medications, and initial eGFR values. Local interpretation provided model insights through counterfactual explanations, which aligned with other system parts. After conducting a bias inspection, it was found that the initial eGFR values and CKD predictions exhibited some bias, but no significant gender bias was identified. The model's logic, extracted by scoped rules, was confirmed to align with existing medical literature. The safety assessment tested potentially dangerous cases and confirmed that the model behaved safely. This system enhances the explainability, reliability, and accountability of the model, promoting its potential integration into healthcare settings and compliance with upcoming regulatory standards, and showing promise for broader applications in healthcare machine learning.

Related papers

• SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing [67.8991481023825]
  Sepsis is the leading cause of in-hospital mortality in the USA. Existing predictive models are usually trained on high-quality data with few missing information. For the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm.
  arXiv  Detail & Related papers  (2024-07-24T04:47:36Z)
• Interpretable Survival Analysis for Heart Failure Risk Prediction [50.64739292687567]
  We propose a novel survival analysis pipeline that is both interpretable and competitive with state-of-the-art survival models. Our pipeline achieves state-of-the-art performance and provides interesting and novel insights about risk factors for heart failure.
  arXiv  Detail & Related papers  (2023-10-24T02:56:05Z)
• Explainable AI for Malnutrition Risk Prediction from m-Health and Clinical Data [3.093890460224435]
  This paper presents a novel AI framework for early and explainable malnutrition risk detection based on heterogeneous m-health data. We performed an extensive model evaluation including both subject-independent and personalised predictions. We also investigated several benchmark XAI methods to extract global model explanations.
  arXiv  Detail & Related papers  (2023-05-31T08:07:35Z)
• Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes [50.8044927215346]
  We consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state. We employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability. Our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.
  arXiv  Detail & Related papers  (2023-02-11T18:07:11Z)
• COVID-Net Biochem: An Explainability-driven Framework to Building Machine Learning Models for Predicting Survival and Kidney Injury of COVID-19 Patients from Clinical and Biochemistry Data [66.43957431843324]
  We introduce COVID-Net Biochem, a versatile and explainable framework for constructing machine learning models. We apply this framework to predict COVID-19 patient survival and the likelihood of developing Acute Kidney Injury during hospitalization.
  arXiv  Detail & Related papers  (2022-04-24T07:38:37Z)
• Understanding Heart-Failure Patients EHR Clinical Features via SHAP Interpretation of Tree-Based Machine Learning Model Predictions [8.444557621643568]
  Heart failure (HF) is a major cause of mortality. We examined whether machine learning models, more specifically the XGBoost model, can accurately predict patient stage based on EHR. Our results indicate that based on structured data from EHR, our models could predict patients' ejection fraction (EF) scores with moderate accuracy.
  arXiv  Detail & Related papers  (2021-03-20T22:17:05Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• An explainable Transformer-based deep learning model for the prediction of incident heart failure [22.513476932615845]
  We developed a novel Transformer deep-learning model for prediction of incident heart failure involving 100,071 patients. The model achieved 0.93 and 0.93 area under the receiver operator curve and 0.69 and 0.70 area under the precision-recall curve. The importance of contextualised medical information was revealed in sensitivity analyses.
  arXiv  Detail & Related papers  (2021-01-27T12:45:15Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• CovidCare: Transferring Knowledge from Existing EMR to Emerging Epidemic for Interpretable Prognosis [20.701122594508675]
  We propose a deep-learning-based approach, CovidCare, to enhance the prognosis for inpatients with emerging infectious diseases. CovidCare learns to embed the COVID-19-related medical features based on massive existing EMR data via transfer learning. We conduct the length of stay prediction experiments for patients on a real-world COVID-19 dataset.
  arXiv  Detail & Related papers  (2020-07-17T09:20:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.