Improving Model's Interpretability and Reliability using Biomarkers

• URL: http://arxiv.org/abs/2402.12394v1
• Date: Fri, 16 Feb 2024 20:19:28 GMT
• Title: Improving Model's Interpretability and Reliability using Biomarkers
• Authors: Gautam Rajendrakumar Gare, Tom Fox, Beam Chansangavej, Amita Krishnan, Ricardo Luis Rodriguez, Bennett P deBoisblanc, Deva Kannan Ramanan, John Michael Galeotti
• Abstract summary: The objective of this study is to assess whether explanations from a decision tree classifier, utilizing biomarkers, can improve users' ability to identify inaccurate model predictions. Our findings demonstrate that decision tree explanations, based on clinically established biomarkers, can assist clinicians in detecting false positives, thus improving the reliability of diagnostic models in medicine.
• Score: 0.04705265502876046
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate and interpretable diagnostic models are crucial in the safety-critical field of medicine. We investigate the interpretability of our proposed biomarker-based lung ultrasound diagnostic pipeline to enhance clinicians' diagnostic capabilities. The objective of this study is to assess whether explanations from a decision tree classifier, utilizing biomarkers, can improve users' ability to identify inaccurate model predictions compared to conventional saliency maps. Our findings demonstrate that decision tree explanations, based on clinically established biomarkers, can assist clinicians in detecting false positives, thus improving the reliability of diagnostic models in medicine.

Related papers

• Methodological Explainability Evaluation of an Interpretable Deep Learning Model for Post-Hepatectomy Liver Failure Prediction Incorporating Counterfactual Explanations and Layerwise Relevance Propagation: A Prospective In Silico Trial [13.171582596404313]
  We developed a variational autoencoder-multilayer perceptron (VAE-MLP) model for preoperative PHLF prediction. This model integrated counterfactuals and layerwise relevance propagation (LRP) to provide insights into its decision-making mechanism. Results from the three-track in silico clinical trial showed that clinicians' prediction accuracy and confidence increased when AI explanations were provided.
  arXiv  Detail & Related papers  (2024-08-07T13:47:32Z)
• Enhancing predictive imaging biomarker discovery through treatment effect analysis [2.8300022117286523]
  This study focuses on the discovery of predictive imaging biomarkers, aiming to leverage pre-treatment images to unveil new causal relationships. Previous approaches relied on labor-intensive handcrafted or manually derived features, which may introduce biases. We propose an evaluation protocol for this task to assess a model's ability to identify predictive imaging biomarkers and differentiate them from prognostic ones.
  arXiv  Detail & Related papers  (2024-06-04T17:54:44Z)
• A marker-less human motion analysis system for motion-based biomarker discovery in knee disorders [60.99112047564336]
  The NHS has been having increased difficulty seeing all low-risk patients, this includes but not limited to suspected osteoarthritis (OA) patients. We propose a novel method of automated biomarker identification for diagnosis of knee disorders and the monitoring of treatment progression.
  arXiv  Detail & Related papers  (2023-04-26T16:47:42Z)
• This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text [56.32427751440426]
  In clinical practice such models must not only be accurate, but provide doctors with interpretable and helpful results. We introduce ProtoPatient, a novel method based on prototypical networks and label-wise attention. We evaluate the model on two publicly available clinical datasets and show that it outperforms existing baselines.
  arXiv  Detail & Related papers  (2022-10-16T10:12:07Z)
• 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)
• Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnostics [2.40246230430283]
  We focus on a specific, recently established high-dimensional biomarker - adaptive immune receptor repertoires (AIRRs) We argue that causal modeling improves machine learning-based biomarker robustness by identifying stable relations between variables.
  arXiv  Detail & Related papers  (2022-04-20T08:15:54Z)
• BI-RADS-Net: An Explainable Multitask Learning Approach for Cancer Diagnosis in Breast Ultrasound Images [69.41441138140895]
  This paper introduces BI-RADS-Net, a novel explainable deep learning approach for cancer detection in breast ultrasound images. The proposed approach incorporates tasks for explaining and classifying breast tumors, by learning feature representations relevant to clinical diagnosis. Explanations of the predictions (benign or malignant) are provided in terms of morphological features that are used by clinicians for diagnosis and reporting in medical practice.
  arXiv  Detail & Related papers  (2021-10-05T19:14:46Z)
• Adversarial Factor Models for the Generation of Improved Autism Diagnostic Biomarkers [19.48133927082379]
  We present applications of adversarial linear factor models in the creation of improved biomarkers for autism spectrum disorder (ASD) diagnosis. First, we demonstrate that an adversarial linear factor model can be used to remove confounding information from our biomarkers, ensuring that they contain only pertinent information on ASD. Second, we show this same model can be used to learn a disentangled representation of multimodal biomarkers that results in an increase in predictive performance.
  arXiv  Detail & Related papers  (2021-09-24T21:56:30Z)
• 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)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)

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.