Confidence Estimation for Automatic Detection of Depression and Alzheimer's Disease Based on Clinical Interviews

• URL: http://arxiv.org/abs/2407.19984v1
• Date: Mon, 29 Jul 2024 13:18:23 GMT
• Title: Confidence Estimation for Automatic Detection of Depression and Alzheimer's Disease Based on Clinical Interviews
• Authors: Wen Wu, Chao Zhang, Philip C. Woodland,
• Abstract summary: This paper investigates confidence estimation for automatic detection of Alzheimer's disease (AD) and depression based on clinical interviews. A novel Bayesian approach is proposed which uses a dynamic Dirichlet prior distribution to model the second-order probability of the predictive distribution.
• Score: 14.626563022137875
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Speech-based automatic detection of Alzheimer's disease (AD) and depression has attracted increased attention. Confidence estimation is crucial for a trust-worthy automatic diagnostic system which informs the clinician about the confidence of model predictions and helps reduce the risk of misdiagnosis. This paper investigates confidence estimation for automatic detection of AD and depression based on clinical interviews. A novel Bayesian approach is proposed which uses a dynamic Dirichlet prior distribution to model the second-order probability of the predictive distribution. Experimental results on the publicly available ADReSS and DAIC-WOZ datasets demonstrate that the proposed method outperforms a range of baselines for both classification accuracy and confidence estimation.

Related papers

• Leveraging Geolocation in Clinical Records to Improve Alzheimer's Disease Diagnosis Using DMV Framework [3.004788114489393]
  Alzheimer's Disease (AD) early detection is critical for enabling timely intervention and improving patient outcomes. This paper presents a DMV framework using Llama3-70B and GPT-4o as embedding models to analyze clinical notes and predict a continuous risk score associated with early AD onset.
  arXiv  Detail & Related papers  (2025-02-06T18:33:59Z)
• Trust-informed Decision-Making Through An Uncertainty-Aware Stacked Neural Networks Framework: Case Study in COVID-19 Classification [10.265080819932614]
  This study presents an uncertainty-aware stacked neural networks model for the reliable classification of COVID-19 from radiological images. The model addresses the critical gap in uncertainty-aware modeling by focusing on accurately identifying confidently correct predictions. The architecture integrates uncertainty quantification methods, including Monte Carlo dropout and ensemble techniques, to enhance predictive reliability.
  arXiv  Detail & Related papers  (2024-09-19T04:20:12Z)
• 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)
• Revisiting Confidence Estimation: Towards Reliable Failure Prediction [53.79160907725975]
  We find a general, widely existing but actually-neglected phenomenon that most confidence estimation methods are harmful for detecting misclassification errors. We propose to enlarge the confidence gap by finding flat minima, which yields state-of-the-art failure prediction performance.
  arXiv  Detail & Related papers  (2024-03-05T11:44:14Z)
• Uncertainty Quantification on Clinical Trial Outcome Prediction [37.25114005044208]
  We propose incorporating uncertainty quantification into clinical trial outcome predictions. Our main goal is to enhance the model's ability to discern nuanced differences. We have adopted a selective classification approach to fulfill our objective.
  arXiv  Detail & Related papers  (2024-01-07T13:48:05Z)
• Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty [52.03490691733464]
  We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks. By leveraging subjective logic theory, we explicitly model probability and uncertainty for the problem of medical image segmentation. DeviS incorporates an uncertainty-aware filtering module, which utilizes the metric of uncertainty-calibrated error to filter reliable data.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology [40.96261204117952]
  We train models to identify lung adenocarcinoma vs. squamous cell carcinoma and show that high-confidence predictions outperform predictions without UQ. We show that UQ thresholding remains reliable in the setting of domain shift, with accurate high-confidence predictions of adenocarcinoma vs. squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts.
  arXiv  Detail & Related papers  (2022-04-09T17:35:37Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• An evaluation of word-level confidence estimation for end-to-end automatic speech recognition [70.61280174637913]
  We investigate confidence estimation for end-to-end automatic speech recognition (ASR) We provide an extensive benchmark of popular confidence methods on four well-known speech datasets. Our results suggest a strong baseline can be obtained by scaling the logits by a learnt temperature.
  arXiv  Detail & Related papers  (2021-01-14T09:51:59Z)
• 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)
• Diagnostic Uncertainty Calibration: Towards Reliable Machine Predictions in Medical Domain [20.237847764018138]
  We propose an evaluation framework for class probability estimates (CPEs) in the presence of label uncertainty. We also formalize evaluation metrics for higher-order statistics, including inter-rater disagreement. We show that our approach significantly enhances the reliability of uncertainty estimates.
  arXiv  Detail & Related papers  (2020-07-03T12:54:08Z)

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.