A multi-stage machine learning model on diagnosis of esophageal manometry

• URL: http://arxiv.org/abs/2106.13869v1
• Date: Fri, 25 Jun 2021 20:09:23 GMT
• Title: A multi-stage machine learning model on diagnosis of esophageal manometry
• Authors: Wenjun Kou, Dustin A. Carlson, Alexandra J. Baumann, Erica N. Donnan, Jacob M. Schauer, Mozziyar Etemadi, John E. Pandolfino
• Abstract summary: The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
• Score: 50.591267188664666
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: High-resolution manometry (HRM) is the primary procedure used to diagnose esophageal motility disorders. Its interpretation and classification includes an initial evaluation of swallow-level outcomes and then derivation of a study-level diagnosis based on Chicago Classification (CC), using a tree-like algorithm. This diagnostic approach on motility disordered using HRM was mirrored using a multi-stage modeling framework developed using a combination of various machine learning approaches. Specifically, the framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. In the swallow-level stage, three models based on convolutional neural networks (CNNs) were developed to predict swallow type, swallow pressurization, and integrated relaxation pressure (IRP). At the study-level stage, model selection from families of the expert-knowledge-based rule models, xgboost models and artificial neural network(ANN) models were conducted, with the latter two model designed and augmented with motivation from the export knowledge. A simple model-agnostic strategy of model balancing motivated by Bayesian principles was utilized, which gave rise to model averaging weighted by precision scores. The averaged (blended) models and individual models were compared and evaluated, of which the best performance on test dataset is 0.81 in top-1 prediction, 0.92 in top-2 predictions. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data. Moreover, the proposed modeling framework could be easily extended to multi-modal tasks, such as diagnosis of esophageal patients based on clinical data from both HRM and functional luminal imaging probe panometry (FLIP).

Related papers

• Supervised Score-Based Modeling by Gradient Boosting [49.556736252628745]
  We propose a Supervised Score-based Model (SSM) which can be viewed as a gradient boosting algorithm combining score matching. We provide a theoretical analysis of learning and sampling for SSM to balance inference time and prediction accuracy. Our model outperforms existing models in both accuracy and inference time.
  arXiv  Detail & Related papers  (2024-11-02T07:06:53Z)
• Transformer-Based Self-Supervised Learning for Histopathological Classification of Ischemic Stroke Clot Origin [0.0]
  Identifying the thromboembolism source in ischemic stroke is crucial for treatment and secondary prevention. This study describes a self-supervised deep learning approach in digital pathology of emboli for classifying ischemic stroke clot origin.
  arXiv  Detail & Related papers  (2024-05-01T23:40:12Z)
• The Importance of Downstream Networks in Digital Pathology Foundation Models [1.689369173057502]
  We evaluate seven feature extractor models across three different datasets with 162 different aggregation model configurations. We find that the performance of many current feature extractor models is notably similar.
  arXiv  Detail & Related papers  (2023-11-29T16:54:25Z)
• On the Generalization and Adaption Performance of Causal Models [99.64022680811281]
  Differentiable causal discovery has proposed to factorize the data generating process into a set of modules. We study the generalization and adaption performance of such modular neural causal models. Our analysis shows that the modular neural causal models outperform other models on both zero and few-shot adaptation in low data regimes.
  arXiv  Detail & Related papers  (2022-06-09T17:12:32Z)
• DeepAD: A Robust Deep Learning Model of Alzheimer's Disease Progression for Real-World Clinical Applications [0.9999629695552196]
  We propose a novel multi-task deep learning model to predict Alzheimer's disease progression. Our model integrates high dimensional MRI features from a 3D convolutional neural network with other data modalities.
  arXiv  Detail & Related papers  (2022-03-17T05:42:00Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• Multi-task fusion for improving mammography screening data classification [3.7683182861690843]
  We propose a pipeline approach, where we first train a set of individual, task-specific models. We then investigate the fusion thereof, which is in contrast to the standard model ensembling strategy. Our fusion approaches improve AUC scores significantly by up to 0.04 compared to standard model ensembling.
  arXiv  Detail & Related papers  (2021-12-01T13:56:27Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Generative Autoencoder Kernels on Deep Learning for Brain Activity Analysis [3.04585143845864]
  Hessenberg decomposition-based ELM autoencoder (HessELM-AE) is a novel kernel to generate different presentations of the input data. The aim of the study is analyzing the performance of the novel Deep AE kernel for clinical availability on electroencephalogram (EEG) with stroke patients.
  arXiv  Detail & Related papers  (2021-01-21T08:19:47Z)
• Hybrid modeling: Applications in real-time diagnosis [64.5040763067757]
  We outline a novel hybrid modeling approach that combines machine learning inspired models and physics-based models. We are using such models for real-time diagnosis applications.
  arXiv  Detail & Related papers  (2020-03-04T00:44:57Z)

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.