Compensating trajectory bias for unsupervised patient stratification using adversarial recurrent neural networks

• URL: http://arxiv.org/abs/2112.07239v1
• Date: Tue, 14 Dec 2021 09:01:28 GMT
• Title: Compensating trajectory bias for unsupervised patient stratification using adversarial recurrent neural networks
• Authors: Avelino Javer, Owen Parsons, Oliver Carr, Janie Baxter, Christian Diedrich, Eren El\c{c}i, Steffen Schaper, Katrin Coboeken, Robert D\"urichen
• Abstract summary: We show that patient embeddings and clusters might be impacted by a trajectory bias. Results are dominated by the amount of data contained in each patients trajectory, instead of clinically relevant details. We present a method that can overcome this issue using an adversarial training scheme on top of a RNN-AE.
• Score: 0.6323908398583082
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electronic healthcare records are an important source of information which can be used in patient stratification to discover novel disease phenotypes. However, they can be challenging to work with as data is often sparse and irregularly sampled. One approach to solve these limitations is learning dense embeddings that represent individual patient trajectories using a recurrent neural network autoencoder (RNN-AE). This process can be susceptible to unwanted data biases. We show that patient embeddings and clusters using previously proposed RNN-AE models might be impacted by a trajectory bias, meaning that results are dominated by the amount of data contained in each patients trajectory, instead of clinically relevant details. We investigate this bias on 2 datasets (from different hospitals) and 2 disease areas as well as using different parts of the patient trajectory. Our results using 2 previously published baseline methods indicate a particularly strong bias in case of an event-to-end trajectory. We present a method that can overcome this issue using an adversarial training scheme on top of a RNN-AE. Our results show that our approach can reduce the trajectory bias in all cases.

Related papers

• Self-Supervised Mental Disorder Classifiers via Time Reversal [0.0]
  We demonstrate that a model trained on the time direction of functional neuro-imaging data could help in any downstream task. We train a Deep Neural Network on Independent components derived from fMRI data using the Independent component analysis (ICA) technique. We show that learning time direction helps a model learn some causal relation in fMRI data that helps in faster convergence.
  arXiv  Detail & Related papers  (2022-11-29T17:24:43Z)
• Discovering Invariant Rationales for Graph Neural Networks [104.61908788639052]
  Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input graph's features. We propose a new strategy of discovering invariant rationale (DIR) to construct intrinsically interpretable GNNs.
  arXiv  Detail & Related papers  (2022-01-30T16:43:40Z)
• Stroke recovery phenotyping through network trajectory approaches and graph neural networks [0.966840768820136]
  We analyze data from the NINDS tPA trial using the Trajectory Profile Clustering (TPC) method to identify distinct stroke recovery patterns. Our analysis identified 3 distinct stroke trajectory profiles that align with clinically relevant stroke syndromes.
  arXiv  Detail & Related papers  (2021-09-29T18:46:08Z)
• TRAPDOOR: Repurposing backdoors to detect dataset bias in machine learning-based genomic analysis [15.483078145498085]
  Under-representation of groups in datasets can lead to inaccurate predictions for certain groups, which can exacerbate systemic discrimination issues. We propose TRAPDOOR, a methodology for identification of biased datasets by repurposing a technique that has been mostly proposed for nefarious purposes: Neural network backdoors. Using a real-world cancer dataset, we analyze the dataset with the bias that already existed towards white individuals and also introduced biases in datasets artificially.
  arXiv  Detail & Related papers  (2021-08-14T17:02:02Z)
• Categorical EHR Imputation with Generative Adversarial Nets [11.171712535005357]
  We propose a simple and yet effective approach that is based on previous work on GANs for data imputation. We show that our imputation approach largely improves the prediction accuracy, compared to more traditional data imputation approaches.
  arXiv  Detail & Related papers  (2021-08-03T18:50:26Z)
• Imputation-Free Learning from Incomplete Observations [73.15386629370111]
  We introduce the importance of guided gradient descent (IGSGD) method to train inference from inputs containing missing values without imputation. We employ reinforcement learning (RL) to adjust the gradients used to train the models via back-propagation. Our imputation-free predictions outperform the traditional two-step imputation-based predictions using state-of-the-art imputation methods.
  arXiv  Detail & Related papers  (2021-07-05T12:44:39Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• Convolutional Neural Networks for Sleep Stage Scoring on a Two-Channel EEG Signal [63.18666008322476]
  Sleep problems are one of the major diseases all over the world. Basic tool used by specialists is the Polysomnogram, which is a collection of different signals recorded during sleep. Specialists have to score the different signals according to one of the standard guidelines.
  arXiv  Detail & Related papers  (2021-03-30T09:59:56Z)
• An anatomically-informed 3D CNN for brain aneurysm classification with weak labels [0.2010294990327175]
  We address the task of brain aneurysm detection as a patch-wise binary classification with weak labels. Our approach comes with the non-trivial challenge of the data set creation. We propose a novel, anatomically-driven approach by using a multi-scale and multi-input 3D Convolutional Neural Network.
  arXiv  Detail & Related papers  (2020-11-27T08:00:54Z)
• DeepRite: Deep Recurrent Inverse TreatmEnt Weighting for Adjusting Time-varying Confounding in Modern Longitudinal Observational Data [68.29870617697532]
  We propose Deep Recurrent Inverse TreatmEnt weighting (DeepRite) for time-varying confounding in longitudinal data. DeepRite is shown to recover the ground truth from synthetic data, and estimate unbiased treatment effects from real data.
  arXiv  Detail & Related papers  (2020-10-28T15:05:08Z)
• Learning from Failure: Training Debiased Classifier from Biased Classifier [76.52804102765931]
  We show that neural networks learn to rely on spurious correlation only when it is "easier" to learn than the desired knowledge. We propose a failure-based debiasing scheme by training a pair of neural networks simultaneously. Our method significantly improves the training of the network against various types of biases in both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2020-07-06T07:20:29Z)

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.