ExBEHRT: Extended Transformer for Electronic Health Records to Predict Disease Subtypes & Progressions

• URL: http://arxiv.org/abs/2303.12364v3
• Date: Fri, 11 Aug 2023 14:59:36 GMT
• Title: ExBEHRT: Extended Transformer for Electronic Health Records to Predict Disease Subtypes & Progressions
• Authors: Maurice Rupp, Oriane Peter, Thirupathi Pattipaka
• Abstract summary: We introduce ExBEHRT, an extended version of BEHRT (BERT applied to electronic health records) We extend the feature space to several multimodal records, namely demographics, clinical characteristics, vital signs, smoking status, diagnoses, procedures, medications, and laboratory tests. We show that additional features significantly improve model performance for various downstream tasks in different diseases.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this study, we introduce ExBEHRT, an extended version of BEHRT (BERT applied to electronic health records), and apply different algorithms to interpret its results. While BEHRT considers only diagnoses and patient age, we extend the feature space to several multimodal records, namely demographics, clinical characteristics, vital signs, smoking status, diagnoses, procedures, medications, and laboratory tests, by applying a novel method to unify the frequencies and temporal dimensions of the different features. We show that additional features significantly improve model performance for various downstream tasks in different diseases. To ensure robustness, we interpret model predictions using an adaptation of expected gradients, which has not been previously applied to transformers with EHR data and provides more granular interpretations than previous approaches such as feature and token importances. Furthermore, by clustering the model representations of oncology patients, we show that the model has an implicit understanding of the disease and is able to classify patients with the same cancer type into different risk groups. Given the additional features and interpretability, ExBEHRT can help make informed decisions about disease trajectories, diagnoses, and risk factors of various diseases.

Related papers

• Deep State-Space Generative Model For Correlated Time-to-Event Predictions [54.3637600983898]
  We propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events. Our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.
  arXiv  Detail & Related papers  (2024-07-28T02:42:36Z)
• MPRE: Multi-perspective Patient Representation Extractor for Disease Prediction [3.914545513460964]
  We propose the Multi-perspective Patient Representation Extractor (MPRE) for disease prediction. Specifically, we propose Frequency Transformation Module (FTM) to extract the trend and variation information of dynamic features. In the 2D Multi-Extraction Network (2D MEN), we form the 2D temporal tensor based on trend and variation. We also propose the First-Order Difference Attention Mechanism (FODAM) to calculate the contributions of differences in adjacent variations to the disease diagnosis.
  arXiv  Detail & Related papers  (2024-01-01T13:52:05Z)
• Longitudinal patient stratification of electronic health records with flexible adjustment for clinical outcomes [0.7874708385247353]
  We develop a recurrent neural network autoencoder to cluster EHR data using reconstruction, outcome, and clustering losses. We demonstrate the model performance on $29,229$ diabetes patients, showing it finds clusters of patients with both different trajectories and different outcomes.
  arXiv  Detail & Related papers  (2021-11-11T11:21:39Z)
• Medical Profile Model: Scientific and Practical Applications in Healthcare [1.718235998156457]
  We present the patient histories as temporal sequences of diseases for which embeddings are learned in an unsupervised setup. The embedding space includes demographic parameters which allow the creation of generalized patient profiles. The training of such a medical profile model has been performed on a dataset of more than one million patients.
  arXiv  Detail & Related papers  (2021-06-21T13:30:43Z)
• 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)
• Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records [57.75125067744978]
  We propose a data augmentation method to facilitate domain adaptation. adversarially generated samples are used during domain adaptation. Results confirm the effectiveness of our method and the generality on different tasks.
  arXiv  Detail & Related papers  (2021-01-13T03:20:20Z)
• Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning [48.7576911714538]
  We propose a platelet deposition model and an inferential scheme to estimate the biologically meaningful parameters using approximate computation. This work opens up an unprecedented opportunity of personalized pathology test for CVD detection and medical treatment.
  arXiv  Detail & Related papers  (2020-10-13T15:20:21Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• Predicting Clinical Diagnosis from Patients Electronic Health Records Using BERT-based Neural Networks [62.9447303059342]
  We show the importance of this problem in medical community. We present a modification of Bidirectional Representations from Transformers (BERT) model for classification sequence. We use a large-scale Russian EHR dataset consisting of about 4 million unique patient visits.
  arXiv  Detail & Related papers  (2020-07-15T09:22:55Z)
• Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale [0.5498849973527224]
  We present an unsupervised framework based on deep learning to process heterogeneous EHRs. We derive patient representations that can efficiently and effectively enable patient stratification at scale.
  arXiv  Detail & Related papers  (2020-03-14T00:04:20Z)

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.