ICU Bloodstream Infection Prediction: A Transformer-Based Approach for EHR Analysis

• URL: http://arxiv.org/abs/2405.00819v1
• Date: Wed, 1 May 2024 19:00:30 GMT
• Title: ICU Bloodstream Infection Prediction: A Transformer-Based Approach for EHR Analysis
• Authors: Ortal Hirszowicz, Dvir Aran,
• Abstract summary: We introduce RatchetEHR, a novel framework designed for the predictive analysis of electronic health records (EHR) data in intensive care unit (ICU) settings. R RatchetEHR demonstrates superior predictive performance compared to other methods, including RNN, LSTM, and XGBoost. A key innovation in RatchetEHR is the integration of the Graph Convolutional Transformer (GCT) component, which significantly enhances the ability to identify hidden structural relationships.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We introduce RatchetEHR, a novel transformer-based framework designed for the predictive analysis of electronic health records (EHR) data in intensive care unit (ICU) settings, with a specific focus on bloodstream infection (BSI) prediction. Leveraging the MIMIC-IV dataset, RatchetEHR demonstrates superior predictive performance compared to other methods, including RNN, LSTM, and XGBoost, particularly due to its advanced handling of sequential and temporal EHR data. A key innovation in RatchetEHR is the integration of the Graph Convolutional Transformer (GCT) component, which significantly enhances the ability to identify hidden structural relationships within EHR data, resulting in more accurate clinical predictions. Through SHAP value analysis, we provide insights into influential features for BSI prediction. RatchetEHR integrates multiple advancements in deep learning which together provide accurate predictions even with a relatively small sample size and highly imbalanced dataset. This study contributes to medical informatics by showcasing the application of advanced AI techniques in healthcare and sets a foundation for further research to optimize these capabilities in EHR data analysis.

Related papers

• Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models [69.06149482021071]
  We propose a novel EHR data generation model called EHRPD. It is a diffusion-based model designed to predict the next visit based on the current one while also incorporating time interval estimation. We conduct experiments on two public datasets and evaluate EHRPD from fidelity, privacy, and utility perspectives.
  arXiv  Detail & Related papers  (2024-06-20T02:20:23Z)
• Recent Advances in Predictive Modeling with Electronic Health Records [71.19967863320647]
  utilizing EHR data for predictive modeling presents several challenges due to its unique characteristics. Deep learning has demonstrated its superiority in various applications, including healthcare.
  arXiv  Detail & Related papers  (2024-02-02T00:31:01Z)
• Evaluating the Fairness of the MIMIC-IV Dataset and a Baseline Algorithm: Application to the ICU Length of Stay Prediction [65.268245109828]
  This paper uses the MIMIC-IV dataset to examine the fairness and bias in an XGBoost binary classification model predicting the ICU length of stay. The research reveals class imbalances in the dataset across demographic attributes and employs data preprocessing and feature extraction. The paper concludes with recommendations for fairness-aware machine learning techniques for mitigating biases and the need for collaborative efforts among healthcare professionals and data scientists.
  arXiv  Detail & Related papers  (2023-12-31T16:01:48Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• FineEHR: Refine Clinical Note Representations to Improve Mortality Prediction [3.9026461169566673]
  Large-scale electronic health records provide machine learning models with an abundance of clinical text and vital sign data. Despite the emergence of advanced Natural Language Processing (NLP) algorithms for clinical note analysis, the complex textual structure and noise present in raw clinical data have posed significant challenges. We propose FINEEHR, a system that utilizes two representation learning techniques, namely metric learning and fine-tuning, to refine clinical note embeddings.
  arXiv  Detail & Related papers  (2023-04-24T02:42:52Z)
• On the Importance of Clinical Notes in Multi-modal Learning for EHR Data [0.0]
  Previous research has shown that jointly using clinical notes with electronic health record data improved predictive performance for patient monitoring. We first confirm that performance significantly improves over state-of-the-art EHR data models when combining EHR data and clinical notes. We then provide an analysis showing improvements arise almost exclusively from a subset of notes containing broader context on patient state rather than clinician notes.
  arXiv  Detail & Related papers  (2022-12-06T15:18:57Z)
• Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applications [9.374416143268892]
  generative adversarial network (GAN) entitled EHR-M-GAN which synthesizes textitmixed-type timeseries EHR data. We have validated EHR-M-GAN on three publicly-available intensive care unit databases with records from a total of 141,488 unique patients.
  arXiv  Detail & Related papers  (2021-12-22T17:17:34Z)
• SANSformers: Self-Supervised Forecasting in Electronic Health Records with Attention-Free Models [48.07469930813923]
  This work aims to forecast the demand for healthcare services, by predicting the number of patient visits to healthcare facilities. We introduce SANSformer, an attention-free sequential model designed with specific inductive biases to cater for the unique characteristics of EHR data. Our results illuminate the promising potential of tailored attention-free models and self-supervised pretraining in refining healthcare utilization predictions across various patient demographics.
  arXiv  Detail & Related papers  (2021-08-31T08:23:56Z)
• 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)
• Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records [2.2859570135269625]
  We train a Heterogeneous Graph Model (HGM) on Electronic Health Record data and use the resulting embedding vector as additional information added to a Conal Neural Network (CNN) model for predicting in-hospital mortality. We find that adding HGM to a CNN model increases the mortality prediction accuracy up to 4%.
  arXiv  Detail & Related papers  (2020-12-28T02:27:09Z)
• Bidirectional Representation Learning from Transformers using Multimodal Electronic Health Record Data to Predict Depression [11.1492931066686]
  We present a temporal deep learning model to perform bidirectional representation learning on EHR sequences to predict depression. The model generated the highest increases of precision-recall area under the curve (PRAUC) from 0.70 to 0.76 in depression prediction compared to the best baseline model.
  arXiv  Detail & Related papers  (2020-09-26T17:56:37Z)

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.