Transformer Models for Acute Brain Dysfunction Prediction

• URL: http://arxiv.org/abs/2303.07305v1
• Date: Mon, 13 Mar 2023 17:30:04 GMT
• Title: Transformer Models for Acute Brain Dysfunction Prediction
• Authors: Brandon Silva, Miguel Contreras, Tezcan Ozrazgat Baslanti, Yuanfang Ren, Guan Ziyuan, Kia Khezeli, Azra Bihorac, Parisa Rashidi
• Abstract summary: Acute brain dysfunctions (ABD) are prevalent in the ICU, especially among older patients. We develop a machine learning system for real-time prediction of ADB using Electronic Health Record (HER) data. Our system can then be deployed for real-time prediction of ADB in ICUs to reduce the number of incidents caused by ABD.
• Score: 3.2884323672632028
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Acute brain dysfunctions (ABD), which include coma and delirium, are prevalent in the ICU, especially among older patients. The current approach in manual assessment of ABD by care providers may be sporadic and subjective. Hence, there exists a need for a data-driven robust system automating the assessment and prediction of ABD. In this work, we develop a machine learning system for real-time prediction of ADB using Electronic Health Record (HER) data. Our data processing pipeline enables integration of static and temporal data, and extraction of features relevant to ABD. We train several state-of-the-art transformer models and baseline machine learning models including CatBoost and XGB on the data that was collected from patients admitted to the ICU at UF Shands Hospital. We demonstrate the efficacy of our system for tasks related to acute brain dysfunction including binary classification of brain acuity and multi-class classification (i.e., coma, delirium, death, or normal), achieving a mean AUROC of 0.953 on our Long-former implementation. Our system can then be deployed for real-time prediction of ADB in ICUs to reduce the number of incidents caused by ABD. Moreover, the real-time system has the potential to reduce costs, duration of patients stays in the ICU, and mortality among those afflicted.

Related papers

• REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates [54.96885726053036]
  This paper introduces a novel graph-based residual state update mechanism (REST) for real-time EEG signal analysis. By leveraging a combination of graph neural networks and recurrent structures, REST efficiently captures both non-Euclidean geometry and temporal dependencies within EEG data. Our model demonstrates high accuracy in both seizure detection and classification tasks.
  arXiv  Detail & Related papers  (2024-06-03T16:30:19Z)
• A multi-cohort study on prediction of acute brain dysfunction states using selective state space models [12.0129301272171]
  acute brain dysfunction (ABD) is a critical challenge due to its prevalence and severe implications for patient outcomes. Our research attempts to solve these problems by harnessing Electronic Health Records (EHR) data. Existing models solely predict a single state (e.g., either delirium or coma) require at least 24 hours of observation data to make predictions. Our research fills these gaps in the existing literature by dynamically predicting delirium, coma, mortality and fluctuating for 12-hour intervals throughout an ICU stay.
  arXiv  Detail & Related papers  (2024-03-11T22:58:11Z)
• Detecting Visual Cues in the Intensive Care Unit and Association with Patient Clinical Status [0.9867627975175174]
  Existing patient assessments in the ICU are mostly sporadic and administered manually. We developed a new "masked loss computation" technique that addresses the data imbalance problem. We performed AU inference on 634,054 frames to evaluate the association between facial AUs and clinically important patient conditions.
  arXiv  Detail & Related papers  (2023-11-01T15:07:03Z)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• Safe AI for health and beyond -- Monitoring to transform a health service [51.8524501805308]
  We will assess the infrastructure required to monitor the outputs of a machine learning algorithm. We will present two scenarios with examples of monitoring and updates of models.
  arXiv  Detail & Related papers  (2023-03-02T17:27:45Z)
• Sequential Diagnosis Prediction with Transformer and Ontological Representation [35.88195694025553]
  We propose an end-to-end robust transformer-based model called SETOR to handle irregular intervals between a patient's visits with admitted timestamps and length of stay in each visit. Experiments conducted on two real-world healthcare datasets show that, our sequential diagnoses prediction model SETOR achieves better predictive results than previous state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-09-07T13:09:55Z)
• Multiple Organ Failure Prediction with Classifier-Guided Generative Adversarial Imputation Networks [4.040013871160853]
  Multiple organ failure (MOF) is a severe syndrome with a high mortality rate among Intensive Care Unit (ICU) patients. Applying machine learning models to electronic health records is a challenge due to the pervasiveness of missing values.
  arXiv  Detail & Related papers  (2021-06-22T15:49:01Z)
• BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes [53.163089893876645]
  We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey. An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys. We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
  arXiv  Detail & Related papers  (2020-09-24T00:42:36Z)
• A Machine Learning Early Warning System: Multicenter Validation in Brazilian Hospitals [4.659599449441919]
  Early recognition of clinical deterioration is one of the main steps for reducing inpatient morbidity and mortality. Since hospital wards are given less attention compared to the Intensive Care Unit, ICU, we hypothesized that when a platform is connected to a stream of EHR, there would be a drastic improvement in dangerous situations awareness. With the application of machine learning, the system is capable to consider all patient's history and through the use of high-performing predictive models, an intelligent early warning system is enabled.
  arXiv  Detail & Related papers  (2020-06-09T21:21:38Z)
• Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [62.997667081978825]
  COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
  arXiv  Detail & Related papers  (2020-05-10T01:45:03Z)
• Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification [80.00316465793702]
  We present a deep learning framework that enables robust modeling in challenging scenarios. Our results show that using 85% lesser labeled data, we can build predictive models that match the performance of classifiers trained in a large-scale data setting.
  arXiv  Detail & Related papers  (2020-05-03T02:36:00Z)

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.