Approaching adverse event detection utilizing transformers on clinical time-series

• URL: http://arxiv.org/abs/2311.09165v1
• Date: Wed, 15 Nov 2023 18:05:31 GMT
• Title: Approaching adverse event detection utilizing transformers on clinical time-series
• Authors: Helge Fredriksen (1), Per Joel Burman (2), Ashenafi Woldaregay (2), Karl {\O}yvind Mikalsen (2), St{\aa}le Nymo (3) ((1) UiT - The Arctic University of Norway, (2) The Norwegian Centre for Clinical Artificial Intelligence, (3) Nordland Hospital Trust)
• Abstract summary: We analyzed 16 months of vital sign recordings obtained from the Nordland Hospital Trust (NHT) We employed an self-supervised framework based on the STraTS transformer architecture to represent the time series data in a latent space. These representations were then subjected to various clustering techniques to explore potential patient phenotypes based on their clinical progress.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Patients being admitted to a hospital will most often be associated with a certain clinical development during their stay. However, there is always a risk of patients being subject to the wrong diagnosis or to a certain treatment not pertaining to the desired effect, potentially leading to adverse events. Our research aims to develop an anomaly detection system for identifying deviations from expected clinical trajectories. To address this goal we analyzed 16 months of vital sign recordings obtained from the Nordland Hospital Trust (NHT). We employed an self-supervised framework based on the STraTS transformer architecture to represent the time series data in a latent space. These representations were then subjected to various clustering techniques to explore potential patient phenotypes based on their clinical progress. While our preliminary results from this ongoing research are promising, they underscore the importance of enhancing the dataset with additional demographic information from patients. This additional data will be crucial for a more comprehensive evaluation of the method's performance.

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)
• Mapping Patient Trajectories: Understanding and Visualizing Sepsis Prognostic Pathways from Patients Clinical Narratives [2.3383924361298876]
  We propose a systematic methodology for developing sepsis prognostic pathways derived from clinical notes. Visualizing these pathways sheds light on the likelihood and direction of disease progression across various contexts. This empowers healthcare providers to implement more personalized and effective healthcare strategies for individual patients.
  arXiv  Detail & Related papers  (2024-07-20T14:45:55Z)
• TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [57.067409211231244]
  This paper presents meticulously curated AIready datasets covering multi-modal data (e.g., drug molecule, disease code, text, categorical/numerical features) and 8 crucial prediction challenges in clinical trial design. We provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
  arXiv  Detail & Related papers  (2024-06-30T09:13:10Z)
• Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction [15.06049250330114]
  The Intensive Care Unit (ICU) is one of the most important parts of a hospital, which admits critically ill patients and provides continuous monitoring and treatment. Various patient outcome prediction methods have been attempted to assist healthcare professionals in clinical decision-making.
  arXiv  Detail & Related papers  (2023-08-24T05:26:56Z)
• Modelling Patient Trajectories Using Multimodal Information [0.0]
  We propose a solution to model patient trajectories that combines different types of information and considers the temporal aspect of clinical data. The developed solution was evaluated on two different clinical outcomes, unexpected patient readmission and disease progression.
  arXiv  Detail & Related papers  (2022-09-09T10:20:54Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• Longitudinal modeling of MS patient trajectories improves predictions of disability progression [2.117653457384462]
  This work addresses the task of optimally extracting information from longitudinal patient data in the real-world setting. We show that with machine learning methods suited for patient trajectories modeling, we can predict disability progression of patients in a two-year horizon. Compared to the models available in the literature, this work uses the most complete patient history for MS disease progression prediction.
  arXiv  Detail & Related papers  (2020-11-09T20:48:00Z)
• MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response [58.0291320452122]
  This paper aims at a unified deep learning approach to predict patient prognosis and therapy response. We formalize the prognosis modeling as a multi-modal asynchronous time series classification task. Our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.
  arXiv  Detail & Related papers  (2020-10-08T15:30:17Z)
• Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data [94.37521840642141]
  We suggest a semi-supervised methodology for the analysis of large clinical datasets, characterized by mixed data types and missing values. The methodology is based on application of elastic principal graphs which can address simultaneously the tasks of dimensionality reduction, data visualization, clustering, feature selection and quantifying the geodesic distances (pseudotime) in partially ordered sequences of observations.
  arXiv  Detail & Related papers  (2020-07-07T21:04:55Z)
• Temporal Phenotyping using Deep Predictive Clustering of Disease Progression [97.88605060346455]
  We develop a deep learning approach for clustering time-series data, where each cluster comprises patients who share similar future outcomes of interest. Experiments on two real-world datasets show that our model achieves superior clustering performance over state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2020-06-15T20:48:43Z)

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.