Deep Representation Learning-Based Dynamic Trajectory Phenotyping for Acute Respiratory Failure in Medical Intensive Care Units

• URL: http://arxiv.org/abs/2405.02563v1
• Date: Sat, 4 May 2024 04:29:22 GMT
• Title: Deep Representation Learning-Based Dynamic Trajectory Phenotyping for Acute Respiratory Failure in Medical Intensive Care Units
• Authors: Alan Wu, Tilendra Choudhary, Pulakesh Upadhyaya, Ayman Ali, Philip Yang, Rishikesan Kamaleswaran,
• Abstract summary: Sepsis-induced acute respiratory failure (ARF) is a serious complication with a poor prognosis. This paper presents a deep representation learningbased phenotyping method to identify distinct groups of clinical trajectories of septic patients with ARF.
• Score: 4.9981612589160775
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sepsis-induced acute respiratory failure (ARF) is a serious complication with a poor prognosis. This paper presents a deep representation learningbased phenotyping method to identify distinct groups of clinical trajectories of septic patients with ARF. For this retrospective study, we created a dataset from electronic medical records (EMR) consisting of data from sepsis patients admitted to medical intensive care units who required at least 24 hours of invasive mechanical ventilation at a quarternary care academic hospital in southeast USA for the years 2016-2021. A total of N=3349 patient encounters were included in this study. Clustering Representation Learning on Incomplete Time Series Data (CRLI) algorithm was applied to a parsimonious set of EMR variables in this data set. To validate the optimal number of clusters, the K-means algorithm was used in conjunction with dynamic time warping. Our model yielded four distinct patient phenotypes that were characterized as liver dysfunction/heterogeneous, hypercapnia, hypoxemia, and multiple organ dysfunction syndrome by a critical care expert. A Kaplan-Meier analysis to compare the 28-day mortality trends exhibited significant differences (p < 0.005) between the four phenotypes. The study demonstrates the utility of our deep representation learning-based approach in unraveling phenotypes that reflect the heterogeneity in sepsis-induced ARF in terms of different mortality outcomes and severity. These phenotypes might reveal important clinical insights into an effective prognosis and tailored treatment strategies.

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)
• Collaborative learning of common latent representations in routinely collected multivariate ICU physiological signals [0.0]
  Our algorithm integrates Long Short-Term Memory (LSTM) networks with collaborative filtering concepts to identify common physiological states across patients. Tested on real-world ICU clinical data for intracranial hypertension (IH) detection in patients with brain injury, our method achieved an area under the curve (AUC) of 0.889 and average precision (AP) of 0.725.
  arXiv  Detail & Related papers  (2024-02-27T22:10:51Z)
• Identifying acute illness phenotypes via deep temporal interpolation and clustering network on physiologic signatures [6.315312816818801]
  Initial hours of hospital admission impact clinical trajectory, but early clinical decisions often suffer due to data paucity. We created a single-center, longitudinal EHR dataset for 75,762 adults admitted to a tertiary care center for 6+ hours. We proposed a deep temporal clustering and clustering network to extract latent representations from sparse, irregularly sampled vital sign data.
  arXiv  Detail & Related papers  (2023-07-27T21:05:23Z)
• Tissue Classification During Needle Insertion Using Self-Supervised Contrastive Learning and Optical Coherence Tomography [53.38589633687604]
  We propose a deep neural network that classifies the tissues from the phase and intensity data of complex OCT signals acquired at the needle tip. We show that with 10% of the training set, our proposed pretraining strategy helps the model achieve an F1 score of 0.84 whereas the model achieves an F1 score of 0.60 without it.
  arXiv  Detail & Related papers  (2023-04-26T14:11:04Z)
• ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data [1.370633147306388]
  Sepsis is a deadly condition affecting many patients in the hospital. We propose the use of Active Learning Recurrent Neural Networks (ALRts) for short temporal horizons to improve the prediction of irregularly sampled temporal events such as sepsis. We show that an active learning RNN model trained on limited data can form robust sepsis predictions comparable to models using the entire training dataset.
  arXiv  Detail & Related papers  (2022-12-13T04:31:49Z)
• A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data [0.0]
  We developed a hybrid machine learning/deep learning model to classify patients in two outcome categories, non-ICU and ICU. A fully 3D patient-level CNN classifier on baseline CT images is used as feature extractor. The model aims to provide clinical decision support to medical doctors, with the probability score of belonging to an outcome class and with case-based SHAP interpretation of features importance.
  arXiv  Detail & Related papers  (2021-05-13T08:39:56Z)
• 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)
• 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)
• 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)
• Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning [48.05232274463484]
  Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of affected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images.
  arXiv  Detail & Related papers  (2020-05-06T15:19:15Z)
• 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.