Early Prediction of Mortality in Critical Care Setting in Sepsis Patients Using Structured Features and Unstructured Clinical Notes

• URL: http://arxiv.org/abs/2112.01230v1
• Date: Tue, 9 Nov 2021 19:57:05 GMT
• Title: Early Prediction of Mortality in Critical Care Setting in Sepsis Patients Using Structured Features and Unstructured Clinical Notes
• Authors: Jiyoung Shin, Yikuan Li, Yuan Luo
• Abstract summary: Using the MIMIC-III database, we integrated demographic data, physiological measurements and clinical notes. We built and applied several machine learning models to predict the risk of hospital mortality and 30-day mortality in sepsis patients.
• Score: 4.387308555401595
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sepsis is an important cause of mortality, especially in intensive care unit (ICU) patients. Developing novel methods to identify early mortality is critical for improving survival outcomes in sepsis patients. Using the MIMIC-III database, we integrated demographic data, physiological measurements and clinical notes. We built and applied several machine learning models to predict the risk of hospital mortality and 30-day mortality in sepsis patients. From the clinical notes, we generated clinically meaningful word representations and embeddings. Supervised learning classifiers and a deep learning architecture were used to construct prediction models. The configurations that utilized both structured and unstructured clinical features yielded competitive F-measure of 0.512. Our results showed that the approaches integrating both structured and unstructured clinical features can be effectively applied to assist clinicians in identifying the risk of mortality in sepsis patients upon admission to the ICU.

Related papers

• XAI for In-hospital Mortality Prediction via Multimodal ICU Data [57.73357047856416]
  We propose an efficient, explainable AI solution for predicting in-hospital mortality via multimodal ICU data. We employ multimodal learning in our framework, which can receive heterogeneous inputs from clinical data and make decisions. Our framework can be easily transferred to other clinical tasks, which facilitates the discovery of crucial factors in healthcare research.
  arXiv  Detail & Related papers  (2023-12-29T14:28:04Z)
• Multimodal Pretraining of Medical Time Series and Notes [45.89025874396911]
  Deep learning models show promise in extracting meaningful patterns, but they require extensive labeled data. We propose a novel approach employing self-supervised pretraining, focusing on the alignment of clinical measurements and notes. In downstream tasks, including in-hospital mortality prediction and phenotyping, our model outperforms baselines in settings where only a fraction of the data is labeled.
  arXiv  Detail & Related papers  (2023-12-11T21:53:40Z)
• Prediction of Post-Operative Renal and Pulmonary Complications Using Transformers [69.81176740997175]
  We evaluate the performance of transformer-based models in predicting postoperative acute renal failure, pulmonary complications, and postoperative in-hospital mortality. Our results demonstrate that transformer-based models can achieve superior performance in predicting postoperative complications and outperform traditional machine learning models.
  arXiv  Detail & Related papers  (2023-06-01T14:08:05Z)
• A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Prediction [8.625186194860696]
  We provide a novel multimodal transformer to fuse clinical notes and structured EHR data for better prediction of in-hospital mortality. To improve interpretability, we propose an integrated gradients (IG) method to select important words in clinical notes. We also investigate the significance of domain adaptive pretraining and task adaptive fine-tuning on the Clinical BERT.
  arXiv  Detail & Related papers  (2022-08-09T03:49:52Z)
• Integrating Physiological Time Series and Clinical Notes with Transformer for Early Prediction of Sepsis [10.791880225915255]
  Sepsis is a leading cause of death in the Intensive Care Units (ICU) We propose a multimodal Transformer model for early sepsis prediction. We use the physiological time series data and clinical notes for each patient within $36$ hours of ICU admission.
  arXiv  Detail & Related papers  (2022-03-28T03:19:03Z)
• Enriching Unsupervised User Embedding via Medical Concepts [51.17532619610099]
  Unsupervised user embedding aims to encode patients into fixed-length vectors without human supervisions. Medical concepts extracted from the clinical notes contain rich connections between patients and their clinical categories. We propose a concept-aware unsupervised user embedding that jointly leverages text documents and medical concepts from two clinical corpora.
  arXiv  Detail & Related papers  (2022-03-20T18:54:05Z)
• COVID-Net Clinical ICU: Enhanced Prediction of ICU Admission for COVID-19 Patients via Explainability and Trust Quantification [71.80459780697956]
  We introduce COVID-Net Clinical ICU, a neural network for ICU admission prediction based on patient clinical data. The proposed COVID-Net Clinical ICU was built using a clinical dataset from Hospital Sirio-Libanes comprising of 1,925 COVID-19 patients. We conducted system-level insight discovery using a quantitative explainability strategy to study the decision-making impact of different clinical features.
  arXiv  Detail & Related papers  (2021-09-14T14:16:32Z)
• Improving Early Sepsis Prediction with Multi Modal Learning [5.129463113166068]
  Clinical text provides essential information to estimate the severity of sepsis. We employ state-of-the-art NLP models such as BERT and a highly specialized NLP model in Amazon Comprehend Medical to represent the text. Our methods significantly outperforms a clinical criteria suggested by experts, qSOFA, as well as the winning model of the PhysioNet Computing in Cardiology Challenge for predicting Sepsis.
  arXiv  Detail & Related papers  (2021-07-23T09:25:31Z)
• 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)
• ISeeU2: Visually Interpretable ICU mortality prediction using deep learning and free-text medical notes [0.0]
  We show a Deep Learning model trained on MIMIC-III to predict mortality using raw nursing notes, together with visual explanations for word importance. Our model reaches a ROC of 0.8629, outperforming the traditional SAPS-II score and providing enhanced interpretability when compared with similar Deep Learning approaches.
  arXiv  Detail & Related papers  (2020-05-19T08:30:34Z)
• Integrating Physiological Time Series and Clinical Notes with Deep Learning for Improved ICU Mortality Prediction [21.919977518774015]
  We study how physiological time series data and clinical notes can be integrated into a unified mortality prediction model. Our results show that a late fusion approach can provide a statistically significant improvement in prediction mortality over using individual modalities in isolation.
  arXiv  Detail & Related papers  (2020-03-24T18:25:49Z)

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.