Improving Early Sepsis Prediction with Multi Modal Learning
- URL: http://arxiv.org/abs/2107.11094v1
- Date: Fri, 23 Jul 2021 09:25:31 GMT
- Title: Improving Early Sepsis Prediction with Multi Modal Learning
- Authors: Fred Qin, Vivek Madan, Ujjwal Ratan, Zohar Karnin, Vishaal Kapoor,
Parminder Bhatia, and Taha Kass-Hout
- Abstract summary: 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.
- Score: 5.129463113166068
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Sepsis is a life-threatening disease with high morbidity, mortality and
healthcare costs. The early prediction and administration of antibiotics and
intravenous fluids is considered crucial for the treatment of sepsis and can
save potentially millions of lives and billions in health care costs.
Professional clinical care practitioners have proposed clinical criterion which
aid in early detection of sepsis; however, performance of these criterion is
often limited. Clinical text provides essential information to estimate the
severity of the sepsis in addition to structured clinical data. In this study,
we explore how clinical text can complement structured data towards early
sepsis prediction task. In this paper, we propose multi modal model which
incorporates both structured data in the form of patient measurements as well
as textual notes on the patient. 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. On the MIMIC-III dataset containing records of ICU
admissions, we show that by using these notes, one achieves an improvement of
6.07 points in a standard utility score for Sepsis prediction and 2.89% in
AUROC score. 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.
Related papers
- SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing [67.8991481023825]
Sepsis is the leading cause of in-hospital mortality in the USA.
Existing predictive models are usually trained on high-quality data with few missing information.
For the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm.
arXiv Detail & Related papers (2024-07-24T04:47:36Z) - 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) - 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) - This Patient Looks Like That Patient: Prototypical Networks for
Interpretable Diagnosis Prediction from Clinical Text [56.32427751440426]
In clinical practice such models must not only be accurate, but provide doctors with interpretable and helpful results.
We introduce ProtoPatient, a novel method based on prototypical networks and label-wise attention.
We evaluate the model on two publicly available clinical datasets and show that it outperforms existing baselines.
arXiv Detail & Related papers (2022-10-16T10:12:07Z) - 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) - Literature-Augmented Clinical Outcome Prediction [10.46990394710927]
We introduce techniques to help bridge this gap between EBM and AI-based clinical models.
We propose a novel system that automatically retrieves patient-specific literature based on intensive care (ICU) patient information.
Our model is able to substantially boost predictive accuracy on three challenging tasks in comparison to strong recent baselines.
arXiv Detail & Related papers (2021-11-16T11:19:02Z) - Early Prediction of Mortality in Critical Care Setting in Sepsis
Patients Using Structured Features and Unstructured Clinical Notes [4.387308555401595]
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.
arXiv Detail & Related papers (2021-11-09T19:57:05Z) - 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) - 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)
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.