Cargando…

Multi-Modal Data Analysis for Pneumonia Status Prediction Using Deep Learning (MDA-PSP)

Evaluating several vital signs and chest X-ray (CXR) reports regularly to determine the recovery of the pneumonia patients at general wards is a challenge for doctors. A recent study shows the identification of pneumonia by the history of symptoms and signs including vital signs, CXR, and other clin...

Descripción completa

Detalles Bibliográficos
Autores principales: Sheu, Ruey-Kai, Chen, Lun-Chi, Wu, Chieh-Liang, Pardeshi, Mayuresh Sunil, Pai, Kai-Chih, Huang, Chien-Chung, Chen, Chia-Yu, Chen, Wei-Cheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317409/
https://www.ncbi.nlm.nih.gov/pubmed/35885612
http://dx.doi.org/10.3390/diagnostics12071706
_version_ 1784755049622142976
author Sheu, Ruey-Kai
Chen, Lun-Chi
Wu, Chieh-Liang
Pardeshi, Mayuresh Sunil
Pai, Kai-Chih
Huang, Chien-Chung
Chen, Chia-Yu
Chen, Wei-Cheng
author_facet Sheu, Ruey-Kai
Chen, Lun-Chi
Wu, Chieh-Liang
Pardeshi, Mayuresh Sunil
Pai, Kai-Chih
Huang, Chien-Chung
Chen, Chia-Yu
Chen, Wei-Cheng
author_sort Sheu, Ruey-Kai
collection PubMed
description Evaluating several vital signs and chest X-ray (CXR) reports regularly to determine the recovery of the pneumonia patients at general wards is a challenge for doctors. A recent study shows the identification of pneumonia by the history of symptoms and signs including vital signs, CXR, and other clinical parameters, but they lack predicting the recovery status after starting treatment. The goal of this paper is to provide a pneumonia status prediction system for the early affected patient’s discharge from the hospital within 7 days or late discharge more than 7 days. This paper aims to design a multimodal data analysis for pneumonia status prediction using deep learning classification (MDA-PSP). We have developed a system that takes an input of vital signs and CXR images of the affected patient with pneumonia from admission day 1 to day 3. The deep learning then classifies the health status improvement or deterioration for predicting the possible discharge state. Therefore, the scope is to provide a highly accurate prediction of the pneumonia recovery on the 7th day after 3-day treatment by the SHAP (SHapley Additive exPlanation), imputation, adaptive imputation-based preprocessing of the vital signs, and CXR image feature extraction using deep learning based on dense layers-batch normalization (BN) with class weights for the first 7 days’ general ward patient in MDA-PSP. A total of 3972 patients with pneumonia were enrolled by de-identification with an adult age of 71 mean ± 17 sd and 64% of them were male. After analyzing the data behavior, appropriate improvement measures are taken by data preprocessing and feature vectorization algorithm. The deep learning method of Dense-BN with SHAP features has an accuracy of 0.77 for vital signs, 0.92 for CXR, and 0.75 for the combined model with class weights. The MDA-PSP hybrid method-based experiments are proven to demonstrate higher prediction accuracy of 0.75 for pneumonia patient status. Henceforth, the hybrid methods of machine and deep learning for pneumonia patient discharge are concluded to be a better approach.
format Online
Article
Text
id pubmed-9317409
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-93174092022-07-27 Multi-Modal Data Analysis for Pneumonia Status Prediction Using Deep Learning (MDA-PSP) Sheu, Ruey-Kai Chen, Lun-Chi Wu, Chieh-Liang Pardeshi, Mayuresh Sunil Pai, Kai-Chih Huang, Chien-Chung Chen, Chia-Yu Chen, Wei-Cheng Diagnostics (Basel) Article Evaluating several vital signs and chest X-ray (CXR) reports regularly to determine the recovery of the pneumonia patients at general wards is a challenge for doctors. A recent study shows the identification of pneumonia by the history of symptoms and signs including vital signs, CXR, and other clinical parameters, but they lack predicting the recovery status after starting treatment. The goal of this paper is to provide a pneumonia status prediction system for the early affected patient’s discharge from the hospital within 7 days or late discharge more than 7 days. This paper aims to design a multimodal data analysis for pneumonia status prediction using deep learning classification (MDA-PSP). We have developed a system that takes an input of vital signs and CXR images of the affected patient with pneumonia from admission day 1 to day 3. The deep learning then classifies the health status improvement or deterioration for predicting the possible discharge state. Therefore, the scope is to provide a highly accurate prediction of the pneumonia recovery on the 7th day after 3-day treatment by the SHAP (SHapley Additive exPlanation), imputation, adaptive imputation-based preprocessing of the vital signs, and CXR image feature extraction using deep learning based on dense layers-batch normalization (BN) with class weights for the first 7 days’ general ward patient in MDA-PSP. A total of 3972 patients with pneumonia were enrolled by de-identification with an adult age of 71 mean ± 17 sd and 64% of them were male. After analyzing the data behavior, appropriate improvement measures are taken by data preprocessing and feature vectorization algorithm. The deep learning method of Dense-BN with SHAP features has an accuracy of 0.77 for vital signs, 0.92 for CXR, and 0.75 for the combined model with class weights. The MDA-PSP hybrid method-based experiments are proven to demonstrate higher prediction accuracy of 0.75 for pneumonia patient status. Henceforth, the hybrid methods of machine and deep learning for pneumonia patient discharge are concluded to be a better approach. MDPI 2022-07-13 /pmc/articles/PMC9317409/ /pubmed/35885612 http://dx.doi.org/10.3390/diagnostics12071706 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sheu, Ruey-Kai
Chen, Lun-Chi
Wu, Chieh-Liang
Pardeshi, Mayuresh Sunil
Pai, Kai-Chih
Huang, Chien-Chung
Chen, Chia-Yu
Chen, Wei-Cheng
Multi-Modal Data Analysis for Pneumonia Status Prediction Using Deep Learning (MDA-PSP)
title Multi-Modal Data Analysis for Pneumonia Status Prediction Using Deep Learning (MDA-PSP)
title_full Multi-Modal Data Analysis for Pneumonia Status Prediction Using Deep Learning (MDA-PSP)
title_fullStr Multi-Modal Data Analysis for Pneumonia Status Prediction Using Deep Learning (MDA-PSP)
title_full_unstemmed Multi-Modal Data Analysis for Pneumonia Status Prediction Using Deep Learning (MDA-PSP)
title_short Multi-Modal Data Analysis for Pneumonia Status Prediction Using Deep Learning (MDA-PSP)
title_sort multi-modal data analysis for pneumonia status prediction using deep learning (mda-psp)
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317409/
https://www.ncbi.nlm.nih.gov/pubmed/35885612
http://dx.doi.org/10.3390/diagnostics12071706
work_keys_str_mv AT sheurueykai multimodaldataanalysisforpneumoniastatuspredictionusingdeeplearningmdapsp
AT chenlunchi multimodaldataanalysisforpneumoniastatuspredictionusingdeeplearningmdapsp
AT wuchiehliang multimodaldataanalysisforpneumoniastatuspredictionusingdeeplearningmdapsp
AT pardeshimayureshsunil multimodaldataanalysisforpneumoniastatuspredictionusingdeeplearningmdapsp
AT paikaichih multimodaldataanalysisforpneumoniastatuspredictionusingdeeplearningmdapsp
AT huangchienchung multimodaldataanalysisforpneumoniastatuspredictionusingdeeplearningmdapsp
AT chenchiayu multimodaldataanalysisforpneumoniastatuspredictionusingdeeplearningmdapsp
AT chenweicheng multimodaldataanalysisforpneumoniastatuspredictionusingdeeplearningmdapsp