Machine Learning for Community-Acquired Pneumonia Diagnosis Using Routine Clinical and Laboratory Data
Background: Community-acquired pneumonia (CAP) is diagnosed based on clinical information, laboratory tests, and chest imaging. However, chest radiography is often inaccessible in primary care, causing variability in clinical diagnosis. This study aims to develop a machine learning model to diagnose...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Journal of Global Antimicrobial Resistance |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213716524004090 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850251819142348800 |
|---|---|
| author | Sung Yoon Lim Eunhye Cho Bokhee Jung Jaeyeon Lee Miyoung Kim Sooyoung Yoo Seyoung Jung Joon Yhup Lee Sejin Nam Hyunju Lee Eu Suk Kim |
| author_facet | Sung Yoon Lim Eunhye Cho Bokhee Jung Jaeyeon Lee Miyoung Kim Sooyoung Yoo Seyoung Jung Joon Yhup Lee Sejin Nam Hyunju Lee Eu Suk Kim |
| author_sort | Sung Yoon Lim |
| collection | DOAJ |
| description | Background: Community-acquired pneumonia (CAP) is diagnosed based on clinical information, laboratory tests, and chest imaging. However, chest radiography is often inaccessible in primary care, causing variability in clinical diagnosis. This study aims to develop a machine learning model to diagnose CAP using only clinical and laboratory data. METHODS: This study included patients who presented with fever and respiratory symptoms to the outpatient clinic or emergency room of a tertiary care center between 2009 and 2018. A total of 10,707 adult patients were randomly divided into training (70%) and test (30%) sets. We analyzed the model for internal validation on 1,364 patients who visited the same institution between August 2019 and December 2020.The performance of the machine-learning models was measured using the area under the receiver operating characteristic curve (AUROC). RESULTS: Among the algorithms tested, eXtreme Gradient Boosting (XGBOOST) achieved the highest AUROC (0.936, 95% CI: 0.924-0.947), followed by the gradient boost (0.931, 95% CI: 0.919-0.943) and random forest (0.926, 95% CI: 0.912-0.938) models in the test set. The most significant independent variables for diagnosing pneumonia were the presence of cough, crackle lung sounds, and CRP levels. In the validation set, XGBOOST achieved an AUC of 0.919 (95% CI: 0.886-0.933), with a sensitivity of 82.30%, specificity of 88.92%, and accuracy of 87.90%. CONCLUSIONS: The machine learning model accurately diagnosed community-acquired pneumonia, indicating its potential to assist in primary care settings without relying on chest imaging. |
| format | Article |
| id | doaj-art-daa7163bed0849c2ba3c98965441c4e7 |
| institution | OA Journals |
| issn | 2213-7165 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Global Antimicrobial Resistance |
| spelling | doaj-art-daa7163bed0849c2ba3c98965441c4e72025-08-20T01:57:48ZengElsevierJournal of Global Antimicrobial Resistance2213-71652024-12-01397210.1016/j.jgar.2024.10.232Machine Learning for Community-Acquired Pneumonia Diagnosis Using Routine Clinical and Laboratory DataSung Yoon Lim0Eunhye Cho1Bokhee Jung2Jaeyeon Lee3Miyoung Kim4Sooyoung Yoo5Seyoung Jung6Joon Yhup Lee7Sejin Nam8Hyunju Lee9Eu Suk Kim10Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, South KoreaDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, South KoreaDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, South KoreaOffice of eHealth Research and Businesses, Seoul National University Bundang Hospital, Seongnam, Republic of KoreaOffice of eHealth Research and Businesses, Seoul National University Bundang Hospital, Seongnam, Republic of KoreaOffice of eHealth Research and Businesses, Seoul National University Bundang Hospital, Seongnam, Republic of KoreaOffice of eHealth Research and Businesses, Seoul National University Bundang Hospital, Seongnam, Republic of KoreaR&D Center, ezCaretech Co., Ltd, Republic of KoreaR&D Center, ezCaretech Co., Ltd, Republic of KoreaDepartment of Pediatrics, Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of KoreaDivisions of Infectious disease, Department of Internal medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, South KoreaBackground: Community-acquired pneumonia (CAP) is diagnosed based on clinical information, laboratory tests, and chest imaging. However, chest radiography is often inaccessible in primary care, causing variability in clinical diagnosis. This study aims to develop a machine learning model to diagnose CAP using only clinical and laboratory data. METHODS: This study included patients who presented with fever and respiratory symptoms to the outpatient clinic or emergency room of a tertiary care center between 2009 and 2018. A total of 10,707 adult patients were randomly divided into training (70%) and test (30%) sets. We analyzed the model for internal validation on 1,364 patients who visited the same institution between August 2019 and December 2020.The performance of the machine-learning models was measured using the area under the receiver operating characteristic curve (AUROC). RESULTS: Among the algorithms tested, eXtreme Gradient Boosting (XGBOOST) achieved the highest AUROC (0.936, 95% CI: 0.924-0.947), followed by the gradient boost (0.931, 95% CI: 0.919-0.943) and random forest (0.926, 95% CI: 0.912-0.938) models in the test set. The most significant independent variables for diagnosing pneumonia were the presence of cough, crackle lung sounds, and CRP levels. In the validation set, XGBOOST achieved an AUC of 0.919 (95% CI: 0.886-0.933), with a sensitivity of 82.30%, specificity of 88.92%, and accuracy of 87.90%. CONCLUSIONS: The machine learning model accurately diagnosed community-acquired pneumonia, indicating its potential to assist in primary care settings without relying on chest imaging.http://www.sciencedirect.com/science/article/pii/S2213716524004090PneumoniaDiagnosisMachine learning |
| spellingShingle | Sung Yoon Lim Eunhye Cho Bokhee Jung Jaeyeon Lee Miyoung Kim Sooyoung Yoo Seyoung Jung Joon Yhup Lee Sejin Nam Hyunju Lee Eu Suk Kim Machine Learning for Community-Acquired Pneumonia Diagnosis Using Routine Clinical and Laboratory Data Journal of Global Antimicrobial Resistance Pneumonia Diagnosis Machine learning |
| title | Machine Learning for Community-Acquired Pneumonia Diagnosis Using Routine Clinical and Laboratory Data |
| title_full | Machine Learning for Community-Acquired Pneumonia Diagnosis Using Routine Clinical and Laboratory Data |
| title_fullStr | Machine Learning for Community-Acquired Pneumonia Diagnosis Using Routine Clinical and Laboratory Data |
| title_full_unstemmed | Machine Learning for Community-Acquired Pneumonia Diagnosis Using Routine Clinical and Laboratory Data |
| title_short | Machine Learning for Community-Acquired Pneumonia Diagnosis Using Routine Clinical and Laboratory Data |
| title_sort | machine learning for community acquired pneumonia diagnosis using routine clinical and laboratory data |
| topic | Pneumonia Diagnosis Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2213716524004090 |
| work_keys_str_mv | AT sungyoonlim machinelearningforcommunityacquiredpneumoniadiagnosisusingroutineclinicalandlaboratorydata AT eunhyecho machinelearningforcommunityacquiredpneumoniadiagnosisusingroutineclinicalandlaboratorydata AT bokheejung machinelearningforcommunityacquiredpneumoniadiagnosisusingroutineclinicalandlaboratorydata AT jaeyeonlee machinelearningforcommunityacquiredpneumoniadiagnosisusingroutineclinicalandlaboratorydata AT miyoungkim machinelearningforcommunityacquiredpneumoniadiagnosisusingroutineclinicalandlaboratorydata AT sooyoungyoo machinelearningforcommunityacquiredpneumoniadiagnosisusingroutineclinicalandlaboratorydata AT seyoungjung machinelearningforcommunityacquiredpneumoniadiagnosisusingroutineclinicalandlaboratorydata AT joonyhuplee machinelearningforcommunityacquiredpneumoniadiagnosisusingroutineclinicalandlaboratorydata AT sejinnam machinelearningforcommunityacquiredpneumoniadiagnosisusingroutineclinicalandlaboratorydata AT hyunjulee machinelearningforcommunityacquiredpneumoniadiagnosisusingroutineclinicalandlaboratorydata AT eusukkim machinelearningforcommunityacquiredpneumoniadiagnosisusingroutineclinicalandlaboratorydata |