{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T12:30:35Z","timestamp":1791808235683,"version":"4.3.4"},"reference-count":74,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Respiratory diseases represent a persistent global health challenge, underscoring the need for intelligent, accurate, and personalized diagnostic and therapeutic systems. Existing methods frequently suffer from limitations in diagnostic precision, lack of individualized treatment, and constrained adaptability to complex clinical scenarios. To address these challenges, our study introduces a modular AI-powered framework that integrates an audio-based disease classification model with simulated molecular biomarker profiles to evaluate the feasibility of future multimodal diagnostic extensions, alongside a synthetic-data-driven prescription recommendation engine. The disease classification model analyzes respiratory sound recordings and accurately distinguishes among eight clinical classes: bronchiectasis, pneumonia, upper respiratory tract infection (URTI), lower respiratory tract infection (LRTI), asthma, chronic obstructive pulmonary disease (COPD), bronchiolitis, and healthy respiratory state. The proposed model achieved a classification accuracy of 99.99% on a holdout test set, including 94.2% accuracy on pediatric samples. In parallel, the prescription module provides individualized treatment recommendations comprising drug, dosage, and frequency trained on a carefully constructed synthetic dataset designed to emulate real-world prescribing logic.The model achieved over 99% accuracy in medication prediction tasks, outperforming baseline models such as those discussed in research. Minimal misclassification in the confusion matrix and strong clinician agreement on 200 prescriptions (Cohen\u2019s \u03ba = 0.91 [0.87\u20130.94] for drug selection, 0.78 [0.74\u20130.81] for dosage, 0.96 [0.93\u20130.98] for frequency) further affirm the system\u2019s reliability. Adjusted clinician disagreement rates were 2.7% (drug), 6.4% (dosage), and 1.5% (frequency). SHAP analysis identified age and smoking as key predictors, enhancing model explainability. Dosage accuracy was 91.3%, and most disagreements occurred in renal-impaired and pediatric cases. However, our study is presented strictly as a proof-of-concept. The use of synthetic data and the absence of access to real patient records constitute key limitations. A trialed clinical deployment was conducted under a controlled environment with a positive rate of satisfaction from experts and users, but the proposed system must undergo extensive validation with de-identified electronic medical records (EMRs) and regulatory scrutiny before it can be considered for practical application. Nonetheless, the findings offer a promising foundation for the future development of clinically viable AI-assisted respiratory care tools.<\/jats:p>","DOI":"10.3390\/ijms26157135","type":"journal-article","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T10:28:09Z","timestamp":1753352889000},"page":"7135","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A Multimodal AI Framework for Automated Multiclass Lung Disease Diagnosis from Respiratory Sounds with Simulated Biomarker Fusion and Personalized Medication Recommendation"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7983-2189","authenticated-orcid":false,"family":"Abdullah","sequence":"first","affiliation":[{"name":"Center for Computing Research, Instituto Polit\u00e9cnico Nacional, Mexico City 07738, Mexico"},{"name":"Department of Computer Sciences, Bahria University, Lahore 54600, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6154-1893","authenticated-orcid":false,"given":"Zulaikha","family":"Fatima","sequence":"additional","affiliation":[{"name":"Faculty of Allied Health Sciences, Superior University, Lahore Campus, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9035-642X","authenticated-orcid":false,"given":"Jawad","family":"Abdullah","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, Bahria University, Lahore 54600, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8308-8882","authenticated-orcid":false,"given":"Jos\u00e9 Luis Oropeza","family":"Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Center for Computing Research, Instituto Polit\u00e9cnico Nacional, Mexico City 07738, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3901-3522","authenticated-orcid":false,"given":"Grigori","family":"Sidorov","sequence":"additional","affiliation":[{"name":"Center for Computing Research, Instituto Polit\u00e9cnico Nacional, Mexico City 07738, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jindal, S.K., and Jindal, A. (2022). Respiratory disorders: Contemporary issues in 2020. Biomedical Translational Research: From Disease Diagnosis to Treatment, Springer Nature.","DOI":"10.1007\/978-981-16-8845-4_8"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jadhav, S.P., Singh, H., Hussain, S., Gilhotra, R., Mishra, A., Prasher, P., Krishnan, A., and Gupta, G. (2021). Introduction to lung diseases. Targeting Cellular Signalling Pathways in Lung Diseases, Springer.","DOI":"10.1007\/978-981-33-6827-9_1"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4","DOI":"10.2500\/aap.2015.36.3812","article-title":"Burden of chronic obstructive pulmonary disease: Healthcare costs and beyond","volume":"Volume 36","author":"May","year":"2015","journal-title":"Proceedings of the Allergy and Asthma Proceedings"},{"key":"ref_4","first-page":"570","article-title":"Negative chest X-rays in primary care patients with lung cancer","volume":"56","author":"Stapley","year":"2006","journal-title":"Br. J. Gen. Pract."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"100832","DOI":"10.1016\/j.imu.2021.100832","article-title":"Towards using cough for respiratory disease diagnosis by leveraging Artificial Intelligence: A survey","volume":"29","author":"Ijaz","year":"2022","journal-title":"Inform. Med. Unlocked"},{"key":"ref_6","unstructured":"World Health Organization (2024, August 15). Respiratory Diseases. Available online: https:\/\/platform.who.int\/mortality\/themes\/theme-details\/topics\/topic-details\/MDB\/respiratory-diseases."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1080\/10408444.2019.1584153","article-title":"Computational modeling of lung deposition of inhaled particles in chronic obstructive pulmonary disease (COPD) patients: Identification of gaps in knowledge and data","volume":"49","author":"Ganguly","year":"2019","journal-title":"Crit. Rev. Toxicol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Garcia-Mendez, J.P., Lal, A., Herasevich, S., Tekin, A., Pinevich, Y., Lipatov, K., Wang, H.-Y., Qamar, S., Ayala, I.N., and Khapov, I. (2023). Machine learning for automated classification of abnormal lung sounds obtained from public databases: A systematic review. Bioengineering, 10.","DOI":"10.3390\/bioengineering10101155"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sfayyih, A.H., Sabry, A.H., Jameel, S.M., Sulaiman, N., Raafat, S.M., Humaidi, A.J., and Al Kubaiaisi, Y.M. (2023). Acoustic-based deep learning architectures for lung disease diagnosis: A comprehensive overview. Diagnostics, 13.","DOI":"10.3390\/diagnostics13101748"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1111\/cts.12884","article-title":"Precision medicine, AI, and the future of personalized health care","volume":"14","author":"Johnson","year":"2021","journal-title":"Clin. Transl. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Najjar, R. (2023). Redefining radiology: A review of artificial intelligence integration in medical imaging. Diagnostics, 13.","DOI":"10.20944\/preprints202306.1124.v1"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Siddique, A., Shaukat, K., and Jan, T. (2024). An Intelligent Mechanism to Detect Multi-Factor Skin Cancer. Diagnostics, 14.","DOI":"10.3390\/diagnostics14131359"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Abdullah, J., Siddique, A., Fatima, Z., and Shaukat, K. (2024). Traumatic Brain Injury Structure Detection Using Advanced Wavelet Transformation Fusion Algorithm with Proposed CNN-ViT. Information, 15.","DOI":"10.3390\/info15100612"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e11","DOI":"10.1002\/ctd2.11","article-title":"Biomarkers for Respiratory Diseases: Present Applications and Future Discoveries","volume":"1","author":"Liu","year":"2021","journal-title":"Clin. Transl. Discov."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"31","DOI":"10.3892\/mi.2024.155","article-title":"Precision Medicine for Respiratory Diseases: A Current Viewpoint","volume":"4","author":"Georgakopoulou","year":"2024","journal-title":"Med. Int."},{"key":"ref_16","first-page":"1021","article-title":"Driving Pressure and Lung Compliance Obtained by Electrical Bioimpedance: The Case of Smokers","volume":"29","author":"Kashina","year":"2025","journal-title":"Comput. Sist."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"00797-2024","DOI":"10.1183\/23120541.00797-2024","article-title":"Artificial neural network risk prediction of COPD exacerbations using urine biomarkers","volume":"11","author":"Yousuf","year":"2025","journal-title":"ERJ Open Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"95","DOI":"10.3892\/etm.2025.12845","article-title":"Machine learning identifies potential diagnostic biomarkers associated with ferroptosis in obstructive sleep apnea","volume":"29","author":"Chen","year":"2025","journal-title":"Exp. Ther. Med."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hamilton, F. (2025). Revolutionizing Lung Cancer Detection: Evaluating AI Models for VOC Analysis and Unveiling Key Exhaled Biomarkers. medRxiv.","DOI":"10.1101\/2025.02.03.25321431"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.compbiomed.2003.11.001","article-title":"Two-stage classification of respiratory sound patterns","volume":"35","author":"Sankur","year":"2005","journal-title":"Comput. Biol. Med."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chambres, G., Hanna, P., and Desainte-Catherine, M. (2018, January 4\u20136). Automatic detection of patients with respiratory diseases using lung sound analysis. Proceedings of the 2018 International Conference on Content-Based Multimedia Indexing (CBMI), La Rochelle, France.","DOI":"10.1109\/CBMI.2018.8516489"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"035001","DOI":"10.1088\/1361-6579\/ab03ea","article-title":"An open access database for the evaluation of respiratory sound classification algorithms","volume":"40","author":"Rocha","year":"2019","journal-title":"Physiol. Meas."},{"key":"ref_23","first-page":"535","article-title":"Deep neural network for respiratory sound classification in wearable devices enabled by patient specific model tuning","volume":"14","author":"Acharya","year":"2020","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Monaco, A., Amoroso, N., Bellantuono, L., Pantaleo, E., Tangaro, S., and Bellotti, R. (2020). Multi-time-scale features for accurate respiratory sound classification. Appl. Sci., 10.","DOI":"10.3390\/app10238606"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kim, Y., Hyon, Y., Jung, S.S., Lee, S., Yoo, G., Chung, C., and Ha, T. (2021). Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-96724-7"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hsu, F.S., Huang, S.R., Huang, C.W., Huang, C.J., Cheng, Y.R., Chen, C.C., and Lai, F. (2021). Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database\u2014HF_Lung_V1. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0254134"},{"key":"ref_27","unstructured":"G\u00f3mez, A.F.R., and Orjuela-Ca\u00f1\u00f3n, A.D. (2021, January 26\u201328). Respiratory sounds classification employing a multi-label approach. Proceedings of the 2021 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI), Cali, Colombia."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.bbe.2020.11.003","article-title":"Automatic identification of respiratory diseases from stethoscopic lung sound signals using ensemble classifiers","volume":"41","author":"Fraiwan","year":"2021","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e369","DOI":"10.7717\/peerj-cs.369","article-title":"Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease","volume":"7","author":"Srivastava","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"012055","DOI":"10.1088\/1742-6596\/1937\/1\/012055","article-title":"Wearables for respiratory sound classification","volume":"1937","author":"Shivapathy","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"108589","DOI":"10.1016\/j.apacoust.2021.108589","article-title":"Accurate respiratory sound classification model based on piccolo pattern","volume":"188","author":"Tasar","year":"2022","journal-title":"Appl. Acoust."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1109\/TBCAS.2022.3204910","article-title":"SPRSound: Open-source SJTU pediatric respiratory sound database","volume":"16","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Dar, J.A., Srivastava, K.K., and Mishra, A. (2023). Lung anomaly detection from respiratory sound database (sound signals). Comput. Biol. Med., 164.","DOI":"10.1016\/j.compbiomed.2023.107311"},{"key":"ref_34","first-page":"1077","article-title":"Respiratory Disease Pre-Diagnosis through a Novel Pattern Classification Algorithm based on Associative Memories","volume":"28","year":"2024","journal-title":"Computaci\u00f3n y Sistemas"},{"key":"ref_35","first-page":"1321","article-title":"Framework to Support Radiologist Personnel in the Diagnosis of Diseases in Medical Images Using Deep Learning and Personalized DICOM Tags","volume":"28","author":"Rivera","year":"2024","journal-title":"Computaci\u00f3n y Sistemas"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1007\/s12065-021-00565-2","article-title":"Precision\u2013recall curve (PRC) classification trees","volume":"15","author":"Miao","year":"2022","journal-title":"Evol. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kamarudin, A.N., Cox, T., and Kolamunnage-Dona, R. (2017). Time-dependent ROC curve analysis in medical research: Current methods and applications. BMC Med Res. Methodol., 17.","DOI":"10.1186\/s12874-017-0332-6"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"21525","DOI":"10.1007\/s11042-023-16160-9","article-title":"Ensemble of loss functions to improve generalizability of deep metric learning methods","volume":"83","author":"Zabihzadeh","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_39","first-page":"989","article-title":"Performance Comparison of Stereo Correspondence Algorithms in Dense Image Matching","volume":"28","author":"Boukhatem","year":"2024","journal-title":"Comput. Sist."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Caceres Rossi, R. (2025, July 11). PulmoDataSets: A Curated Collection of Pulmonary and Respiratory Disease Datasets. R Package Version 0.1.0. Available online: https:\/\/github.com\/lightbluetitan\/pulmodatasets.","DOI":"10.32614\/CRAN.package.PulmoDataSets"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Murugesh, T.S., Vasudevan, S.K., and Pulari, S.R. (2024). Python: A Practical Learning Approach, CRC Press.","DOI":"10.1201\/9781032712673"},{"key":"ref_42","unstructured":"Morales, S., Claris\u00f3, R., and Cabot, J. (2024). A Framework to Model ML Engineering Processes. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1007\/s13755-024-00283-w","article-title":"Artificial intelligence-based framework to identify the abnormalities in the COVID-19 disease and other common respiratory diseases from digital stethoscope data using deep CNN","volume":"12","author":"Lella","year":"2024","journal-title":"Health Inf. Sci. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1007\/s41781-024-00114-3","article-title":"PanDA: Production and Distributed Analysis System","volume":"8","author":"Maeno","year":"2024","journal-title":"Comput. Softw. Big Sci."},{"key":"ref_45","unstructured":"Buede, D.M., and Miller, W.D. (2024). The Engineering Design of Systems: Models and Methods, John Wiley & Sons."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Krause, J., Classen, C., Dey, D., Lausberg, E., Kessler, L., Eggermann, T., Kurth, I., Begemann, M., and Kraft, F. (2024). CNVizard: A lightweight streamlit application for an interactive analysis of copy number variants. bioRxiv.","DOI":"10.1101\/2024.06.14.598969"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Rocha, B.M., Filos, D., Mendes, L., Vogiatzis, I., Perantoni, E., Kaimakamis, E., and Maglaveras, N. (2017, January 18\u201321). A respiratory sound database for the development of automated classification. Proceedings of the Precision Medicine Powered by pHealth and Connected Health: ICBHI 2017, Thessaloniki, Greece.","DOI":"10.1007\/978-981-10-7419-6_6"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.compbiomed.2007.07.001","article-title":"Design of a DSP-based instrument for real-time classification of pulmonary sounds","volume":"38","author":"Alsmadi","year":"2008","journal-title":"Comput. Biol. Med."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Brunese, L., Mercaldo, F., Reginelli, A., and Santone, A. (2022). A neural network-based method for respiratory sound analysis and lung disease detection. Appl. Sci., 12.","DOI":"10.3390\/app12083877"},{"key":"ref_50","first-page":"CCRPM-S530","article-title":"Analysis of respiratory sounds: State of the art","volume":"2","author":"Reichert","year":"2008","journal-title":"Clin. Med. Circ. Respir. Pulm. Med."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"182938","DOI":"10.1155\/2014\/182938","article-title":"Detection of lungs status using morphological complexities of respiratory sounds","volume":"2014","author":"Mondal","year":"2014","journal-title":"Sci. World J."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Kahya, Y.P., Yeginer, M., and Bilgic, B. (September, January 30). Classifying respiratory sounds with different feature sets. Proceedings of the 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, New York, NY, USA.","DOI":"10.1109\/IEMBS.2006.4398041"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"107188","DOI":"10.1016\/j.apacoust.2019.107188","article-title":"A constrained tonal semi-supervised non-negative matrix factorization to classify presence\/absence of wheezing in respiratory sounds","volume":"161","year":"2020","journal-title":"Appl. Acoust."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Shi, Y., Li, Y., Cai, M., and Zhang, X.D. (2019). A lung sound category recognition method based on wavelet decomposition and BP neural network. Int. J. Biol. Sci., 15.","DOI":"10.7150\/ijbs.29863"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Gupta, R., Singh, R., Travieso-Gonz\u00e1lez, C.M., Burget, R., and Dutta, M.K. (2024). DeepRespNet: A deep neural network for classification of respiratory sounds. Biomed. Signal Process. Control, 93.","DOI":"10.1016\/j.bspc.2024.106191"},{"key":"ref_56","unstructured":"Aliane, C.V. (2020). Implementation of Automated Machine Learning for the Support of Data Quality Processes. [Ph.D. Thesis, University of Applied Sciences Technikum Wien]."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"240179","DOI":"10.1183\/16000617.0179-2024","article-title":"Neutrophilic Inflammation in Bronchiectasis","volume":"34","author":"Chalmers","year":"2025","journal-title":"Eur. Respir. Rev."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"D\u00edez-Padrisa, N., Bassat, Q., Machevo, S., Quint\u00f3, L., Morais, L., Nhampossa, T., and Roca, A. (2010). Procalcitonin and C-Reactive Protein for Invasive Bacterial Pneumonia Diagnosis among Children in Mozambique, a Malaria-Endemic Area. PLoS ONE, 5.","DOI":"10.1371\/journal.pone.0013226"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Globenko, A.A., Kuzin, G.V., Rydlovskaya, A.V., Isaeva, E.I., Vetrova, E.N., Pritchina, T.Y.N., and Nebolsin, V.E. (2023). Curtailing Virus-Induced Inflammation in Respiratory Infections: Emerging Strategies for Therapeutic Interventions. Front. Pharmacol., 14.","DOI":"10.3389\/fphar.2023.1087850"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.cyto.2017.12.028","article-title":"Altered Regulatory Cytokine Profiles in Cases of Pediatric Respiratory Syncytial Virus Infection","volume":"103","author":"Fan","year":"2018","journal-title":"Cytokine"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"107130","DOI":"10.1016\/j.rmed.2023.107130","article-title":"Imaging-Derived Biomarkers in Asthma: Current Status and Future Perspectives","volume":"208","author":"Pompe","year":"2023","journal-title":"Respir. Med."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Phillips, K.M., Lavere, P.F., Hanania, N.A., and Adrish, M. (2025). The Emerging Biomarkers in Chronic Obstructive Pulmonary Disease: A Narrative Review. Diagnostics, 15.","DOI":"10.3390\/diagnostics15101245"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.1164\/rccm.201712-2567OC","article-title":"Reduced Nasal Viral Load and IFN Responses in Infants with Respiratory Syncytial Virus Bronchiolitis and Respiratory Failure","volume":"198","author":"Thwaites","year":"2018","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_64","unstructured":"Uhl\u00e9n, M.e.a. (2024, November 20). The Human Protein Atlas: Tissue-Based Map of the Human Proteome. Online Database. Available online: https:\/\/www.proteinatlas.org\/."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","article-title":"Deep learning for time series classification: A review","volume":"33","author":"Forestier","year":"2019","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2020.12.010","article-title":"Review on Convolutional Neural Networks (CNN) in vegetation remote sensing","volume":"173","author":"Kattenborn","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_67","unstructured":"Egan, S., Fedorko, W., Lister, A., Pearkes, J., and Gay, C. (2017). Long Short-Term Memory (LSTM) networks with jet constituents for boosted top tagging at the LHC. arXiv."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.visres.2004.07.042","article-title":"Modeling the influence of task on attention","volume":"45","author":"Navalpakkam","year":"2005","journal-title":"Vis. Res."},{"key":"ref_69","first-page":"37","article-title":"Enhancing Audio Classification Through MFCC Feature Extraction and Data Augmentation with CNN and RNN Models","volume":"15","author":"Rezaul","year":"2024","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Ibrahim, A.M., Abdel-Aziz, H.R., Mohamed, H.A.H., Zaghamir, D.E.F., Wahba, N.M.I., Hassan, G.A., Shaban, M., El-Nablaway, M., Aldughmi, O.N., and Aboelola, T.H. (2024). Balancing confidentiality and care coordination: Challenges in patient privacy. BMC Nurs., 23.","DOI":"10.1186\/s12912-024-02231-1"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"4957","DOI":"10.1109\/TPAMI.2024.3362821","article-title":"Synthetic Data in Human Analysis: A Survey","volume":"46","author":"Joshi","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_72","unstructured":"Kumari, G., Sinha, A., and Ekbal, A. Unintended Bias Detection and Mitigation in Misogynous Memes. Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Newaz, A., Mohosheu, M.S., Al Noman, M.A., and Jabid, T. (2024, January 24\u201326). iBRF: Improved Balanced Random Forest Classifier. Proceedings of the 2024 35th Conference of Open Innovations Association (FRUCT), Tampere, Finland.","DOI":"10.23919\/FRUCT61870.2024.10516372"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Shaikh, T.A., Islam, T.U., Mir, S.R., Namgail, T., and Gulzar, I.U.H. (2024). Deep Ensemble Learning for Intelligent Healthcare Computing: A Case Study of Alzheimer\u2019s Disease. Int. J. Next-Gener. Comput., 15.","DOI":"10.47164\/ijngc.v15i2.1475"}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/15\/7135\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:15:10Z","timestamp":1760033710000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/15\/7135"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,24]]},"references-count":74,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["ijms26157135"],"URL":"https:\/\/doi.org\/10.3390\/ijms26157135","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,24]]}}}