A machine learning model for supporting symptom-based referral and diagnosis of bronchitis and pneumonia in limited resource settings

• Autorzy: Stokes Katy , Castaldo Rossana , Franzese Monica , Salvatore Marco , Fico Giuseppe , Pokvic Lejla Gurbeta , Badnjevic Almir , Pecchia Leandro

Identyfikatory

DOI

10.1016/j.bbe.2021.09.002

• Języki publikacji: EN

Abstrakty

EN

Pneumonia is a leading cause of mortality in limited resource settings (LRS), which are common in low- and middle-income countries (LMICs). Accurate referrals can reduce the devastating impact of pneumonia, especially in LRS. Discriminating pneumonia from other respiratory conditions based only on symptoms is a major challenge. Machine learning has shown promise in overcoming the diagnostic difficulties of pneumonia (i.e., low specificity of symptoms, lack of accessible diagnostic tests and varied clinical presentation). Many scientific papers are now focusing on deep-learning methods applied to clinical images, which is unaffordable for initial patient referral in LMICs. The current study used a dataset of 4500 patients (1500 patients affected by bronchitis, 3000 by pneumonia) from a middle-income country, containing information on subject population characteristics, symptoms and laboratory test results. Manual feature selection was performed, focusing on clinical symptoms that are easily measurable in LRS and in community settings. Three common machine learning methods were tested and compared: logistic regression; decision tree and support vector machine. Models were developed through a holdout process of training-validation and testing. We focused on six clinically relevant, easily interpreted patient symptoms as best indicators for pneumonia. Our final model was a decision tree, achieving an AUC of 93%, with the advantage of being fully intelligible and easily interpreted. The performance achieved suggested that intelligible machine learning models can enhance symptom-based referral of pneumonia in LRS and in community settings.

Słowa kluczowe

EN

machine learning; healthcare; screening tool; pneumonia diagnosis; LMICs

PL

uczenie maszynowe; ochrona zdrowia; zapalenie płuc; LMICs

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 4
• Strony: 1288--1302
• Opis fizyczny: Bibliogr. 79 poz., rys., tab., wykr.

Twórcy

autor

Stokes Katy

• University of Warwick, Coventry, UK

autor

Castaldo Rossana

• IRCCS SDN, Via E. Gianturco, 113, 80143 Naples, Italy

autor

Franzese Monica

• IRCCS SDN, Via E. Gianturco, Naples, Italy

autor

Salvatore Marco

• IRCCS SDN, Via E. Gianturco, Naples, Italy

autor

Fico Giuseppe

• Life Supporting Technologies, Universidad Politécnica de Madrid, Madrid, Spain

autor

Pokvic Lejla Gurbeta

• Medical Device Inspection Laboratory Verlab, Sarajevo, Bosnia and Herzegovina

autor

Badnjevic Almir

• University of Sarajevo Sarajevo, Bosnia and Herzegovina

autor

Pecchia Leandro

• University of Warwick, Coventry, UK

Bibliografia

• [1] Pneumonia. Fact Sheets: World Health Organization; 2019.
• [2] Qazi S, Aboubaker S, MacLean R, Fontaine O, Mantel C, Goodman T, et al. Ending preventable child deaths from pneumonia and diarrhoea by 2025. Development of the integrated Global Action Plan for the Prevention and Control of Pneumonia and Diarrhoea. Arch Dis Childhood. 2015;100: S23.
• [3] Berman S. Epidemiology of acute respiratory infections in children of developing countries. Rev Infect Dis 1991;13: S454–62.
• [4] American Lung Association Scientific, Panel MER. Pneumonia symptoms and diagnosis. 2021.
• [5] Tasaka S. Recent advances in the diagnosis and management of pneumocystis pneumonia. Tuberc Respir Dis (Seoul) 2020;83:132–40.
• [6] Geiling J, Burkle Jr FM, Amundson D, Dominguez-Cherit G, Gomersall CD, Lim ML, et al. Resource-poor settings: infrastructure and capacity building. Chest 2014;146: e156S–e167.
• [7] van Zyl C, Badenhorst M, Hanekom S, Heine M. Unravelling ‘low-resource settings’: a systematic scoping review with qualitative content analysis. BMJ Glob Health 2021;6 e005190.
• [8] Pecchia L, Piaggio D, Maccaro A, Formisano C, Iadanza E. The inadequacy of regulatory frameworks in time of crisis and in low-resource settings: personal protective equipment and COVID-19. Health Technol 2020;10:1375–83.
• [9] Foster KR, Koprowski R, Skufca JD. Machine learning, medical diagnosis, and biomedical engineering research - commentary. Biomed Eng Online 2014;13:94.
• [10] Palaniappan R, Sundaraj K, Ahamed NU. Machine learning in lung sound analysis: a systematic review. Biocybernet Biomed Eng 2013;33:129–35.
• [11] Wardlaw AJ, Rick EM, Pur Ozyigit L, Scadding A, Gaillard EA, Pashley CH. New perspectives in the diagnosis and management of allergic fungal airway disease. J Asthma Allergy 2021;14:557–73.
• [12] Stephen O, Sain M, Maduh UJ, Jeong D-U. An efficient deep learning approach to pneumonia classification in healthcare. J Healthc Eng 2019;2019:4180949.
• [13] Sirazitdinov I, Kholiavchenko M, Mustafaev T, Yixuan Y, Kuleev R, Ibragimov B. Deep neural network ensemble for pneumonia localization from a large-scale chest X-ray database. Comput Electr Eng 2019;78:388–99.
• [14] Yue Z, Ma L, Zhang R. Comparison and validation of deep learning models for the diagnosis of pneumonia. Comput Intell Neurosci 2020;2020:8876798.
• [15] Walsh SLF, Calandriello L, Silva M, Sverzellati N. Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study. Lancet Respir Med 2018;6:837–45.
• [16] Wang K, Zhang X, Huang S, Chen F. Automatic detection of pneumonia in chest X-ray images using cooperative convolutional neural networks. 2019. pp. 328-40.
• [17] Wu X, Song Z, Zhai X, Zuo L, Mei X, Xiang R, et al. Simultaneous and visual detection of infectious bronchitis virus and Newcastle disease virus by multiple LAMP and lateral flow dipstick. Poult Sci 2019;98:5401–11.
• [18] Masud M, Bairagi AK, Nahid A-A, Sikder N, Rubaiee S, Ahmed A, et al. A pneumonia diagnosis scheme based on hybrid features extracted from chest radiographs using an ensemble learning algorithm. J Healthc Eng 2021;2021:8862089.
• [19] Nahid A-A, Sikder N, Bairagi AK, Razzaque MA, Masud M, Z. Kouzani A, et al. A novel method to identify pneumonia through analyzing chest radiographs employing a multichannel convolutional neural network. Sensors. 2020;20.
• [20] Srivastava A, Jain S, Miranda R, Patil S, Pandya S, Kotecha K. Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease. PeerJ Comput Sci 2021;7 e369.
• [21] Hira S, Bai A, Hira S. An automatic approach based on CNN architecture to detect Covid-19 disease from chest X-ray images. Appl Intell 2020.
• [22] Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, et al. Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy. Radiology 2020;296:E65–71.
• [23] Elgendi M, Nasir MU, Tang Q, Fletcher RR, Howard N, Menon C, et al. The performance of deep neural networks in differentiating chest X-rays of COVID-19 patients from other bacterial and viral pneumonias. Front Med 2020;7.
• [24] Brunese L, Mercaldo F, Reginelli A, Santone A. Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays. Comput Methods Programs Biomed 2020;196 105608.
• [25] Theodoratou E, Al-Jilaihawi S, Woodward F, Ferguson J, Jhass A, Balliet M, et al. The effect of case management on childhood pneumonia mortality in developing countries. Int J Epidemiol 2010;39(Suppl 1):i155–71.
• [26] Marsh DR, Gilroy KE, Van de Weerdt R, Wansi E, Qazi S. Community case management of pneumonia: at a tipping point? Bull World Health Organ 2008;86:381–9.
• [27] Elmardi KA, Malik EM, Abdelgadir T, Ali SH, Elsyed AH, Mudather MA, et al. Feasibility and acceptability of home-based management of malaria strategy adapted to Sudan’s conditions using artemisinin-based combination therapy and rapid diagnostic test. Malar J 2009;8:39.
• [28] Harvey SA, Jennings L, Chinyama M, Masaninga F, Mulholland K, Bell DR. Improving community health worker use of malaria rapid diagnostic tests in Zambia: package instructions, job aid and job aid-plus-training. Malar J 2008;7:160.
• [29] Hamer DH, Brooks ET, Semrau K, Pilingana P, MacLeod WB, Siazeele K, et al. Quality and safety of integrated community case management of malaria using rapid diagnostic tests and pneumonia by community health workers. Pathog Glob Health 2012;106:32–9.
• [30] Gurbeta L, Badnjevic A, Maksimovic M, Omanovic-Miklicanin E, Sejdic E. A telehealth system for automated diagnosis of asthma and chronical obstructive pulmonary disease. J Am Med Inform Assoc 2018;25:1213–7.
• [31] Silva V, Novo ADR, Souza D, Rêgo A. Machine learning to assist in pneumonia decision making: A systematic review of the literature. SBC: Anais do VIII Symposium on Knowledge Discovery, Mining and Learning; 2020. p. 201–8.
• [32] Badnjevic A, Cifrek M, Koruga D, Osmankovic D. Neuro-fuzzy classification of asthma and chronic obstructive pulmonary disease. BMC Med Inf Decis Making 2015;15:S1.
• [33] Kinkade S, Long NA. Acute bronchitis.
• [34] Naydenova E, Tsanas A, Howie S, Casals-Pascual C, De Vos M. The power of data mining in diagnosis of childhood pneumonia. J R Soc Interface 2016;13 20160266.
• [35] Grigull L, Lechner WM. Supporting diagnostic decisions using hybrid and complementary data mining applications: a pilot study in the pediatric emergency department. Pediatr Res 2012;71:725–31.
• [36] Haug PJ, Ferraro JP, Holmen J, Wu X, Mynam K, Ebert M, et al. An ontology-driven, diagnostic modeling system. J Am Med Inform Assoc 2013;20:e102–10.
• [37] Groeneveld GH, van’t Wout JW, Aarts NJ, van Rooden CJ, Verheij TJM, Cobbaert CM, et al. Prediction model for pneumonia in primary care patients with an acute respiratory tract infection: role of symptoms, signs, and biomarkers. BMC Infect Dis. 2019;19:976.
• [38] Ge Y, Wang Q, Wang L,Wu H, Peng C, Wang J, et al. Predicting post-stroke pneumonia using deep neural network approaches. Int J Med Inf 2019;132 103986.
• [39] Bejan CA, Xia F, Vanderwende L, Wurfel MM, Yetisgen-Yildiz M. Pneumonia identification using statistical feature selection. J Am Med Inform Assoc 2012;19:817–23.
• [40] Steurer J, Held U, Spaar A, Bausch B, Zoller M, Hunziker R, et al. A decision aid to rule out pneumonia and reduce unnecessary prescriptions of antibiotics in primary care patients with cough and fever. BMC Med 2011;9:56.
• [41] van Vugt SF, Broekhuizen BDL, Lammens C, Zuithoff NPA, de Jong PA, Coenen S, et al. Use of serum C reactive protein and procalcitonin concentrations in addition to symptoms and signs to predict pneumonia in patients presenting to primary care with acute cough: diagnostic study. BMJ 2013;346:f2450.
• [42] De Santis O, Kilowoko M, Kyungu E, Sangu W, Cherpillod P, Kaiser L, et al. Predictive value of clinical and laboratory features for the main febrile diseases in children living in Tanzania: A prospective observational study. PLoS One. 2017;12:e0173314-e.
• [43] Feng C, Huang Z, Wang L, Chen X, Zhai Y, Zhu F, et al. A novel triage tool of artificial intelligence assisted diagnosis aid system for suspected COVID-19 pneumonia in fever clinics. medRxiv. 2020:2020.03.19.20039099.
• [44] Badnjevic A, Gurbeta L, Custovic E. An expert diagnostic system to automatically identify asthma and chronic obstructive pulmonary disease in clinical settings. Sci Rep. 2018;8:11645.
• [45] Badnjevic A, Cifrek M, Koruga D, Osmankovic D. Neuro-fuzzy classification of asthma and chronic obstructive pulmonary disease. BMC medical informatics and decision making. 2015;15 Suppl 3:S1-S.
• [46] Fraiwan L, Hassanin O, Fraiwan M, Khassawneh B, Ibnian AM, Alkhodari M. Automatic identification of respiratory diseases from stethoscopic lung sound signals using ensemble classifiers. Biocybernet Biomed Eng 2020;41:1–14.
• [47] Pervaiz F, Chavez MA, Ellington LE, Grigsby M, Gilman RH, Miele CH, et al. Building a prediction model for radiographically confirmed pneumonia in Peruvian children: from symptoms to imaging. Chest 2018;154:1385–94.
• [48] Nuzhat S, Ahmed T, Kawser CA, Khan AI, Islam SMR, Shahrin L, et al. Age specific fast breathing in under-five diarrheal children in an urban hospital: Acidosis or pneumonia? PLoS One. 2017;12:e0185414-e.
• [49] Woodhead M, Blasi F, Ewig S, Garau J, Huchon G, Ieven M, et al. Guidelines for the management of adult lower respiratory tract infections-Full version. Clin Microbiol Infect 2011;17:E1–E59.
• [50] Mandell LA, Wunderink RG, Anzueto A, Bartlett JG, Campbell GD, Dean NC, et al. Infectious Diseases Society of America/American Thoracic Society consensus guidelines on the management of community-acquired pneumonia in adults. Clin Infect Dis 2007;44:S27–72.
• [51] Parab S, Bhalerao S. Choosing statistical test. Int J Ayurveda Res 2010;1:187–91.
• [52] Feng C, Huang Z,Wang L, Chen X, Zhai Y, Zhu F, et al. A novel triage tool of artificial intelligence assisted diagnosis aid system for suspected COVID-19 pneumonia in fever clinics. Ann Transl Med 2021;9.
• [53] Aguinis H, Gottfredson RK, Joo H. Best-practice recommendations for defining, identifying, and handling outliers. Organ Res Methods 2013;16:270–301.
• [54] Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 1996;49:1373–9.
• [55] Foster KR, Koprowski R, Skufca JD. Machine learning, medical diagnosis, and biomedical engineering research-commentary. Biomed Eng Online 2014;13:94.
• [56] Castaldo R, Montesinos L, Melillo P, James C, Pecchia L. Ultrashort term HRV features as surrogates of short term HRV: a case study on mental stress detection in real life. BMC Med Inf Decis Making 2019;19:12.
• [57] Hilbe JM. Logistic regression models. CRC Press; 2009.
• [58] Lawrence RL, Wright A. Rule-based classification systems using classification and regression tree (CART) analysis. Photogramm Eng Remote Sens 2001;67:1137–42.
• [59] Noble WS. What is a support vector machine? Nat Biotechnol 2006;24:1565–7.
• [60] Parker C. An analysis of performance measures for binary classifiers. In: IEEE 11th International Conference on Data Mining. IEEE; 2011. p. 517–26.
• [61] Foster KR, Koprowski R, Skufca JD. Machine learning, medical diagnosis, and biomedical engineering research - commentary. Biomed Eng Online 2014;13:94.
• [62] Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 1993;39:561–77.
• [63] Bartlett JG, Mundy LM. Community-acquired pneumonia. N Engl J Med 1995;333:1618–24.
• [64] Kim ES, Park KU, Lee SH, Lee YJ, Park JS, Cho Y-J, et al. Comparison of viral infection in healthcare-associated pneumonia (HCAP) and community-acquired pneumonia (CAP). PLoS One. 2018;13:e0192893-e.
• [65] Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med 2019;17:195.
• [66] Porter P, Abeyratne U, Swarnkar V, Tan J, Ng TW, Brisbane JM, et al. A prospective multicentre study testing the diagnostic accuracy of an automated cough sound centred analytic system for the identification of common respiratory disorders in children. Respir Res 2019;20:81.
• [67] Rother A-K, Schwerk N, Brinkmann F, Klawonn F, Lechner W, Grigull L. Diagnostic support for selected paediatric pulmonary diseases using answer-pattern recognition in questionnaires based on combined data mining applications–a monocentric observational pilot study. PLoS One. 2015;10:e0135180-e.
• [68] Nuzhat S, Ahmed T, Kawser CA, Khan AI, Islam SMR, Shahrin L, et al. Age specific fast breathing in under-five diarrheal children in an urban hospital: Acidosis or pneumonia? PLoS ONE 2017;12 e0185414.
• [69] Feng C, Wang L, Chen X, Zhai Y, Zhu F, Chen H, et al. A novel triage tool of artificial intelligence-assisted diagnosis aid system for suspected COVID-19 pneumonia in fever clinics. medRxiv. 2021:2020.03.19.20039099.
• [70] DeLisle S, Kim B, Deepak J, Siddiqui T, Gundlapalli A, Samore M, et al. Using the electronic medical record to identify community-acquired pneumonia: toward a replicable automated strategy. PLoS ONE 2013;8 e70944.
• [71] Principi N, Esposito S. Biomarkers in pediatric community-acquired pneumonia. Int J Mol Sci 2017;18:447.
• [72] Markus AF, Kors JA, Rijnbeek PR. The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies. J Biomed Inform 2020:103655.
• [73] Porumb M, Iadanza E, Massaro S, Pecchia L. A convolutional neural network approach to detect congestive heart failure. Biomed Signal Process Control 2020;55 101597.
• [74] Porumb M, Stranges S, Pescapè A, Pecchia L. Precision medicine and artificial intelligence: A pilot study on deep learning for hypoglycemic events detection based on ECG. Sci Rep 2020;10:1–16.
• [75] Groeneveld GH, van’t Wout JW, Aarts NJ, van Rooden CJ, Verheij TJM, Cobbaert CM, et al. Prediction model for pneumonia in primary care patients with an acute respiratory tract infection: role of symptoms, signs, and biomarkers. BMC Infect Dis. 2019;19:976.
• [76] Naydenova E, Tsanas A, Howie S, Casals-Pascual C, De Vos M. The power of data mining in diagnosis of childhood pneumonia. J R Soc Interface 2016;13.
• [77] Rother AK, Schwerk N, Brinkmann F, Klawonn F, Lechner W, Grigull L. Diagnostic support for selected paediatric pulmonary diseases using answer-pattern recognition in questionnaires based on combined data mining applications–a monocentric observational pilot study. PLoS ONE 2015;10 e0135180.
• [78] van Vugt SF, Broekhuizen BD, Lammens C, Zuithoff NP, de Jong PA, Coenen S, et al. Use of serum C reactive protein and procalcitonin concentrations in addition to symptoms and signs to predict pneumonia in patients presenting to primary care with acute cough: diagnostic study. BMJ 2013;346 f2450.
• [79] De Santis O, Kilowoko M, Kyungu E, Sangu W, Cherpillod P, Kaiser L, et al. Predictive value of clinical and laboratory features for the main febrile diseases in children living in Tanzania: A prospective observational study. PLoS ONE 2017;12 e0173314.