Amit Gupta, Neerja Gupta, Madhulika Kabra, Tejinder Kaur, Pavan Gabra, Maroof A Khan, Manisha Jana
Impact of Structured Reporting of Skeletal Survey in Skeletal Dysplasia: A Single Institution Experience
The Indian journal of radiology & imaging
acute exacerbations copd aecopd episodes breathlessness cough sputum associated risk hospitalisation progressive lung function decline death often missed diagnosed late accurate timely intervention improve poor outcomes digital tools used capture symptoms clinical data copd study aims apply machine learning largest available realworld digital dataset develop aecopd prediction tools could used support early intervention improve clinical outcomes create validate machine learning predictive model forecasts exacerbations copd 18 days advance model based routine patiententered data mycopd selfmanagement app adaptations adaboost algorithm employed machine learning approaches dataset included 506 patients users 2017 2021 55066 app records available stable copd event labels 1263 records aecopd event labels data used training model included copd assessment test cat scores symptom scores smoking history previous exacerbation frequency exacerbation records used model confined 18 days preceding selfreported exacerbation event theeasyensemble classifier resulted sensitivity 670 specificity 65 positive predictive value ppv 50 negative predictive value npv 989 adaboost model costsensitive decision tree resulted sensitivity 350 specificity 890 ppv 708 npv 983 preliminary analysis demonstrates machine learning approaches realworld data widely deployed digital therapeutic potential predict aecopd used confidently exclude risk exacerbations copd within next 8 days