Miguel Angel Robles-Sanchez, Paloma Amil-Bujan, Cristina Bosch-Farré, Clàudia Coll-Martínez, Maria Jesús Arévalo, Elisenda Anglada, Rebeca Menéndez, Xavier Montalban, Jaume Sastre-Garriga, Lluís Ramió-Torrentà, Carme Bertran-Noguer
An expert patient program to improve the empowerment and quality of life of people with multiple sclerosis: protocol for a multicenter pre-post intervention study
Frontiers in neurology
aim devise machine learning algorithm exploiting preoperative clinical data forecast hazard pneumothorax postcoaxial needle lung biopsy ccnb thereby informing clinical decisionmaking enhancing perioperative care retrospective analysis aggregated clinical imaging data patients lung nodules 3 cm biopsies variable selection done using univariate analysis lasso regression dataset subsequently divided training 80 validation 20 subsets various machine learning ml classifiers employed consolidated approach ascertain paramount model followed individualized risk profiling showcased shapley additive explanations shap 325 patients included study 196 64325 experienced postoperative pneumothorax highrisk factors determined cancer lesiontype gold size depth gaussian naive bayes gnb classifier demonstrated superior prediction area curve auc 082 95 ci 071094 complemented accuracy rate 08 sensitivity 071 specificity 084 f1 score 061 test cohort formulated prognostic algorithm exhibited commendable efficacy preoperatively prognosticating ccnbinduced pneumothorax harboring potential refine personalized risk appraisals steer clinical judgment ameliorate perioperative patient stewardship