title: Risk Assessment for Venous Thromboembolism in Chemotherapy-Treated Ambulatory Cancer Patients.
abstract: To design a precision medicine approach aimed at exploiting significant patterns in data, in order to produce venous thromboembolism (VTE) risk predictors for cancer outpatients that might be of advantage over the currently recommended model (Khorana score).
  
  
  
  Multiple kernel learning (MKL) based on support vector machines and random optimization (RO) models were used to produce VTE risk predictors (referred to as machine learning [ML]-RO) yielding the best classification performance over a training (3-fold cross-validation) and testing set.
  
  
  
  Attributes of the patient data set ( n = 1179) were clustered into 9 groups according to clinical significance. Our analysis produced 6 ML-RO models in the training set, which yielded better likelihood ratios (LRs) than baseline models. Of interest, the most significant LRs were observed in 2 ML-RO approaches not including the Khorana score (ML-RO-2: positive likelihood ratio [+LR] = 1.68, negative likelihood ratio [-LR] = 0.24; ML-RO-3: +LR = 1.64, -LR = 0.37). The enhanced performance of ML-RO approaches over the Khorana score was further confirmed by the analysis of the areas under the Precision-Recall curve (AUCPR), and the approaches were superior in the ML-RO approaches (best performances: ML-RO-2: AUCPR = 0.212; ML-RO-3-K: AUCPR = 0.146) compared with the Khorana score (AUCPR = 0.096). Of interest, the best-fitting model was ML-RO-2, in which blood lipids and body mass index/performance status retained the strongest weights, with a weaker association with tumor site/stage and drugs.
  
  
  
  Although the monocentric validation of the presented predictors might represent a limitation, these results demonstrate that a model based on MKL and RO may represent a novel methodological approach to derive VTE risk classifiers. Moreover, this study highlights the advantages of optimizing the relative importance of groups of clinical attributes in the selection of VTE risk predictors.
authors.affiliation: San Raffaele Roma Open University, Rome, Italy (PF, NS, FG).
authors.forename: Patrizia
authors.initials: P
authors.lastname: Ferroni
authors.affiliation: Department of Enterprise Engineering, University of Rome "Tor Vergata," Rome, Italy (FMZ).
authors.forename: Fabio Massimo
authors.initials: FM
authors.lastname: Zanzotto
authors.affiliation: San Raffaele Roma Open University, Rome, Italy (PF, NS, FG).
authors.forename: Noemi
authors.initials: N
authors.lastname: Scarpato
authors.affiliation: BioBIM (InterInstitutional Multidisciplinary Biobank, IRCCS San Raffaele Pisana, Rome, Italy (SR, FG).|Department of Systems Medicine, Medical Oncology, University of Rome "Tor Vergata," Rome, Italy (SR, MR).
authors.forename: Silvia
authors.initials: S
authors.lastname: Riondino
authors.affiliation: Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University, Rome, Italy (UN).
authors.forename: Umberto
authors.initials: U
authors.lastname: Nanni
authors.affiliation: Department of Systems Medicine, Medical Oncology, University of Rome "Tor Vergata," Rome, Italy (SR, MR).
authors.forename: Mario
authors.initials: M
authors.lastname: Roselli
authors.affiliation: San Raffaele Roma Open University, Rome, Italy (PF, NS, FG).|BioBIM (InterInstitutional Multidisciplinary Biobank, IRCCS San Raffaele Pisana, Rome, Italy (SR, FG).|Department of Systems Medicine, Medical Oncology, University of Rome "Tor Vergata," Rome, Italy (SR, MR).
authors.forename: Fiorella
authors.initials: F
authors.lastname: Guadagni
mesh_terms: D000925:Anticoagulants; D000970:Antineoplastic Agents / Q000009:adverse effects* / Q000627:therapeutic use; D015992:Body Mass Index; D006304:Health Status; D006801:Humans; D008055:Lipids / Q000097:blood; D000069550:Machine Learning; D009367:Neoplasm Staging; D009369:Neoplasms / Q000188:drug therapy*; D010045:Outpatients; D018570:Risk Assessment; D012307:Risk Factors; D054556:Venous Thromboembolism / Q000139:chemically induced*
chemical_list: D000925:Anticoagulants; D000970:Antineoplastic Agents; D008055:Lipids
keywords: cancer; clinical decision support systems; machine learning; random optimization; venous thromboembolism