Dataset Information

Assessing fracture risk using gradient boosting machine (GBM) models.

ABSTRACT: Advanced bone imaging with quantitative computed tomography (QCT) has had limited success in significantly improving fracture prediction beyond standard areal bone mineral density (aBMD) measurements. Thus, we examined whether a machine learning paradigm, gradient boosting machine (GBM) modeling, which can incorporate diverse measurements of bone density and geometry from central QCT imaging and of bone microstructure from high-resolution peripheral QCT imaging, can improve fracture prediction. We studied two cohorts of postmenopausal women: 105 with and 99 without distal forearm fractures (Distal Forearm Cohort) and 40 with at least one grade 2 or 3 vertebral deformity and 78 with no vertebral fracture (Vertebral Cohort). Within each cohort, individual bone density, structure, or strength variables had areas under receiver operating characteristic curves (AUCs) ranging from 0.50 to 0.84 (median 0.61) for discriminating women with and without fracture. Using all possible variables in the GBM model, the AUCs were close to 1.0. Fracture predictions in the Vertebral Cohort using the GBM models built with the Distal Forearm Cohort had AUCs of 0.82-0.95, while predictions in the Distal Forearm Cohort using models built with the Vertebral Cohort had AUCs of 0.80-0.83. Attempts at capturing a comparable parametric model using the top variables from the Distal Forearm Cohort resulted in resulted in an AUC of 0.81. Relatively high AUCs for differing fracture types suggest that an underlying fracture propensity is being captured by this modeling approach. More complex modeling, such as with GBM, creates stronger fracture predictions and may allow deeper insights into information provided by advanced bone imaging techniques.

SUBMITTER: Atkinson EJ

PROVIDER: S-EPMC3408850 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Assessing fracture risk using gradient boosting machine (GBM) models.

Atkinson Elizabeth J EJ Therneau Terry M TM Melton L Joseph LJ Camp Jon J JJ Achenbach Sara J SJ Amin Shreyasee S Khosta Sundeep S

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research 20120601 6

Advanced bone imaging with quantitative computed tomography (QCT) has had limited success in significantly improving fracture prediction beyond standard areal bone mineral density (aBMD) measurements. Thus, we examined whether a machine learning paradigm, gradient boosting machine (GBM) modeling, which can incorporate diverse measurements of bone density and geometry from central QCT imaging and of bone microstructure from high-resolution peripheral QCT imaging, can improve fracture prediction. ...[more]

PMID: 22367889

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Project description:BACKGROUND:Artificial intelligence-enabled electronic health record (EHR) analysis can revolutionize medical practice from the diagnosis and prediction of complex diseases to making recommendations in patient care, especially for chronic conditions such as chronic kidney disease (CKD), which is one of the most frequent complications in patients with diabetes and is associated with substantial morbidity and mortality. OBJECTIVE:The longitudinal prediction of health outcomes requires effective representation of temporal data in the EHR. In this study, we proposed a novel temporal-enhanced gradient boosting machine (GBM) model that dynamically updates and ensembles learners based on new events in patient timelines to improve the prediction accuracy of CKD among patients with diabetes. METHODS:Using a broad spectrum of deidentified EHR data on a retrospective cohort of 14,039 adult patients with type 2 diabetes and GBM as the base learner, we validated our proposed Landmark-Boosting model against three state-of-the-art temporal models for rolling predictions of 1-year CKD risk. RESULTS:The proposed model uniformly outperformed other models, achieving an area under receiver operating curve of 0.83 (95% CI 0.76-0.85), 0.78 (95% CI 0.75-0.82), and 0.82 (95% CI 0.78-0.86) in predicting CKD risk with automatic accumulation of new data in later years (years 2, 3, and 4 since diabetes mellitus onset, respectively). The Landmark-Boosting model also maintained the best calibration across moderate- and high-risk groups and over time. The experimental results demonstrated that the proposed temporal model can not only accurately predict 1-year CKD risk but also improve performance over time with additionally accumulated data, which is essential for clinical use to improve renal management of patients with diabetes. CONCLUSIONS:Incorporation of temporal information in EHR data can significantly improve predictive model performance and will particularly benefit patients who follow-up with their physicians as recommended.

Project description:BackgroundImproving feed efficiency (FE) is a major challenge in pig production. This complex trait is characterized by a high variability. Therefore, the identification of predictors of FE may be a relevant strategy to reduce phenotyping efforts in breeding and selection programs. The aim of this study was to investigate the suitability of expressed muscle genes in prediction of FE traits in growing pigs. The approach considered different transcriptomics experiments to cover a large range of FE values and identify reliable predictors.ResultsMicroarrays data were obtained from longissimus muscles of two lines divergently selected for residual feed intake (RFI). Pigs (n = 71) from three experiments belonged to generations 6 to 8 of selection, were fed either a diet with a standard composition or a diet rich in fiber and lipids, received feed ad libitum or at restricted level, and weighed between 80 and 115 kg at slaughter. For each pig, breeding value for RFI was estimated (RFI-BV), and feed conversion ratio (FCR) and energy-based feed conversion ratio (FCRe) were calculated during the test periods. Gradient boosting algorithms were used on the merged muscle transcriptomes to identify very important predictors of FE traits. About 20,405 annotated molecular probes were commonly expressed in longissimus muscle across experiments. Six to 267 expressed muscle genes covering a variety of biological processes were found as important predictors for RFI-BV (R2 = 0.63-0.65), FCR (R2 = 0.61-0.70) and FCRe (R2 = 0.49-0.52). The error of prediction was less than 8% for FCR. Altogether, 56 predictors were common to RFI-BV and FCR. Expression levels of 24 target genes were further measured by qPCR. Linear regression confirmed the good accuracy of combining mRNA levels of these genes to fit FE traits (RFI-BV: R2 = 0.73, FRC: R2 = 0.76; FCRe: R2 = 0.75). Stepwise regression procedure highlighted 10 genes (FKBP5, MUM1, AKAP12, FYN, TMED3, PHKB, TGF, SOCS6, ILR4, and FRAS1) in a linear combination predicting FCR and FCRe. In addition, FKBP5 and expression levels of five other genes (IGF2, SERINC3, CSRNP3, EZR and RPL16) significantly contributed to RFI-BV.ConclusionIt was possible to identify few genes expressed in muscle that might be reliable predictors of feed efficiency.

Dataset Information

Assessing fracture risk using gradient boosting machine (GBM) models.

Publications

Assessing fracture risk using gradient boosting machine (GBM) models.

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