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Erratum to: Methods for evaluating medical tests and biomarkers

Diagnostic and prognostic research 2017年第1期1卷 11页

作者： Gowri Gopalakrishna Miranda Langendam Rob Scholten Patrick Bossuyt Mariska Leeflang Anna Noel-Storr James Thomas Iain Marshall Byron Wallace Penny Whiting Clare Davenport Gowri GopalaKrishna Isabel de Salis Sue Mallett Robert Wolff Richard Riley Marie Westwood Jos Kleinen Gary Collins Hans Reitsma Karel Moons Antonia Zapf Annika Hoyer Katharina Kramer Oliver Kuss J Ensor J J Deeks E C Martin R D Riley Gerta Rücker Susanne Steinhauser Martin Schumacher Joie Ensor Kym Snell Brian Willis Thomas Debray Jon Deeks Lavinia Ferrante di Ruffano Sian Taylor-Phillips Chris Hyde Stuart A Taylor Gauraang Batnagar Lavinia Ferrante Di Ruffano Farah Seedat Aileen Clarke Sarah Byron Frances Nixon Rebecca Albrow Thomas Walker Carla Deakin Zhivko Zhelev Harriet Hunt Yaling Yang Lucy Abel James Buchanan Thomas Fanshawe Bethany Shinkins Laure Wynants Jan Verbakel Sabine Van Huffel Dirk Timmerman Ben Van Calster Aeliko Zwinderman Jason Oke Jack O'Sullivan Rafael Perera Brian Nicholson Hannah L Bromley Tracy E Roberts Adele Francis Denniis Petrie G Bruce Mann Kinga Malottki Holly Smith Lucinda Billingham Alice Sitch Oke Gerke Mie Holm-Vilstrup Eivind Antonsen Segtnan Ulrich Halekoh Poul Flemming Høilund-Carlsen Bernard G Francq Jac Dinnes Julie Parkes Walter Gregory Jenny Hewison Doug Altman William Rosenberg Peter Selby Julien Asselineau Paul Perez Aïssatou Paye Emilie Bessede Cécile Proust-Lima Christiana Naaktgeboren Joris de Groot Anne Rutjes Johannes Reitsma Emmanuel Ogundimu Jonathan Cook Yannick Le Manach Yvonne Vergouwe Romin Pajouheshnia Rolf Groenwold Karen Moons Linda Peelen Daan Nieboer Bavo De Cock Micael J Pencina Ewout W Steyerberg Jennifer Cooper Nick Parsons Chris Stinton Steve Smith Andy Dickens Rachel Jordan Alexandra Enocson David Fitzmaurice Peymane Adab Charles Boachie Gaj Vidmar Karoline Freeman Martin Connock Rachel Court Carl Moons Jessica Harris Andrew Mumford Zoe Plummer Kurtis Lee Barnaby Reeves Chris Rogers Veerle Verheyden Gianni D Angelini Gavin J Murphy Jeremy Huddy Melody Ni Katherine Good Graham Cooke 1Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 2Cochrane Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands. 234Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 49Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 3Cochrane Dementia and Cognitive Improvement Group University of Oxford Oxford UK. 4EPPI-Centre Department of Social Science University College London London UK. 5Division of Health and Social Care Research King's College London London UK. 6College of Computer and Information Science Northeastern University Boston USA. 13University Hospitals Bristol NHS Foundation Trust School of Social and Community Medicine Bristol UK. 275Institute of Applied Health Research University of Birmingham Birmingham UK. 9Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 10School of Social and Community Medicine University of Bristol Bristol UK. 179Institute of Applied Health Research University of Birmingham Birmingham UK. 12Kleijnen Systematic Reviews Ltd York UK. 25Research Institute for Primary Care and Health Sciences Keele University Keele UK. 188Centre for Statistics in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK. 191Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands. 237Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands. 18Department of Medical Statistics University Medical Center Göttingen Göttingen Germany. 19Institute for B

[This corrects the article DOI: 10.1186/s41512-016-0001-y.].

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Temporal Evaluation of Risk Factors for Acute Myocardial Infarction Readmissions

Temporal Evaluation of Risk Factors for Acute Myocardial Inf...

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IEEE International Conference on Healthcare informatics (ICHI)

作者： Gregor Stiglic Adam Davey Zoran Obradovic Faculty of Health Sciences University of Maribor Maribor Slovenia Department of Public Health Temple University Philadelphia PA USA Center for Data Analytics and Biomedical Informatics Temple University Philadelphia PA USA

Risk-adjusted 30-day readmission rates for specific diagnoses including myocardial infarction are now used to index hospital reimbursement rates, making prediction of this outcome particularly salient. In order to consider how the importance of various predictors may be changing over time, we apply a modified prequential evaluation technique with an extended training set to this classification problem. Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression using cyclical coordinate descent was used for classification. This paper proposes a bootstrapping based approach to evaluation of sparse coefficients in large sparse datasets with binary and numerical features. It was evaluated on an eight-year dataset of hospital discharge records of myocardial infarction patients consisting of 312,309 discharge records. Results indicate diagnoses (clustered around related disease systems) and length of stay are the most important positive predictors, whereas procedures and diagnoses correcting for small groups of patients and total charges are more important among negative predictors. Temporal comparisons tend to suggest that the importance of features themselves is changing, rather than their prevalence.

关键词： Hospitals Discharges (electric) Diseases Medical diagnostic imaging Predictive models Training Logistics

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Distributed privacy preserving decision support system for predicting hospitalization risk in hospitals with insufficient data

Distributed privacy preserving decision support system for p...

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11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012

作者： Mathew, George Obradovic, Zoran Center for Data Analytics and Biomedical Informatics Temple University Philadelphia PA United States

ISBN: (纸本)9780769549132

Building prediction models for suggestive knowledge from multiple sources dynamically is of great interest from a clinical decision support point of view. This is valuable in situations where the local clinical data repository does not have sufficient number of records to draw conclusions from. However, due to privacy concerns, hospitals are reluctant to divulge patient records. Consequently, a distributed model building mechanism that can use just the statistics from multiple hospitals' databases is valuable. Our DIDT algorithm builds a model in that fashion. In this study, using National Inpatient Sample (NIS) data for 2009, we demonstrate that DIDT algorithm can be used to help collaboratively build a better decision-making model in situations where hospitals have small number of records that are insufficient to make good local models. Based on 262 attributes used for model building, we showed that 9 collaborating hospitals each with less than 100 cases of hospitalizations related to diabetes were able to achieve 9.9% improvement in accuracies of hospitalization prediction collectively using a distributed model as compared to relying on local models developed on their own. When relying on local risk prediction models for diabetes at these 9 hospitals, 159 of 357 patients were misclassified and prediction was impossible for another 16 patients. Our integrated model reduced the misclassification to 138 effectively providing accurate early diagnostics to 37 additional patients. We also introduce the concept of banding to improve DIDT algorithm so as to logically combine multiple hospitals when large number of hospitals is involved for reduction in cross-validation folds. © 2012 IEEE.

关键词： Hospitals

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Supervised clustering of label ranking data

Supervised clustering of label ranking data

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12th SIAM International Conference on data Mining, SDM 2012

作者： Grbovic, Mihajlo Djuric, Nemanja Vucetic, Slobodan Department of Computer and Information Sciences Center for Data Analytics and Biomedical Informatics Temple University United States

ISBN: (纸本)9781611972320

In this paper we study supervised clustering in the context of label ranking data. Segmentation of such complex data has many potential real-world applications. For example, in target marketing, the goal is to cluster customers in the feature space by taking into consideration the assigned, potentially incomplete product preferences, such that the preferences of instances within a cluster are more similar than the preferences of customers in the other clusters. We establish several heuristic baselines for this application that make use of well-known algorithms such as K-means, and propose a principled algorithm specifically tailored for this type of clustering. It is based on the Plackett- Luce (PL) probabilistic ranking model. Each cluster is represented as a union of Voronoi cells defined by a set of prototypes and is assigned a set of PL label scores that determine the cluster-specific label ranking. The unknown cluster PL parameters and prototype positions are determined using a supervised learning technique. Cluster membership and ranking for a new instance is determined by membership of its nearest prototype. The proposed algorithms were empirically evaluated on synthetic and reallife label ranking data. The PL-based method was superior to the heuristically-based supervised clustering approaches. The proposed PL-based algorithm was also evaluated on the task of label ranking prediction. The results showed that it is highly competitive to the state of the art label ranking algorithms, and that it is particularly accurate on data with partial rankings. Copyright © 2012 by the Society for Industrial and Applied Mathematics.

关键词： Supervised learning

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A method for design of data-tailored partitioning algorithms for optimizing the number of clusters in microarray analysis

A method for design of data-tailored partitioning algorithms...

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2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012

作者： Vukicevic, Milan Delibasic, Boris Jovanovic, Milos Suknovic, Milija Obradovic, Zoran Center for Business Decision-Making Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Center for Data Analytics and Biomedical Informatics CIST Temple University Philadelphia United States

ISBN: (纸本)9781467311892

We propose a method for designing a partitioning clustering algorithm from reusable components that is suitable for finding the appropriate number of clusters (K) in microarray data. The proposed method is evaluated on 10 datasets (4 syntetic and 6 real-word microarrays) by considering 1008 reusable-component-based algorithms and four normalization methods. The best performing algorithm were reported on every dataset and also rules were identified for designing microarray-specific clustering algorithms. The obtained results indicate that in the majority of cases a data-tailored clustering algorithm design outperforms the results reported in the literature. In addition, data normalization can have an important influence on algorithm performance. The method proposed in this paper gives insights for design of divisive clustering algorithms that can reveal the optimal K in a microarray dataset. © 2012 IEEE.

关键词： Clustering algorithms

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A data mining approach for optimization of acute inflammation therapy

A data mining approach for optimization of acute inflammatio...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Vladan Radosavljević Kosta Ristovski Zoran Obradović Center for Data Analytics and Biomedical Informatics Temple University Philadelphia USA

Acute inflammation is a medical condition which occurs over seconds, minutes or hours and is characterized as a systemic inflammatory response to an infection. Delaying treatment by only one hour decreases patient chance of survival by about 7%. Therefore, there is a critical need for tools that can aid therapy optimization for this potentially fatal condition. Towards this objective we developed a data driven approach for therapy optimization where a predictive model for patients' behavior is learned directly from historical data. As such, the predictive model is incorporated into a model predictive control optimization algorithm to find optimal therapy, which will lead the patient to a healthy state. To save on the cost of clinical trials and potential failure, we evaluated our model on a population of virtual patients capable of emulating the inflammatory response. Patients are treated with two drugs for which dosage and timing are critical for the outcome of the treatment. Our results show significant improvement in percentage of healthy outcomes comparing to previously proposed methods for acute inflammation treatment found in literature and in clinical practice. In particular, application of our method rescued 88% of patients that would otherwise die within 168 hours due to septic or aseptic state. In contrast, the best method from literature rescued only 73% of patients.

关键词： Predictive models Medical treatment Mathematical model Optimization Predictive control data models Pathogens

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Predicting viral infection by selecting informative biomarkers from temporal high-dimensional gene expression data

Predicting viral infection by selecting informative biomarke...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qiang Lou Zoran Obradovic Center for Data Analytics and Biomedical Informatics Temple University Philadelphia USA

In order to more accurately predict an individual's health status, in clinical applications it is often important to perform analysis of high-dimensional gene expression data that varies with time. A major challenge in predicting from such temporal microarray data is that the number of biomarkers used as features is typically much larger than the number of labeled subjects. One way to address this challenge is to perform feature selection as a preprocessing step and then apply a classification method on selected features. However, traditional feature selection methods cannot handle multivariate temporal data without applying techniques that flatten temporal data into a single matrix in advance. In this study, a feature selection filter that can directly select informative features from temporal gene expression data is proposed. In our approach we measure the distance between multivariate temporal data from two subjects. Based on this distance, we define the objective function of temporal margin based feature selection to maximize each subject's temporal margin in its own relevant subspace. The experimental results on two real flu data sets provide evidence that our method outperforms the alternatives, which flatten the temporal data in advance.

关键词： Gene expression Biomarkers Time measurement Time series analysis Optimization Predictive models Vectors

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Distributed Privacy Preserving Decision Support System for Predicting Hospitalization Risk in Hospitals with Insufficient data

Distributed Privacy Preserving Decision Support System for P...

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International Conference on Machine Learning and Applications (ICMLA)

作者： George Mathew Zoran Obradovic Center for Data Analytics and Biomedical Informatics Temple University Philadelphia PA

关键词： Hospitals Accuracy Distributed databases data models Buildings Predictive models Diabetes

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Using an atlas-based approach in the analysis of gene expression maps obtained by voxelation

Using an atlas-based approach in the analysis of gene expres...

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8th International Workshop on Artificial Intelligence Applications and Innovations, AIAI 2012: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB

作者： Zacharaki, Evangelia I. Skoura, Angeliki An, Li Smith, Desmond J. Megalooikonomou, Vasileios Department of Computer Engineering and Informatics University of Patras Patras Greece Data Engineering Laboratory Center for Data Analytics and Biomedical Informatics Temple University PA United States Department of Molecular and Medical Pharmacology David Geffen School of Medicine UCLA CA United States

ISBN: (纸本)9783642334115

The integration of gene expression datasets with gene function information provides valuable insights in unraveling the molecular mechanisms of the brain. In this paper, gene expression maps, acquired by the technique of voxelation, are analyzed using an atlas-based framework and the extracted spatial information is employed to organize genes in significant clusters. Moreover, gene function enrichment analysis of clusters enables exploring the relationships among brain regions, gene expressions and gene functions. Our work confirms the hypothesis that genes of similar spatial expression patterns display similar functions indicating that our methodology could assist in the functional identification of unannotated genes. © 2012 IFIP International Federation for Information Processing.

关键词： Brain mapping

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Integration of multiple annotators by aggregating experts and filtering novices

Integration of multiple annotators by aggregating experts an...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ping Zhang Zoran Obradovic Center for Data Analytics and Biomedical Informatics Temple University Philadelphia PA USA

Learning from noisy labels obtained from multiple annotators and without access to any true labels is an increasingly important problem in bioinformatics and biomedicine. In our method, this challenge is addressed by iteratively filtering low-quality annotators and estimating the consensus labels based only on the remaining experts that provide higher-quality annotations. Experiments on biomedical text classification and CASP9 protein disorder prediction tasks provide evidence that the proposed algorithm is more accurate than the majority voting and previously developed multi-annotator approaches. The benefit of using the new method is particularly large when low-quality annotators dominate. Moreover, the new algorithm also suggests the most relevant annotators for each instance, thus paving the way for understanding the behaviors of each annotator and building more reliable predictive models for bioinformatics applications.

关键词： Estimation Classification algorithms Filtering Accuracy Sensitivity Noise measurement Proteins

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