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A secure distributed logistic regression protocol for the detection of rare adverse drug events

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2013年第3期20卷 453-461页

作者： El Emam, Khaled Samet, Saeed Arbuckle, Luk Tamblyn, Robyn Earle, Craig Kantarcioglu, Murat CHEO Res Inst Ottawa ON K1H 8L1 Canada Univ Ottawa Dept Paediat Ottawa ON Canada Mem Univ Newfoundland Fac Med eHlth Res Unit St John NF A1C 5S7 Canada McGill Univ Dept Epidemiol Biostat & Occupat Hlth Montreal PQ Canada Inst Clin Evaluat Sci Toronto ON Canada Univ Texas Dallas Dallas TX 75230 USA

Background There is limited capacity to assess the comparative risks of medications after they enter the market. For rare adverse events, the pooling of data from multiple sources is necessary to have the power and sufficient population heterogeneity to detect differences in safety and effectiveness in genetic, ethnic and clinically defined subpopulations. However, combining datasets from different data custodians or jurisdictions to perform an analysis on the pooled data creates significant privacy concerns that would need to be addressed. Existing protocols for addressing these concerns can result in reduced analysis accuracy and can allow sensitive information to leak. Objective To develop a secure distributed multi-party computation protocol for logistic regression that provides strong privacy guarantees. methods We developed a secure distributed logistic regression protocol using a single analysis center with multiple sites providing data. A theoretical security analysis demonstrates that the protocol is robust to plausible collusion attacks and does not allow the parties to gain new information from the data that are exchanged among them. The computational performance and accuracy of the protocol were evaluated on simulated datasets. Results The computational performance scales linearly as the dataset sizes increase. The addition of sites results in an exponential growth in computation time. However, for up to five sites, the time is still short and would not affect practical applications. The model parameters are the same as the results on pooled raw data analyzed in SAS, demonstrating high model accuracy. Conclusion The proposed protocol and prototype system would allow the development of logistic regression models in a secure manner without requiring the sharing of personal health information. This can alleviate one of the key barriers to the establishment of large-scale post-marketing surveillance programs. We extended the secure protocol to account for co

关键词： Ethical study methods privacy enhancing the conduct of biological/clinical research and trials assuring information system security and personal privacy anonymization privacy and security health services medical informatics pharmacoepidemiology medical education advanced algorithms personal health records and self-care systems languages and computational methods

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A practical approach to achieve private medical record linkage in light of public resources

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2013年第2期20卷 285-292页

作者： Kuzu, Mehmet Kantarcioglu, Murat Durham, Elizabeth Ashley Toth, Csaba Malin, Bradley Univ Texas Dallas Dept Comp Sci Dallas TX 75230 USA Vanderbilt Univ Dept Biomed Informat Nashville TN 37203 USA Vanderbilt Univ Dept Elect Engn & Comp Sci Nashville TN 37203 USA

Objective Integration of patients' records across resources enhances analytics. To address privacy concerns, emerging strategies such as Bloom filter encodings (BFEs), enable integration while obscuring identifiers. However, recent investigations demonstrate BFEs are, in theory, vulnerable to cryptanalysis when encoded identifiers are randomly selected from a public resource. This study investigates the extent to which cryptanalysis conditions hold for (1) real patient records and (2) a countermeasure that obscures the frequencies of the identifying values in encoded datasets. Design First, to investigate the strength of cryptanalysis for real patient records, we build BFEs from identifiers in an electronic medical record system and apply cryptanalysis using identifiers in a publicly available voter registry. Second, to investigate the countermeasure under ideal cryptanalysis conditions, we compose BFEs from the identifiers that are randomly selected from a public voter registry. Measurement We utilize precision (ie, rate of correct re-identified encodings) and computation efficiency (ie, time to complete cryptanalysis) to assess the performance of cryptanalysis in BFEs before and after application of the countermeasure. Results Cryptanalysis can achieve high precision when the encoded identifiers are composed of a random sample of a public resource (ie, a voter registry). However, we also find that the attack is less efficient and may not be practical for more realistic scenarios. By contrast, the proposed countermeasure made cryptanalysis impractical in terms of precision and efficiency. Conclusions Performance of cryptanalysis against BFEs based on patient data is significantly lower than theoretical estimates. The proposed countermeasure makes BFEs resistant to known practical attacks.

关键词： languages and computational methods advanced algorithms personal health records and self-care systems privacy security record linkage ethical study methods statistical analysis of large datasets methods for integration of information from disparate sources distributed systems assuring information system security and personal privacy

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Automated extraction of ejection fraction for quality measurement using regular expressions in Unstructured Information Management Architecture (UIMA) for heart failure

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第5期19卷 859-866页

作者： Garvin, Jennifer H. DuVall, Scott L. South, Brett R. Bray, Bruce E. Bolton, Daniel Heavirland, Julia Pickard, Steve Heidenreich, Paul Shen, Shuying Weir, Charlene Samore, Matthew Goldstein, Mary K. SLC VA Healthcare Syst IDEAS Ctr Salt Lake City UT USA Univ Utah Sch Med Div Epidemiol Salt Lake City UT USA Univ Utah Sch Med Dept Biomed Informat Salt Lake City UT USA VA Palo Alto Hlth Care Syst Palo Alto CA USA Stanford Univ Ctr Primary Care & Outcomes Res PCOR Stanford CA 94305 USA SLC VA Healthcare Syst GRECC Salt Lake City UT USA

Objectives Left ventricular ejection fraction (EF) is a key component of heart failure quality measures used within the Department of Veteran Affairs (VA). Our goals were to build a natural language processing system to extract the EF from free-text echocardiogram reports to automate measurement reporting and to validate the accuracy of the system using a comparison reference standard developed through human review. This project was a Translational Use Case Project within the VA Consortium for Healthcare Informatics. Materials and methods We created a set of regular expressions and rules to capture the EF using a random sample of 765 echocardiograms from seven VA medical centers. The documents were randomly assigned to two sets: a set of 275 used for training and a second set of 490 used for testing and validation. To establish the reference standard, two independent reviewers annotated all documents in both sets;a third reviewer adjudicated disagreements. Results System test results for document-level classification of EF of <40% had a sensitivity (recall) of 98.41%, a specificity of 100%, a positive predictive value (precision) of 100%, and an F measure of 99.2%. System test results at the concept level had a sensitivity of 88.9% (95% CI 87.7% to 90.0%), a positive predictive value of 95% (95% CI 94.2% to 95.9%), and an F measure of 91.9% (95% CI 91.2% to 92.7%). Discussion An EF value of <40% can be accurately identified in VA echocardiogram reports. Conclusions An automated information extraction system can be used to accurately extract EF for quality measurement.

关键词： Natural language processing (NLP) heart failure left ventricular ejection fraction (EF) Improving healthcare workflow and process efficiency applied informatics Improving government and community policy relevant to informatics and health quality process modeling and hypothesis generation Informatics Enhancing the conduct of biological/clinical research and trials applications that link biomedical knowledge from diverse primary sources (includes automated indexing) visualization of data and knowledge uncertain reasoning and decision theory languages and computational methods statistical analysis of large datasets advanced algorithms discovery and text and data mining methods other methods of information extraction automated learning human-computer interaction and human-centered computing cognitive study (including experiments emphasizing verbal protocol analysis and usability) knowledge representations knowledge acquisition and knowledge management delivering health information and knowledge to the public processing and display analysis image representation controlled terminologies and vocabularies ontologies knowledge bases ejection fraction machine learning simulation of complex systems (at all levels: molecules to work groups to organizations) developing/using clinical decision support (other than diagnostic) and guideline systems detecting disease outbreaks and biological threats

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Complex-disease networks of trait-associated single-nucleotide polymorphisms (SNPs) unveiled by information theory

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第2期19卷 295-305页

作者： Li, Haiquan Lee, Younghee Chen, James L. Rebman, Ellen Li, Jianrong Lussier, Yves A. Univ Chicago Ctr Biomed Informat Dept Med Chicago IL 60637 USA Univ Chicago Med Genet Sect Dept Med Chicago IL 60637 USA Univ Illinois Dept Med Hematol Oncol Sect Chicago IL USA Univ Chicago Ctr Comprehens Canc Ludwig Ctr Metastasis Res Computat InstInst Translat Med Chicago IL 60637 USA Univ Chicago Inst Genom & Syst Biol Chicago IL 60637 USA

Objective Thousands of complex-disease single-nucleotide polymorphisms (SNPs) have been discovered in genome-wide association studies (GWAS). However, these intragenic SNPs have not been collectively mined to unveil the genetic architecture between complex clinical traits. The authors hypothesize that biological annotations of host genes of trait-associated SNPs may reveal the biomolecular modularity across complexdisease traits and offer insights for drug repositioning. methods Trait-to-polymorphism (SNPs) associations confirmed in GWAS were used. A novel method to quantify traitetrait similarity anchored in Gene Ontology annotations of human proteins and information theory was developed. The results were then validated with the shortest paths of physical protein interactions between biologically similar traits. Results A network was constructed consisting of 280 significant intertrait similarities among 177 disease traits, which covered 1438 well-validated disease-associated SNPs. Thirty-nine percent of intertrait connections were confirmed by curators, and the following additional studies demonstrated the validity of a proportion of the remainder. On a phenotypic trait level, higher Gene Ontology similarity between proteins correlated with smaller 'shortest distance' in protein interaction networks of complexly inherited diseases (Spearman p<2.23x10(-16)). Further, 'cancer traits' were similar to one another, as were 'metabolic syndrome traits' (Fisher's exact test p = 0.001 and 3.5x10(-7), respectively). Conclusion An imputed disease network by information-anchored functional similarity from GWAS trait-associated SNPs is reported. It is also demonstrated that small shortest paths of protein interactions correlate with complex-disease function. Taken together, these findings provide the framework for investigating drug targets with unbiased functional biomolecular networks rather than worn-out single-gene and subjective canonical pathway approaches.

关键词： Complex disease SNP gene ontology protein-interaction networks information theory translational bioinformatics complex disease ontology bioinformatcis genetics network prostate cancer protein networks pathway analysis network modeling knowledge representations uncertain reasoning and decision theory languages and computational methods

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Advantages of genomic complexity: bioinformatics opportunities in microRNA cancer signatures

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第2期19卷 156-160页

作者： Lussier, Yves A. Stadler, Walter M. Chen, James L. Univ Illinois Sch Med Dept Med Chicago IL USA Univ Illinois Sch Med Dept Bioengn Chicago IL USA Univ Illinois Inst Genom & Syst Biol Chicago IL USA Univ Illinois Computat Inst Chicago IL USA Univ Illinois Dept Med Sect Hematol Chicago IL USA Univ Illinois Dept Med Sect Oncol Chicago IL USA

MicroRNAs, small non-coding RNAs, may act as tumor suppressors or oncogenes, and each regulate their own transcription and that of hundreds of genes, often in a tissue-dependent manner. This creates a tightly interwoven network regulating and underlying oncogenesis and cancer biology. Although protein-coding gene signatures and single protein pathway markers have proliferated over the past decade, routine adoption of the former has been hampered by interpretability, reproducibility, and dimensionality, whereas the single moleculeephenotype reductionism of the latter is often overly simplistic to account for complex phenotypes. MicroRNA-derived biomarkers offer a powerful alternative;they have both the flexibility of gene expression signature classifiers and the desirable mechanistic transparency of single protein biomarkers. Furthermore, several advances have recently demonstrated the robust detection of microRNAs from various biofluids, thus providing an additional opportunity for obtaining bioinformatically derived biomarkers to accelerate the identification of individual patients for personalized therapy.

关键词： MicroRNA signatures gene expression biomarkers bioinformatics knowledge representations uncertain reasoning and decision theory languages and computational methods prostate cancer protein networks pathway analysis network modeling machine learning predictive modeling statistical learning privacy technology

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Protein-network modeling of prostate cancer gene signatures reveals essential pathways in disease recurrence

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第4期18卷 392-402页

作者： Chen, James L. Li, Jianrong Stadler, Walter M. Lussier, Yves A. Univ Chicago Dept Med Ctr Biomed Informat Chicago IL 60637 USA Univ Chicago Inst Genom & Syst Biol Chicago IL 60637 USA Univ Chicago Computat Inst Chicago IL 60637 USA Univ Chicago Dept Med Hematol Oncol Sect Chicago IL 60637 USA

Objective Uncovering the dominant molecular deregulation among the multitude of pathways implicated in aggressive prostate cancer is essential to intelligently developing targeted therapies. Paradoxically, published prostate cancer gene expression signatures of poor prognosis share little overlap and thus do not reveal shared mechanisms. The authors hypothesize that, by analyzing gene signatures with quantitative models of protein-protein interactions, key pathways will be elucidated and shown to be shared. Design The authors statistically prioritized common interactors between established cancer genes and genes from each prostate cancer signature of poor prognosis independently via a previously validated single protein analysis of network (SPAN) methodology. Additionally, they computationally identified pathways among the aggregated interactors across signatures and validated them using a similarity metric and patient survival. Measurement Using an information-theoretic metric, the authors assessed the mechanistic similarity of the interactor signature. Its prognostic ability was assessed in an independent cohort of 198 patients with high-Gleason prostate cancer using Kaplan Meier analysis. Results Of the 13 prostate cancer signatures that were evaluated, eight interacted significantly with established cancer genes (false discovery rate <5%) and generated a 42-gene interactor signature that showed the highest mechanistic similarity (p<0.0001). Via parameter-free unsupervised classification, the interactor signature dichotomized the independent prostate cancer cohort with a significant survival difference (p=0.009). Interpretation of the network not only recapitulated phosphatidylinositol-3 kinase/NF-kappa B signaling, but also highlighted less well established relevant pathways such as the Janus kinase 2 cascade. Conclusions SPAN methodolgy provides a robust means of abstracting disparate prostate cancer gene expression signatures into clinically useful, prioritize

关键词： Prostate cancer protein networks systems biology information theory network modeling Simulation of complex systems (at all levels: molecules to work groups to organizations) knowledge representations Uncertain reasoning and decision theory languages and computational methods statistical analysis of large datasets advanced algorithms discovery and text and data mining methods Natural-language processing Automated learning Ontologies

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Exploiting time in electronic health record correlations

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第Sup1期18卷 I109-I115页

作者： Hripcsak, George Albers, David J. Perotte, Adler Columbia Univ Med Ctr Dept Biomed Informat New York NY 10032 USA

Objective To demonstrate that a large, heterogeneous clinical database can reveal fine temporal patterns in clinical associations;to illustrate several types of associations;and to ascertain the value of exploiting time. Materials and methods Lagged linear correlation was calculated between seven clinical laboratory values and 30 clinical concepts extracted from resident signout notes from a 22-year, 3-million-patient database of electronic health records. Time points were interpolated, and patients were normalized to reduce inter-patient effects. Results The method revealed several types of associations with detailed temporal patterns. Definitional associations included low blood potassium preceding 'hypokalemia.' Low potassium preceding the drug spironolactone with high potassium following spironolactone exemplified intentional and physiologic associations, respectively. Counterintuitive results such as the fact that diseases appeared to follow their effects may be due to the workflow of healthcare, in which clinical findings precede the clinician's diagnosis of a disease even though the disease actually preceded the findings. Fully exploiting time by interpolating time points produced less noisy results. Discussion Electronic health records are not direct reflections of the patient state, but rather reflections of the healthcare process and the recording process. With proper techniques and understanding, and with proper incorporation of time, interpretable associations can be derived from a large clinical database. Conclusion A large, heterogeneous clinical database can reveal clinical associations, time is an important feature, and care must be taken to interpret the results.

关键词： Electronic health records data mining time series associations modeling physiologic and disease processes linking the genotype and phenotype languages and computational methods statistical analysis of large datasets advanced algorithms high-performance and large-scale computing detecting disease outbreaks and biological threats simulation of complex systems (at all levels: molecules to work groups to organizations)

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