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BackgroundMany thousands of patients with a suspected Mendelian disease have their exomes/genomes sequenced every year, but only about 30% receive a definitive diagnosis. Since a novel Mendelian gene-disease association is published on average every business day, thousands of undiagnosed patient cases could receive a diagnosis each year if their genomes were regularly compared to the latest literature. With millions of genomes expected to be sequenced for rare disease analysis by 2025, and considering the current publication rate of 1.1 million new articles per annum in PubMed, manually reanalyzing the growing cases of undiagnosed patients is not sustainable.MethodsWe describe a fully automated reanalysis framework for patients with suspected, but undiagnosed, Mendelian disorders. The presented framework was tested by automatically parsing all ∼100,000 newly published peer reviewed papers every month and matching them on genotype and phenotype with all stored undiagnosed patients. If a new article contains a possible diagnosis for an undiagnosed patient, the system provides notification. We test the accuracy of the automatic reanalysis system on 110 patients, including 61 with available trio data.ResultsEven when trained only on older data, our system identifies 80% of reanalysis diagnoses, while sending only 0.5-1 alerts per patient per year, a 100-1,000-fold efficiency gain over manual literature surveillance of equivalent yield.ConclusionWe show that automatic reanalysis of patients with suspected Mendelian disease is feasible and has the potential to greatly streamline diagnosis. Our system is not intended to replace clinical judgment. Rather, clinical diagnostic services could greatly benefit from a modest re-allocation of time from manual literature exploration to review of automated reanalysis alerts. Our system additionally supports a new paradigm for medical IT systems: proactive, continuously learning and consequently able to autonomously identify valuable insights as they emerge in digital health records. We have launched automated patient reanalysis, trained on the latest data, with user accounts and daily literature updates at https://AMELIE.stanford.edu.

This paper studies the grant peer review process employed by the Turkish regional development agencies, which is adapted from a review procedure of the Education, Audiovisual and Culture Executive Agency of the European Union. To model this process, we consider a Bayesian strategic-form game played by three reviewers who observe both a common and a private score signal about an evaluated project and assign their scores to minimize the sum of their disutilities from the false acceptance and false rejection of the project. We numerically compute the Bayesian Nash equilibria of this game and conduct several comparative statics exercises, after calibrating the model parameters accordingly. We also introduce two simpler review processes and compare their performances to that of the calibrated process in terms of outcome statistics, involving pass and fail rates of the evaluated projects, and manipulation statistics, involving the reviewers' manipulation rate and size of scores. (C) 2019 Elsevier Ltd. All rights reserved.

This paper investigates the impact of referee reliability on the quality and efficiency of peer review. We modeled peer review as a process based on knowledge asymmetries and subject to evaluation bias. We tested various levels of referee reliability and different mechanisms of reviewing effort distribution among agents. We also tested different scientific community structures (cohesive vs. parochial) and competitive science environments (high vs. low competition). We found that referee behavior drastically affects peer review and an equal distribution of the reviewing effort is beneficial only if the scientific community is homogeneous and referee reliability is the rule. We also found that the Matthew effect in the allocation of resources and credit is inherent to a ‘winner takes all’ well functioning science system, more than a consequence of evaluation bias.