Advancing healthcare and biomedical research via new data-driven approaches

Lucila Ohno-Machado
<span title="2017-04-10">2017</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="" style="color: black;">JAMIA Journal of the American Medical Informatics Association</a> </i> &nbsp;
Editor-in-Chief Every issue of JAMIA presents articles related to biomedical data science: from algorithms that discover and validate data-driven patterns, to big data indexing strategies, to predictive models that use novel approaches to analyze new or reused data. In this issue of JAMIA, several articles relied on mining of large data sets: Chen (p. 472) focuses on electronic health records for prediction of clinical order patterns, while Ghassemi (p. 488) uses a time series for prediction of
more &raquo; ... vasopressor effects. Ritchie (p. 577) uses a new approach that utilizes genomic interactions for prediction of clinical outcomes in ovarian cancer. When mining individual level data, it is critical that the privacy of individuals be protected. O'Keefe (p. 544) proposes an online data center approach to prevent reidentification, while Dernoncourt (p. 596) uses recurrent neural networks to de-identify patient notes. Data analysis related to medications is also a popular topic in JAMIA. Patel (p. 614) uses the biomedical literature to suggest drug repositioning based on drug-drug interactions, Noor (p. 556) proposes a drug-drug interaction discovery approach using semantic web technologies, and Shah (p. 565) suggests potentially synergistic drug combinations using data from electronic health records (EHRs) and gene expression measurements. Also related to utilization of EHRs for research, Elemento (p. 513) reports on a cancer precision medicine knowledge-base for interpretation of clinicalgrade mutations, and Garcelon (p. 607) describes an approach to improve full text searches on family history. Banerjee (p. 550) presents a heart failure dashboard designed to reduce readmissions, and Delon (p. 588) uses data from a national health insurance information system to implement epidemiological surveillance of malaria. Clinical decision support systems (CDSS) for emergency care are reviewed by Bennett (p. 655), and the cost-benefit of
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1093/jamia/ocx036</a> <a target="_blank" rel="external noopener" href="">pmid:28403381</a> <a target="_blank" rel="external noopener" href="">fatcat:72odbn4udnfx5fxrpsit72bify</a> </span>
<a target="_blank" rel="noopener" href="" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href=""> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> </button> </a>