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Discoveries from Clinical Data

Large database collections of clinical data -- from longitudinal research projects, electronic medical records, and health information exchanges -- provide opportunities to examine controversial findings from smaller scale clinical studies and to conduct retrospective epidemiological studies in areas that lack clinical trials.

NLM established a goal to integrate biomedical, clinical, and public health information systems that promote scientific discovery and speed the translation of research into practice (NLM Long Range Plan, 2006-2016, Goal 3).  One of NLM's key recommendations to fulfill this goal is to "develop linked databases for discovering relationships between clinical data, genetic information, and environmental factors."

LHNCBC's biostatistician and clinicians are using MIT’s large longitudinal MIMIC-II database (33,000 patients with 40,000 intensive care unit (ICU) visits and 180 million rows of data) to answer clinical research questions. We also contributed standard clinical vocabulary code mappings to the latest MIMIC-II release (v 2.6).

We have completed a study on the impact of obesity on outcomes after critical illness, which was published in the journal Critical Care.

Ongoing studies include: 1) the relationship between vitamin B12 levels and mortality; and 2) the relationship between blood transfusions, feeds, and necrotizing enterocolitis (NEC) in newborns.

We developed and implemented Natural Language Processing algorithms to extract patients’ smoking status and discharge destinations from the MIMIC-II physician discharge summaries. We extracted information on episodes of neonatal apnea and bradycardia as well as maternal history from clinical notes for infants in the neonatal intensive care unit (NICU) for the NEC study. We also extracted data about hypertension and hypertensive medications from free-text notes, and used that data to compare to ICD-9 hypertension diagnosis codes in order to evaluate underreporting of certain common conditions after ICU admission.

To assist with integrating and analyzing the data, LHNCBC's researchers are using NLM-supported clinical vocabulary standards to improve the utility of the MIMIC-II database. We mapped the laboratory tests and medications to LOINC and RxNorm, respectively, and its radiology reports to the LOINC codes that describe the radiology study.

We are also developing the Maximum Likelihood (ML) statistical method -- to address measurement error in NLP-derived variables in order to reduce bias -- which could potentially increase the utility of NLP-derived data.

This LHNCBC research aligns closely with NIH's Big Data to Knowledge (BD2K) initiative, which "seeks to facilitate broad use of biomedical big data through new data sharing policies, catalogs of datasets, and enhanced training for early career scientists entering the new world of big data" by supporting "the management, analysis and integration of large-scale data and informatics."

Publications/Tools: 
Gartrell K, Storr CL, Trinkoff AM, Wilson ML, Gurses AP. Electronic personal health record use among registered nurses. Nurs Outlook. 2015 May-Jun;63(3):278-87. doi: 10.1016/j.outlook.2014.11.013. Epub 2014 Nov 28.
Amini S, Kilicoglu H, Hooft L, ter Riet G. Are we expressing uncertainty of our claims enough? Towards automatic detection of overstatement of claims. 2015. World Conference on Research Integrity (WCRI).
Kury F, Cimino JJ. Identifying Repetitive Institutional Review Board Stipulations by Natural Language Processing and Network Analysis. Stud Health Technol Inform. 2015;216:579-83.
Hripcsak G, Duke JD, Shah NH, Reich CG, Huser V, Schuemie MJ, Suchard MA, Park RW, Wong IC, Rijnbeek PR, van der Lei J, Pratt N, Norén GN, Li YC, Stang PE, Madigan D, Ryan PB. Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers. Stud Health Technol Inform. 2015;216:574-8.
Kilicoglu H, Rogers W. A Hybrid System for Extracting Chemical-Disease Relationships from Scientific Literature. 2015. BioCreative 5 Proceedings.
Cimino JJ, Remennick L. Adapting a Clinical Data Repository to ICD-10-CM through the use of a Terminology Repository. AMIA Annu Symp Proc. 2014 Nov 14;2014:405-13. eCollection 2014.
Pan X, Cimino JJ. Locating relevant patient information in electronic health record data using representations of clinical concepts and database structures. AMIA Annu Symp Proc. 2014 Nov 14;2014:969-75. eCollection 2014.
Abhyankar S, Demner-Fushman D, Callaghan FM, McDonald CJ. Combining structured and unstructured data to identify a cohort of ICU patients who received dialysis. J Am Med Inform Assoc. 2014 Sep-Oct;21(5):801-7. doi: 10.1136/amiajnl-2013-001915. Epub 2014 Jan 2.
Boyce RD, Ryan PB, Noren GN, Schuemie MJ, Reich C, Duke J, Tatonetti NP, Trifiro G, Harpaz R, Overhage JM, Hartzema AG, Kayter M, Voss EA, Lambert CG, Huser V, Dumontier M. Bridging islands of information to establish an integrated knowledge base of drugs and health outcomes of interest. Drug Saf. 2014 Aug;37(8):557-67. doi: 10.1007/s40264-014-0189-0.
Caudle KE, Klein TE, Hoffman JM, Muller DJ, Whirl-Carrillo M, Gong L, McDonagh EM, Sangkuhl K, Thorn CF, Schwab M, Agundez JA, Freimuth RR, Huser V, Lee MT, Iwuchukwu OF, Crews KR, Scott SA,, Wadelius M, Swen JJ, Tyndale RF, Stein CM, Roden D, Relling MV, Williams MS, Johnson SG. Incorporation of pharmacogenomics into routine clinical practice: the Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline development process. Curr Drug Metab. 2014 Feb;15(2):209-17.

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