Group Members
Dina Demner-Fushman,
MD, PhD
Investigator
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(link is external) (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 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(link is external), 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."
Publication Highlights
Confirm or Refute?: A Comparative Study on Citation Sentiment Classification in Clinical Research Publications
Kilicoglu H, Peng Z, Tafreshi S, Tran T, Rosemblat G, Schneider J - PMID: 30753947
Quantifying scientific impact of researchers and journals relies largely on citation counts, despite the acknowledged limitations of this approach.
A systematic approach for developing a corpus of patient reported adverse drug events: A case study for SSRI and SNRI medications.
Zolnoori M, Fung K, Patrick TB, Fontelo P, Kharrazi H, Faiola A, Wu YSS, Eldredge CE, Luo J, Conway M, Zhu J, Park SK, Xu K, Moayyed H, Goudarzvand S - PMID: 30611893
"Psychiatric Treatment Adverse Reactions" (PsyTAR) corpus is an annotated corpus that has been developed using patients narrative data for psychiatric medications, particularly SSRIs (Selective Serotonin Reuptake Inhibitor) and SNRIs (Serotonin Norepinephrine Reuptake Inhibitor) medications.
Expanding vocabularies for complementary and alternative medicine therapies.
Scarton LA, Wang L, Kilicoglu H, Jahries M, Del Fiol M - PMID: 30545491
There is a significant consumer demand for complementary and alternative medicine (CAM) therapies as possible alternatives to drugs in the treatment and prevention of chronic diseases