Provided by the Center for Medicare and Medicaid Services (CMS), the VRDC now carries 17 years of Parts A and B claims data including diagnoses, procedures and medications dispensed in offices (mostly injectable), and vital status derived from Social Security death records. Since late 2006, it also contains Part D medication prescription claims (dispensed by community pharmacies). Very recently the cause of death (captured by CDC), has become available (1999-2016).

The consumer health question answering project was launched to support NLM customer services that receive about 90,000 requests a year from a world-wide pool of customers. The requests are categorized by the customer support services staff and are either answered using about 300 stock answers (with or without modifications) or researched and answered by the staff manually. Responding to a customer with a stock reply takes approximately 4 minutes; answering with a personalized stock reply takes about 10 minutes. To reduce the time and cost of customer services, NLM launched the Consumer Health Information and Question Answering (CHIQA) project. The CHIQA project conducts research in both the automatic classification of customers’ requests and the automatic answering of consumer health questions.

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.

The goal of our work in Biomedical Imaging is two-fold: One, to develop advanced imaging tools for biomedical research in partnership with the National Cancer Institute and other organizations. Secondly, to conduct research in Content Based Image Retrieval (CBIR) to index and retrieve medical images by image features (e.g., shape, color and texture), augmented by textual features as well. This work includes the development of the CervigramFinder for retrieval of uterine cervix images by image features, SPIRS for retrieval of digitized x-ray images of the spine from NHANES II and a distributed global system SPIRS-IRMA for image retrieval by both high-level and detailed features of medical images, in collaboration with Aachen University, Germany.

This project seeks to improve information retrieval from collections of full-text biomedical articles, images, and patient cases, by moving beyond conventional text-based searching to combining both text and visual features.

The current version of Open-i is at https://openi.nlm.nih.gov/.

The Open-i® (pronounced “open eye”) experimental multimedia search engine retrieves and displays structured MEDLINE citations augmented by image-related text and concepts and linked to images based on image features.

The Top LOINC Codes can be downloaded from https://loinc.org/usage/obs/ 

LHNCBC, in cooperation with Regenstrief Institute, obtained and analyzed statistical data from many health care organizations to identify the most frequent subset that organizations could target for mapping. It obtained frequency distribution for three years of laboratory tests sources, including from Partners of Boston and the Indiana Network for Patient Care (an HIE), and United Healthcare, all of whom had mapped the test results to LOINC.