*A file containing all Min/Max Baseline Reports for 2005-2023 in their original format is available in the Attachments section below. A second file includes a separate set of reports, made available from 2002-2017, that did not include OLDMEDLINE records.*

MEDLINE/PubMed annual statistical reports are based upon the data elements in the baseline versions of MEDLINE®/PubMed are available. For each year covered the reports include: total citations containing each element; total occurrences of each element; minimum/average/maximum occurrences of each element in a record; minimum/average/maximum length of a single element occurrence; average record size; and other statistical data describing the content and size of the elements.

*A file containing all Misc Baseline Reports for 2018-2023 in their original format is available in the Attachments section below.*

MEDLINE/PubMed annual statistical reports are based upon the data elements in the baseline versions of MEDLINE®/PubMed are available. For each year covered the reports include: total citations containing each element; total occurrences of each element; minimum/average/maximum occurrences of each element in a record; minimum/average/maximum length of a single element occurrence; average record size; and other statistical data describing the content and size of the elements.

***The Computational Photography Project for Pill Identification (C3PI) was discontinued in 2018. No new images will be added to the collection. Identifiers for pills will not be updated.***

The Computational Photography Project for Pill Identification (C3PI) created the RxIMAGE database of freely available high-quality digital images of prescription pills and associated data for use in conducting computer vision research in text- and image-based search and retrieval. Photographs of pills for the RxIMAGE database were taken under laboratory lighting conditions, from a camera directly above the front and the back faces of the pill, at high resolution, and using specialized digital macro-photography techniques. Image segmentation algorithms were then applied to create the JPEG images in the database.

Historical information about the project is available in the NLM archive at https://wayback.archive-it.org/7867/20190423182937/https:/lhncbc.nlm.nih.gov/project/c3pi-computational-photography-project-pill-identification.

The National Health and Nutrition Examination Surveys (NHANES), conducted by the National Center for Health Statistics, Centers for Disease Control (NCHS/CDC), were designed to assess the health and nutritional status of adults and children in the United States through interviews and direct physical examinations. The NHANES radiographs were scanned by Dr. Bernie Huang at the University of California at Los Angeles and the University of California at San Francisco. Dr. Huang’s group used a Lumysis 100 with a 175 micron spot to scan the first 6000 radiographs. The remaining radiographs were scanned on the Lumysis 150 again with a 175 micron spot size. NOTE: This dataset is no-longer updated with new content.

The Interaction API is a web service for accessing drug-drug interactions. No license is needed to use the Interaction API. Currently, the API uses DrugBank for its interaction information.

I-MAGIC is an interactive tool to demonstrate how the SNOMED CT to ICD-10-CM map (https://www.nlm.nih.gov/research/umls/mapping_projects/snomedct_to_icd10cm.html) can be used to generate ICD-10-CM codes from clinical problems coded in SNOMED CT. It simulates a problem list entry interface in which the user searches for problem terms which are mapped to SNOMED CT codes in the back end. The SNOMED CT codes are then used to derive ICD-10-CM codes using the Map.

MAGPIE is an interactive tool to help users (e.g., professional coders, researchers, clinicians) find SNOMED CT and ICD-10-PCS codes for medical procedures and interventions. MAGPIE is an NLM research tool (it has not been tested in a production environment), and is developed primarily for the 2018 version of ICD-10-PCS. MAGPIE combines lexical and map-assisted searching strategies to look for SNOMED CT, ICD-9-CM, and ICD-10-PCS codes sequentially.

The MEDLINE/PubMed Baseline Repository (MBR) provides access to each MEDLINE/PubMed Baseline snapshot starting with the 2002 MEDLINE Baseline. Each baseline contains a snapshot of MEDLINE citations in the state they were at a given moment in time without the MeSH vocabulary updates and other revisions that occur during the year. The baseline snapshot is created at the beginning of each new MeSH Indexing Year. The records included in the MEDLINE/PubMed Baseline databases represent a static view of the data at the time each baseline database was created.

MedPix is a database of patient cases integrating images and textual information. The content material is organized by disease location (organ system), pathology category, patient profiles, and by image classification and caption. Additional information at https://medpix.nlm.nih.gov/home

MetaMap is a highly configurable application developed by the Lister Hill National Center for Biomedical Communications at the National Library of Medicine (NLM) to map biomedical text to the UMLS Metathesaurus or, equivalently, to identify Metathesaurus concepts referred to in English text. MetaMap employs a knowledge-intensive approach, natural-language processing (NLP), and computational-linguistic techniques, and is used worldwide in industry and academia. At NLM, MetaMap is one of the foundations of NLM's Medical Text Indexer (MTI), which is applied to both semiautomatic and fully automatic indexing of biomedical literature. Technical documentation at http://metamap.nlm.nih.gov/#Downloads