Evidence map›Paper›PMID 27570659›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2016

The Drug Data to Knowledge Pipeline: Large-Scale Claims Data Classification for Pharmacologic Insight.

Mark L Homer, Nathan P Palmer, Olivier Bodenreider, Aurel Cami, Laura Chadwick, Kenneth D Mandl

Abstract read
PubMed Full text at PMC
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Mark L HomerComputational Health Informatics Program, Boston Children's Hospital, Boston, MA;; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA;
Nathan P PalmerComputational Health Informatics Program, Boston Children's Hospital, Boston, MA;; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA;
Olivier BodenreiderNational Library of Medicine, National Institutes of Health, Bethesda, MD, USA;
Aurel CamiComputational Health Informatics Program, Boston Children's Hospital, Boston, MA;; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA;
Laura ChadwickComputational Health Informatics Program, Boston Children's Hospital, Boston, MA;; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA;; MCPHS University, Boston, MA, USA.
Kenneth D MandlComputational Health Informatics Program, Boston Children's Hospital, Boston, MA;; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA;

Funding

National Biomedical Information ServicesZIHLM200888 · NLM · NATIONAL LIBRARY OF MEDICINE · PI PALTOO, DINA · 2009 to 2025
$5200.7M
RESEARCH TRAINING-MEDICAL INFORMATICS 90T15LM007092 · NLM · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI Nils Gehlenborg · 1992 to 2026
$32.8M
Drug terminology at NLM - Integration, dissemination, quality assurance and applicationsZIALM010007 · NLM · NATIONAL LIBRARY OF MEDICINE · PI BODENREIDER, OLIVIER · 2016 to 2020
$3.1M
Instrumenting i2b2 for Improved Medication Research: Adding the Patient VoiceR01GM104303 · NIGMS · BOSTON CHILDREN'S HOSPITAL · PI MANDL, KENNETH D. · 2013 to 2016
$1.6M
NIGMS NIH HHS R01 GM104303NLM NIH HHS T15 LM007092
6 · The paper itself

Abstract

In biomedical informatics, assigning drug codes to categories is a common step in the analysis pipeline. Unfortunately, incomplete mappings are the norm rather than the exception with coverage values less than 85% not uncommon. Here, we perform this linking task on a nationwide insurance claims database with over 13 million members who were dispensed, according to National Drug Codes (NDCs), over 50,000 unique product forms of medication. The chosen approach employs Cerner Multum's VantageRx and the U.S. National Library of Medicine's RxMix. As a result, 94.0% of the NDCs were successfully mapped to categories used by common drug terminologies, e.g., Anatomical Therapeutic Chemical (ATC). Implemented as an SQL database and scripts, the approach is generic and can be setup for a new data set in a few hours. Thus, the method is a viable option for large-scale drug classification.

Identifiers

PMID27570659
PMCPMC5001754

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.