Evidence map›Paper›PMID 31259031›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019

Developing a Data-driven Medication Indication Knowledge Base using a Large Scale Medical Claims Database.

Ying Li, Cao Xiao

Abstract read
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2019. 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

2 authors.

Ying LiIBM T. J. Watson Research Center, Yorktown Heights, NY, USA.
Cao XiaoAI for Healthcare, IBM Research, Cambridge, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medication-indication knowledge base (KB) is useful for clinical care and also a key enabler for secondary use of observational health data. Over the years there are several indication KBs being developed, however, they were built based on curated data sources and thus may not reflect actual clinical practice. The longitudinal observational health data contain information about real world practice of medication indication, but were rarely used in KB construc- tion. A major challenge of leveraging them is the confounders in multi-medication multi-diagnoses relations. In this study, we proposed a sampling based approach that could explicitly handle the aforementioned confounders, and consequently detect more accurate medication-indication relations. Based on this method, we created a medication- indication KB that reflects actual clinical practice and has broad medication and indication coverages. Our work represents the first attempt to develop a medication-indication KB from a large scale observational health data in an automated and unsupervised manner.

Identifiers

PMID31259031
PMCPMC6568115

What OpenQuestion holds

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Registered trials

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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.