Evidence map›Paper›PMID 36636516›Full record

ArticleProceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management2022

MedCV: An Interactive Visualization System for Patient Cohort Identification from Medical Claim Data.

Ashis Kumar Chanda, Tian Bai, Brian L Egleston, Slobodan Vucetic

Abstract read
In one paragraph

Article in Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Ashis Kumar ChandaTemple University, PA, USA.
Tian BaiTemple University, PA, USA.
Brian L EglestonFox Chase Cancer Center, PA, USA.
Slobodan VuceticTemple University, PA, USA.

Funding

WORD PROCESSING CENTER--COREP30CA006927 · NCI · RESEARCH INST OF FOX CHASE CAN CTR · PI Dietmar J Kappes · 1985 to 2026
$138.8M
Deep learning for representation of codes used for SEER-Medicare claims researchR21CA202130 · NCI · RESEARCH INST OF FOX CHASE CAN CTR · PI EGLESTON, BRIAN L, VUCETIC, SLOBODAN · 2016 to 2017
$398k
NCI NIH HHS P30 CA006927NCI NIH HHS R21 CA202130
6 · The paper itself

Abstract

Healthcare providers generate a medical claim after every patient visit. A medical claim consists of a list of medical codes describing the diagnosis and any treatment provided during the visit. Medical claims have been popular in medical research as a data source for retrospective cohort studies. This paper introduces a medical claim visualization system (MedCV) that supports cohort selection from medical claim data. MedCV was developed as part of a design study in collaboration with clinical researchers and statisticians. It helps a researcher to define inclusion rules for cohort selection by revealing relationships between medical codes and visualizing medical claims and patient timelines. Evaluation of our system through a user study indicates that MedCV enables domain experts to define high-quality inclusion rules in a time-efficient manner.

Indexed as

cohort identificationdeep learningelectronic health recordsembeddingMedical claimsretrospective studyvisual analytics

Identifiers

PMID36636516
PMCPMC9830554

What OpenQuestion holds

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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.