Aligned-Layer Text Search in Clinical Notes.

Stephen Wu, Andrew Wen, Yanshan Wang, Sijia Liu, Hongfang Liu

Abstract read

In one paragraph

Article in Studies in health technology and informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Cohort Selection for Clinical Trials From Longitudinal Patient Records: Text Mining Approach.JMIR medical informatics · 2019
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

5 authors.

Stephen WuDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA.

Andrew WenDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA.

Yanshan WangDivision of Biomedical Informatics, Mayo Clinic, Rochester, MN, USA.

Sijia LiuDivision of Biomedical Informatics, Mayo Clinic, Rochester, MN, USA.

Hongfang LiuDivision of Biomedical Informatics, Mayo Clinic, Rochester, MN, USA.

Funding

Semi-structured Information Retrieval in Clinical Text for Cohort IdentificationR01LM011934 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI HERSH, WILLIAM R, LIU, HONGFANG · 2014 to 2025

$5.1M

NLM NIH HHS R01 LM011934

6 · The paper itself

Abstract

Search techniques in clinical text need to make fine-grained semantic distinctions, since medical terms may be negated, about someone other than the patient, or at some time other than the present. While natural language processing (NLP) approaches address these fine-grained distinctions, a task like patient cohort identification from electronic health records (EHRs) simultaneously requires a much more coarse-grained combination of evidence from the text and structured data of each patient's health records. We thus introduce aligned-layer language models, a novel approach to information retrieval (IR) that incorporates the output of other NLP systems. We show that this framework is able to represent standard IR queries, formulate previously impossible multi-layered queries, and customize the desired degree of linguistic granularity.

Indexed as

Information Storage and RetrievalNatural Language ProcessingSemanticsElectronic Health RecordsHumansLinguisticsElectronic Health RecordsInformation Storage and RetrievalNatural Language Processing

Identifiers

PMID29295172

PMCPMC7466869

What OpenQuestion holds

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LicenceCC BY

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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.