Evidence map›Paper›PMID 36673096›Full record

ReviewDiagnostics (Basel, Switzerland)2023

Natural Language Processing Applications for Computer-Aided Diagnosis in Oncology.

Chengtai Li, Yiming Zhang, Ying Weng, Boding Wang, Zhenzhu Li

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
10.4field-weighted citation impact, top 1% of its field
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

20 citing papers in PubMed, 1 synthesis or guideline pooled it, 60 citations in OpenAlex.

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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 at 2 institutions in 1 country.

Chengtai LiSchool of Computer Science, Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China.
Yiming ZhangSchool of Computer Science, Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China.ORCID 0000-0002-9570-8962
Ying WengSchool of Computer Science, Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China.
Boding WangHwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo 315010, China.
Zhenzhu LiHwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo 315010, China.
University of Nottingham Ningbo China · CNUniversity of Chinese Academy of Sciences · CN

Funding

Ningbo Major Science & Technology Project 2022Z126University of Nottingham Ningbo China Project STRKE202205009
6 · The paper itself

Abstract

In the era of big data, text-based medical data, such as electronic health records (EHR) and electronic medical records (EMR), are growing rapidly. EHR and EMR are collected from patients to record their basic information, lab tests, vital signs, clinical notes, and reports. EHR and EMR contain the helpful information to assist oncologists in computer-aided diagnosis and decision making. However, it is time consuming for doctors to extract the valuable information they need and analyze the information from the EHR and EMR data. Recently, more and more research works have applied natural language processing (NLP) techniques, i.e., rule-based, machine learning-based, and deep learning-based techniques, on the EHR and EMR data for computer-aided diagnosis in oncology. The objective of this review is to narratively review the recent progress in the area of NLP applications for computer-aided diagnosis in oncology. Moreover, we intend to reduce the research gap between artificial intelligence (AI) experts and clinical specialists to design better NLP applications. We originally identified 295 articles from the three electronic databases: PubMed, Google Scholar, and ACL Anthology; then, we removed the duplicated papers and manually screened the irrelevant papers based on the content of the abstract; finally, we included a total of 23 articles after the screening process of the literature review. Furthermore, we provided an in-depth analysis and categorized these studies into seven cancer types: breast cancer, lung cancer, liver cancer, prostate cancer, pancreatic cancer, colorectal cancer, and brain tumors. Additionally, we identified the current limitations of NLP applications on supporting the clinical practices and we suggest some promising future research directions in this paper.

Indexed as

computer-aided diagnosiselectronic health recordselectronic medical recordsnatural language processingoncology

Identifiers

PMID36673096
PMCPMC9857980
OpenAlexW4315781034

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.