Artificial Intelligence based Healthcare Knowledge Structure for Automatic Medical Report Generation

Automatic medical report production is a key text summarization application. Traditional text summarizing systems cannot completely interpret and use medical texts’ complicated vocabulary due to the disparities between medical consultation data and general domain data. The crucial knowledge in medical consultations has not been successfully harnessed. Traditional text summary algorithms cannot automatically identify, and filter key information based on medical reports’ structure and generate structured text. An AI-based knowledge-assisted structured medical report creation solution addresses these difficulties. This method uses entity-guided previous domain knowledge and structure-guided task decoupling to fully utilize medical consultation data and medical report structure. The suggested strategy improves ROUGE scores by 2% to 3% compared to similar methods, resulting in more accurate medical reports, according to IMCS21 dataset experiments.

Authors

Institutions

Publication Details

Published
2024-09-18
DOI
https://doi.org/10.1109/ic3i61595.2024.10828853
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Artificial Intelligence based Healthcare Knowledge Structure for Automatic Medical Report Generation

Pramod Kumar, Jitendra Kumar Chaudhary, D. Anand, Faisal Yousef Alghayadh et al.
Topic Modeling
article

Artificial Intelligence based Healthcare Knowledge Structure for Automatic Medical Report Generation

Pramod Kumar, Jitendra Kumar Chaudhary, D. Anand, Faisal Yousef Alghayadh, Vinay Prasad Tamta, Mukesh Soni
article en

Abstract

Automatic medical report production is a key text summarization application. Traditional text summarizing systems cannot completely interpret and use medical texts’ complicated vocabulary due to the disparities between medical consultation data and general domain data. The crucial knowledge in medical consultations has not been successfully harnessed. Traditional text summary algorithms cannot automatically identify, and filter key information based on medical reports’ structure and generate structured text. An AI-based knowledge-assisted structured medical report creation solution addresses these difficulties. This method uses entity-guided previous domain knowledge and structure-guided task decoupling to fully utilize medical consultation data and medical report structure. The suggested strategy improves ROUGE scores by 2% to 3% compared to similar methods, resulting in more accurate medical reports, according to IMCS21 dataset experiments.

Chandigarh University (IN), Lovely Professional University (IN), Alfaisal University (SA), Hemwati Nandan Bahuguna Garhwal University (IN), Almaarefa University, Graphic Era University (IN)
Openalex Percentile: Top 77%
Topic Modeling
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.