Keyword extraction and structuralization of medical reports

Wu, Peihao; Yu, Avon; Tsai, Chin-Ho; Koh, Jia-Ling; Kuo, Chin‐Chi; Chen, Arbee L. P.

doi:10.1007/s13755-020-00108-6

Cited by 9 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For images, in the unstructured data processing of images, some algorithms have performed classification of pictures directly [31], and some studies have algorithms that give these images a text caption, then these images became text data [32]. For text, Wu et al used natural language analysis to automatically extract keyword lists from pathological examination reports [33]. Nandhakumar et al used the characteristics of words or sentences and conditional random field (CRF) models to extract important parts of medical reports [34].…”

Section: Machine-learning-based Methods For Construction Data Processingmentioning

confidence: 99%

ACS: Construction Data Auto-Correction System—Taiwan Public Construction Data Example

Tsai

2021

Sustainability

View full text Add to dashboard Cite

This study aims to develop an automatic data correction system for correcting the public construction data. The unstructured nature of the construction data presents challenges for its management. The different user habits, time-consuming system operation, and long pretraining time all make the data management system full of data in an inconsistent format or even incorrect data. Processing the construction data into a machine-readable format is not only time-consuming but also labor-intensive. Therefore, this study used Taiwan’s public construction data as an example case to develop a natural language processing (NLP) and machine learning-based text classification system, coined as automatic correction system (ACS). The developed system is designed to automatically correct the public construction data, meanwhile improving the efficiency of manual data correction. The ACS has two main features: data correction that converts unstructured data into structured data; a recommendation function that provides users with a recommendation list for manual data correction. For implementation, the developed system was used to correct the data in the public construction cost estimation system (PCCES) in Taiwan. We expect that the ACS can improve the accuracy of the data in the public construction database to increase the efficiency of the practitioners in executing projects. The results show that the system can correct 18,511 data points with an accuracy of 76%. Additionally, the system was also validated to reduce the system operation time by 51.69%.

show abstract

Section: Machine-learning-based Methods For Construction Data Processingmentioning

confidence: 99%

ACS: Construction Data Auto-Correction System—Taiwan Public Construction Data Example

Tsai

2021

Sustainability

View full text Add to dashboard Cite

show abstract

“…Peihao et al [ 18 ] proposed a system to store medical unstructured data and generate the structured form for the various examination items in a report according to the constructed medical dictionary. However, this system focuses on storing information extracted from unstructured data instead of storing unstructured data.…”

Section: Related Workmentioning

confidence: 99%

A heterogeneous multi-modal medical data fusion framework supporting hybrid data exploration

Zhang

Sheng

Liu

et al. 2022

Health Inf Sci Syst

View full text Add to dashboard Cite

Industry 4.0 era has witnessed that more and more high-tech and precise devices are applied into medical field to provide better services. Besides EMRs, medical data include a large amount of unstructured data such as X-rays, MRI scans, CT scans and PET scans, which is still continually increasing. These massive, heterogeneous multi-modal data bring the big challenge to finding valuable data sets for healthcare researchers and other users. The traditional data warehouses are able to integrate the data and support interactive data exploration through ETL process. However, they have high cost and are not real-time. Furthermore, they lack of the ability to deal with multi-modal data in two phases—data fusion and data exploration. In the data fusion phase, it is difficult to unify the multi-modal data under one data model. In the data exploration phase, it is challenging to explore the multi-modal data at the same time, which impedes the process of extracting the diverse information underlying multi-modal data. Therefore, in order to solve these problems, we propose a highly efficient data fusion framework supporting data exploration for heterogeneous multi-modal medical data based on data lake. This framework provides a novel and efficient method to fuse the fragmented multi-modal medical data and store their metadata in the data lake. It offers a user-friendly interface supporting hybrid graph queries to explore multi-modal data. Indexes are created to accelerate the hybrid data exploration. One prototype has been implemented and tested in a hospital, which demonstrates the effectiveness of our framework.

show abstract