Towards automatic encoding of medical procedures using convolutional neural networks and autoencoders

Deng, Yihan; Sander, Andre; Faulstich, Lukas; Denecke, Kerstin

doi:10.1016/j.artmed.2018.10.001

Cited by 21 publications

(16 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the recall, F-measure and accuracy are low in our study compared to these method mentioned above [34][35][36]. For example, the CNN based method had reached a F-measure of 60.86% with high efficiency [34], and the reference [36] building a feature matrix, by a pretrained word embedding model used to train a CNN had a high testing accuracy (F-measure 90.86% ). Whether our system can be fully automated with high precision by combining with the state of the art is a longterm task that we need to consider.…”

Section: Discussionmentioning

confidence: 67%

“…The technical requirements and computational cost are less than those of the other methods found in most studies [7], [11], [32][33][34][35][36]. Convolutional neural network (CNN) [18], [34][35][36] is one of the state of the art proposals to solve the problem of automatic ICD coding. Despite their high accuracy, there is still a long way to go before they can be used in practice.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Construction of a Semi-automatic ICD-10 Coding System

Zhou

Cheng

et al. 2020

Preprint

View full text Add to dashboard Cite

BackgroundThe International Classification of Diseases, 10th Revision (ICD-10) has been widely used to describe the diagnosis information of patients. Automatic ICD-10 coding is important because manually assigning codes is expensive, time consuming and error prone. Although numerous approaches have been developed to explore automatic coding, few of them have been applied in practice. Our aim is to construct a practical, automatic ICD-10 coding machine to improve coding efficiency and quality in daily work. MethodsIn this study, we propose the use of regular expressions (regexps) to establish a correspondence between diagnosis codes and diagnosis descriptions in outpatient settings and at admission and discharge. The description models of the regexps were embedded in our upgraded coding system, which queries a diagnosis description and assigns a unique diagnosis code. Like most studies, the precision (P), recall (R), F-measure (F) and overall accuracy (A) were used to evaluate the system performance. Our study had two stages. The datasets were obtained from the diagnosis information on the homepage of the discharge medical record. The testing sets were from ResultsThe values of P were 89.27% and 88.38% in the first testing phase and the second testing phase, respectively, which demonstrate high precision. The automatic ICD-10 coding system completed more than 160,000 codes in 16 months, which reduced the workload of the coders. In addition, a comparison between the amount of time needed for manual coding and automatic coding indicated the effectiveness of the system-the time needed for automatic coding takes nearly 100 times less than manual coding. ConclusionsOur automatic coding system is well suited for the coding task. Further studies are warranted to perfect the description models of the regexps and to develop synthetic approaches to improve system

show abstract

Section: Discussionmentioning

confidence: 67%

Section: Discussionmentioning

confidence: 99%

Construction of a Semi-automatic ICD-10 Coding System

Zhou

Cheng

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, in future work, with the complete ICD-10 coding set as the goal, matching rules need to be improved constantly. In addition, the recall, F-measure and accuracy are low in our study compared to these method mentioned above [34][35][36]. For example, the CNN based method had reached a F-measure of 60.86% with high efficiency [34], and the reference [36] building a feature matrix, by a pretrained word embedding model used to train a CNN had a high testing accuracy (F-measure 90.86%).…”

Section: Discussionmentioning

confidence: 67%

“…In addition, the recall, F-measure and accuracy are low in our study compared to these method mentioned above [34][35][36]. For example, the CNN based method had reached a F-measure of 60.86% with high efficiency [34], and the reference [36] building a feature matrix, by a pretrained word embedding model used to train a CNN had a high testing accuracy (F-measure 90.86%). Whether our system can be fully automated with high precision by combining with the state of the art is a long-term task that we need to consider.…”

Section: Discussionmentioning

confidence: 67%

“…Five, we update the existing manual coding system based on the rule base of regexps to reduce workload and improve the work quality of coders. The technical requirements and computational cost are less than those of the other methods found in most studies [7,11], [32][33][34][35][36]. CNN [18,[34][35][36] is one of the state of the art proposals to solve the problem of automatic ICD coding.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Construction of a semi-automatic ICD-10 coding system

Zhou

Cheng

et al. 2020

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Background: The International Classification of Diseases, 10th Revision (ICD-10) has been widely used to describe the diagnosis information of patients. Automatic ICD-10 coding is important because manually assigning codes is expensive, time consuming and error prone. Although numerous approaches have been developed to explore automatic coding, few of them have been applied in practice. Our aim is to construct a practical, automatic ICD-10 coding machine to improve coding efficiency and quality in daily work. Methods: In this study, we propose the use of regular expressions (regexps) to establish a correspondence between diagnosis codes and diagnosis descriptions in outpatient settings and at admission and discharge. The description models of the regexps were embedded in our upgraded coding system, which queries a diagnosis description and assigns a unique diagnosis code. Like most studies, the precision (P), recall (R), F-measure (F) and overall accuracy (A) were used to evaluate the system performance. Our study had two stages. The datasets were obtained from the diagnosis information on the homepage of the discharge medical record. The testing sets were from Results: The values of P were 89.27 and 88.38% in the first testing phase and the second testing phase, respectively, which demonstrate high precision. The automatic ICD-10 coding system completed more than 160,000 codes in 16 months, which reduced the workload of the coders. In addition, a comparison between the amount of time needed for manual coding and automatic coding indicated the effectiveness of the system-the time needed for automatic coding takes nearly 100 times less than manual coding. Conclusions: Our automatic coding system is well suited for the coding task. Further studies are warranted to perfect the description models of the regexps and to develop synthetic approaches to improve system performance.

show abstract

Atypical Point of View on Semantic Computing in Healthcare

Mayuri¹,

Mehata²

2021

Semantic Web for Effective Healthcare

View full text Add to dashboard Cite

Towards automatic encoding of medical procedures using convolutional neural networks and autoencoders

Cited by 21 publications

References 11 publications

Construction of a Semi-automatic ICD-10 Coding System

Construction of a Semi-automatic ICD-10 Coding System

Construction of a semi-automatic ICD-10 coding system

Atypical Point of View on Semantic Computing in Healthcare

Contact Info

Product

Resources

About