Deep neural models for ICD-10 coding of death certificates and autopsy reports in free-text

Duarte, Francisco; Martins, Bruno; Pinto, Cátia Sousa; Silva, Mário J.

doi:10.1016/j.jbi.2018.02.011

Cited by 58 publications

(69 citation statements)

References 25 publications

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“…Another study [20] combined three coding systems into a single superior system to improve the performance of automated ICD-9-CM codes in clinical reports. In [12,[21][22], ICD-10 coding of death certificates has already been addressed by automation. In [23] semiautomatic assignment of the indexing system was performed by exploiting the idea of the bag-ofwords mapping indexing tool.…”

Section: Introductionmentioning

confidence: 99%

Construction of a Semi-automatic ICD-10 Coding System

Zhou

Cheng

et al. 2020

Preprint

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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

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Section: Introductionmentioning

confidence: 99%

Construction of a Semi-automatic ICD-10 Coding System

Zhou

Cheng

et al. 2020

Preprint

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“…"Deep neural models for ICD-10 coding of death certificates and autopsy reports in free-text" [48], by Duarte et al, presents an intricate model for automated labeling of medical documents. We consider the author's technique for encoding qualitative data an automated technique because at a high level it relies on entity embedding via Keras Embedding layers.…”

Section: Hierarchical Gated Recurrent Unitsmentioning

confidence: 99%

“…Their approach involves nested components. The source code in [49] that accompanies the paper [48] is most helpful in guiding one to understand Duarte et al 's approach. The authors use a Keras Embedding layer to encode the words in the fields of the clinical forms that constitute the input to their model.…”

Section: Hierarchical Gated Recurrent Unitsmentioning

confidence: 99%

Survey on categorical data for neural networks

2020

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This survey investigates current techniques for representing qualitative data for use as input to neural networks. Techniques for using qualitative data in neural networks are well known. However, researchers continue to discover new variations or entirely new methods for working with categorical data in neural networks. Our primary contribution is to cover these representation techniques in a single work. Practitioners working with big data often have a need to encode categorical values in their datasets in order to leverage machine learning algorithms. Moreover, the size of data sets we consider as big data may cause one to reject some encoding techniques as impractical, due to their running time complexity. Neural networks take vectors of real numbers as inputs. One must use a technique to map qualitative values to numerical values before using them as input to a neural network. These techniques are known as embeddings, encodings, representations, or distributed representations. Another contribution this work makes is to provide references for the source code of various techniques, where we are able to verify the authenticity of the source code. We cover recent research in several domains where researchers use categorical data in neural networks. Some of these domains are natural language processing, fraud detection, and clinical document automation. This study provides a starting point for research in determining which techniques for preparing qualitative data for use with neural networks are best. It is our intention that the reader should use these implementations as a starting point to design experiments to evaluate various techniques for working with qualitative data in neural networks. The third contribution we make in this work is a new perspective on techniques for using categorical data in neural networks. We organize techniques for using categorical data in neural networks into three categories. We find three distinct patterns in techniques that identify a technique as determined, algorithmic, or automated. The fourth contribution we make is to identify several opportunities for future research. The form of the data that one uses as an input to a neural network is crucial for using neural networks effectively. This work is a tool for researchers to find the most effective technique for working with categorical data in neural networks, in big data settings. To the best of our knowledge this is the first in-depth look at techniques for working with categorical data in neural networks.

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“…For example, several studies based on machine learning approaches, such as the support vector machine (SVM) method [5][6][7][8], were proposed to automatically assign ICD-10 codes. With the extensive application of deep learning methods in various fields, these methods have also been widely used in automated ICD coding [9][10][11][12]. These studies indicate that deep learning models can produce interpretable results and can code automatically in a reasonable way.…”

Section: Introductionmentioning

confidence: 99%

“…Another study [20] combined three coding systems into a single superior system to improve the performance of automated ICD-9-CM codes in clinical reports. In [12,21,22], ICD-10 coding of death certificates has already been addressed by automation. In [23] semi-automatic assignment of the indexing system was performed by exploiting the idea of the bag-ofwords mapping indexing tool.…”

Section: Introductionmentioning

confidence: 99%

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

Deep neural models for ICD-10 coding of death certificates and autopsy reports in free-text

Cited by 58 publications

References 25 publications

Construction of a Semi-automatic ICD-10 Coding System

Construction of a Semi-automatic ICD-10 Coding System

Survey on categorical data for neural networks

Construction of a semi-automatic ICD-10 coding system

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