CodeMagic: Semi-Automatic Assignment of ICD-10-AM Codes to Patient Records

Arifoǧlu, Damla; Deniz, Onur; Aleçakir, Kemal; Yöndem, Meltem Turhan

doi:10.1007/978-3-319-09465-6_27

Cited by 18 publications

(16 citation statements)

References 5 publications

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“…Generic features such as bag of words (BoW) counts [20] and term frequency-inverse document frequency (TF-IDF) weights [21,22] are commonly used. Other features are healthcare-specific, including similarity scores between the input document and labels in a disease ontology or the metadata associated with those labels [23,24,25]. It is relatively straightforward to derive features using external medical knowledge and include these in the document representation, but it is more difficult to ensure that the model will learn to use these feature in the manner intended.…”

Section: Related Workmentioning

confidence: 99%

Towards automated clinical coding

Catling

Spithourakis

Riedel

2018

International Journal of Medical Informatics

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Background. Patients' encounters with healthcare services must undergo clinical coding. These codes are typically derived from free-text notes. Manual clinical coding is expensive, time-consuming and prone to error. Automated clinical coding systems have great potential to save resources, and realtime availability of codes would improve oversight of patient care and accelerate research. Automated coding is made challenging by the idiosyncrasies of clinical text, the large number of disease codes and their unbalanced distribution.Methods. We explore methods for representing clinical text and the labels in hierarchical clinical coding ontologies. Text is represented as term frequencyinverse document frequency counts and then as word embeddings, which we use as input to recurrent neural networks. Labels are represented atomically, and then by learning representations of each node in a coding ontology and composing a representation for each label from its respective node path. We consider different strategies for initialisation of the node representations. We evaluate our methods using the publicly-available Medical Information Mart for Intensive Care III dataset: we extract the history of presenting illness section from each discharge summary in the dataset, then predicting the International Classification of Diseases, ninth revision, Clinical Modification codes associated with these.

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Section: Related Workmentioning

confidence: 99%

Towards automated clinical coding

Catling

Spithourakis

Riedel

2018

International Journal of Medical Informatics

View full text Add to dashboard Cite

show abstract

“…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. Most of the above methods are only theoretical, and they have not yet been applied in practice.…”

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

show abstract

“…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. Most of the above methods are only theoretical, and they have not yet been applied in practice.…”

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

CodeMagic: Semi-Automatic Assignment of ICD-10-AM Codes to Patient Records

Cited by 18 publications

References 5 publications

Towards automated clinical coding

Towards automated clinical coding

Construction of a Semi-automatic ICD-10 Coding System

Construction of a semi-automatic ICD-10 coding system

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