A Coding Support System for the ICD-9-CM Standard

Chiaravalloti, Maria Teresa; Guarasci, Roberto; Lagani, Vincenzo; Pasceri, Erika; Trunfio, Roberto

doi:10.1109/ichi.2014.17

Cited by 8 publications

(7 citation statements)

References 7 publications

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“…For example, some authors have explored lexical similarities by enriching the representation through dictionaries [1]- [3]. In a similar way, other proposals have used documents as queries, applying the expansion with ontologies [4]- [6]. Following this tendency, repositories of medical terminology have been explored to improve the representation of documents before applying machine learning [7].…”

Section: A Icd-10 Codingmentioning

confidence: 99%

ICD-10 Coding of Spanish Electronic Discharge Summaries: An Extreme Classification Problem

et al. 2020

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Objective: Medical coding is used to identify and standardize clinical concepts in the records collected from healthcare services. The tenth revision of the International Classification of Diseases (ICD-10) is the most widely-used coding with more than 11,000 different diagnoses, affecting research, reporting, and funding. Unfortunately, ICD-10 code sets tend to follow biased, unbalanced, and scattered distributions. These distribution attributes, along with high lexical variability, severely restrict performance when coded clinical records are used to infer code sets in uncoded records. To improve that inference, we explore a combination of example-based methods optimized to capture codes with different appearance frequencies in data sets. Materials and Methods: The proposed exploration has been carried out on Spanish hospital discharge reports coded by experts, excluding all sentences without any biomedical concept. Representations based on semantic and lexical features are explored, using both global and labelspecific attributes. In turn, algorithms based on binary outputs, groups of subsets and extreme classification are compared. Lists of codes together with their confidence values (certainty probabilities) are suggested by each method. Results: Diverse spectral behaviors are shown for each method. Binary classifiers seem to maximize the capture of more popular codes, while extreme classifiers promote infrequent ones. In order to exploit such differences, ensemble approaches are proposed by weighting every output code according to the method, confidence value and appearance frequency. The rule-based combination reaches a 46% Precision at 10 (P@10), which means a 15% improvement over the best individual proposal. Conclusion: Assembling methods based on weighting each code according to training frequency and performance can achieve better overall Precision scores on extreme distributions, such as ICD-10 coding. INDEX TERMS Extreme classification, XMTC, ICD-10 coding, text mining.

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Section: A Icd-10 Codingmentioning

confidence: 99%

ICD-10 Coding of Spanish Electronic Discharge Summaries: An Extreme Classification Problem

et al. 2020

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“…Lastly, it may also be worth exploring more complex evaluation metrics aligned to the actual benefit for coders. This subject has recently been addressed in the literature, namely by Puentes et al (Puentes et al, 2013) through usability-related performance measures (from the coder perspective), by Perotte et al (Perotte et al, 2013) through hierarchy-based distance measures, and by (Chiaravalloti et al, 2014) based on code rankings. Accordingly, future work should consider these advanced performance measures, as well as issues concerning the acceptability and adoption of coding support tools by coding professionals in line with evidence on EHR system adoption (Weeger and Gewald, 2015…”

Section: Discussionmentioning

confidence: 99%

“…The corpora of clinical records used in previous studies ranged from admission notes (Gundersen et al, 1996) to radiology or pathology reports (Aronson et al, 2007;Crammer et al, 2007;Farkas and Szarvas, 2008;Goldstein et al, 2007;Matykiewicz et al, 2006;Oleynik et al, 2017;Rizzo et al, 2015;Suominen et al, 2008;Zhang, 2008), discharge summaries (Delamarre et al, 1995;Dinwoodie and Howell, 1973;Franz et al, 2000;Friedman et al, 2004;Kevers and Medori, 2010;Kukafka et al, 2006;Larkey and Croft, 1995;Li et al, 2011;Lussier et al, 2000,0;Medori and Fairon, 2010), death certificates (Koopman et al, 2015,1) and entire medical records (Kavuluru et al, 2015;Lita et al, 2008;Morris et al, 2000;Pakhomov et al, 2006;Ruch et al, 2008), with variable structure and level of curation. Moreover, the majority of studies has been based on English texts, with the exception of particular studies in French (Kevers and Medori, 2010;Medori and Fairon, 2010;Pereira et al, 2006;Ruch et al, 2008), Spanish (Pérez et al, 2015), Italian (Chiaravalloti et al, 2014;Rizzo et al, 2015) or German (Franz et al, 2000), while information extraction from Portuguese medical texts is still emmerging (Ferreira, 2011;Rijo et al, 2014). The scope of clinical conditions comprised in each study also varied greatly, ranging from limited sets of respiratory…”

Section: Review Of Studiesmentioning

confidence: 99%

Can structured EHR data support clinical coding? A data mining approach

et al. 2020

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Structured data formats are gaining momentum in electronic health record systems and can be leveraged for decision support and research. Nevertheless, such structured data formats have not been explored for clinical coding, which is an essential process requiring significant manual workload in health organizations. This article explores the extent to which fully structured clinical data can support the assignment of clinical codes to inpatient episodes, through the design and application of a methodology that tackles high dimensionality issues, addresses the multi-label nature of coding and optimizes model parameters. The methodology encompasses transforming database entries to define a feature set and build a data matrix representation, and testing combinations of filter feature selection methods with machine learning models to predict code assignment. The methodology is tested with a real hospital dataset, with results showing varying predictive power across codes but demonstrating the potential of leveraging structuring data to reduce workload and increase efficiency in clinical coding.

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“…All the above mentioned research studies are based on some type of deep learning, machine learning or statistical approach, where the information contained in the training data is distillate into mathematical models, which can be successfully employed for assigning ICD codes (Chiaravalloti et al, 2014). One of the main flaws in these approaches is that training data is annotated by human coders.…”

Section: Related Workmentioning

confidence: 99%

Distributed Knowledge Based Clinical Auto-Coding System

Kaur

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

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Codification of free-text clinical narratives have long been recognised to be beneficial for secondary uses such as funding, insurance claim processing and research. In recent years, many researchers have studied the use of Natural Language Processing (NLP), related Machine Learning (ML) methods and techniques to resolve the problem of manual coding of clinical narratives. Most of the studies are focused on classification systems relevant to the U.S and there is a scarcity of studies relevant to Australian classification systems such as ICD-10-AM and ACHI. Therefore, we aim to develop a knowledge-based clinical auto-coding system, that utilise appropriate NLP and ML techniques to assign ICD-10-AM and ACHI codes to clinical records, while adhering to both local coding standards (Australian Coding Standard) and international guidelines that get updated and validated continuously.

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A Coding Support System for the ICD-9-CM Standard

Cited by 8 publications

References 7 publications

ICD-10 Coding of Spanish Electronic Discharge Summaries: An Extreme Classification Problem

ICD-10 Coding of Spanish Electronic Discharge Summaries: An Extreme Classification Problem

Can structured EHR data support clinical coding? A data mining approach

Distributed Knowledge Based Clinical Auto-Coding System

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