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Falter M, Godderis D, Scherrenberg M, Kizilkilic SE, Xu L, Mertens M, Jansen J, Legroux P, Kindermans H, Sinnaeve P, Neven F, Dendale P. Using natural language processing for automated classification of disease and to identify misclassified ICD codes in cardiac disease. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:229-234. [PMID: 38774372 PMCID: PMC11104467 DOI: 10.1093/ehjdh/ztae008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/30/2024] [Accepted: 02/05/2024] [Indexed: 05/24/2024]
Abstract
Aims ICD codes are used for classification of hospitalizations. The codes are used for administrative, financial, and research purposes. It is known, however, that errors occur. Natural language processing (NLP) offers promising solutions for optimizing the process. To investigate methods for automatic classification of disease in unstructured medical records using NLP and to compare these to conventional ICD coding. Methods and results Two datasets were used: the open-source Medical Information Mart for Intensive Care (MIMIC)-III dataset (n = 55.177) and a dataset from a hospital in Belgium (n = 12.706). Automated searches using NLP algorithms were performed for the diagnoses 'atrial fibrillation (AF)' and 'heart failure (HF)'. Four methods were used: rule-based search, logistic regression, term frequency-inverse document frequency (TF-IDF), Extreme Gradient Boosting (XGBoost), and Bio-Bidirectional Encoder Representations from Transformers (BioBERT). All algorithms were developed on the MIMIC-III dataset. The best performing algorithm was then deployed on the Belgian dataset. After preprocessing a total of 1438 reports was retained in the Belgian dataset. XGBoost on TF-IDF matrix resulted in an accuracy of 0.94 and 0.92 for AF and HF, respectively. There were 211 mismatches between algorithm and ICD codes. One hundred and three were due to a difference in data availability or differing definitions. In the remaining 108 mismatches, 70% were due to incorrect labelling by the algorithm and 30% were due to erroneous ICD coding (2% of total hospitalizations). Conclusion A newly developed NLP algorithm attained a high accuracy for classifying disease in medical records. XGBoost outperformed the deep learning technique BioBERT. NLP algorithms could be used to identify ICD-coding errors and optimize and support the ICD-coding process.
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Affiliation(s)
- Maarten Falter
- Faculty of Medicine and Life Sciences, Hasselt University, Agoralaan gebouw D, 3590 Diepenbeek, Hasselt, Belgium
- Heart Centre Hasselt, Jessa Hospital, Stadsomvaart 11, 3500 Hasselt, Belgium
- Department of Cardiology, KULeuven, Faculty of Medicine, Herestraat 49, 3000 Leuven, Belgium
| | - Dries Godderis
- Data Science Institute, Hasselt University, Agoralaan gebouw D, 3590 Diepenbeek, Hasselt, Belgium
| | - Martijn Scherrenberg
- Faculty of Medicine and Life Sciences, Hasselt University, Agoralaan gebouw D, 3590 Diepenbeek, Hasselt, Belgium
- Heart Centre Hasselt, Jessa Hospital, Stadsomvaart 11, 3500 Hasselt, Belgium
- Faculty of Medicine and Health Sciences, Antwerp University, Universiteitsplein 1, 2610 Antwerp, Belgium
| | - Sevda Ece Kizilkilic
- Faculty of Medicine and Life Sciences, Hasselt University, Agoralaan gebouw D, 3590 Diepenbeek, Hasselt, Belgium
- Heart Centre Hasselt, Jessa Hospital, Stadsomvaart 11, 3500 Hasselt, Belgium
- Faculty of Medicine and Health Sciences, Ghent University, Corneel Heymanslaan 10, 9000 Gent, Belgium
| | - Linqi Xu
- Faculty of Medicine and Life Sciences, Hasselt University, Agoralaan gebouw D, 3590 Diepenbeek, Hasselt, Belgium
- Heart Centre Hasselt, Jessa Hospital, Stadsomvaart 11, 3500 Hasselt, Belgium
| | - Marc Mertens
- Department of Information and Communications Technology, Jessa Hospital, Stadsomvaart 11, 3500 Hasselt, Belgium
| | - Jan Jansen
- Department of Information and Communications Technology, Jessa Hospital, Stadsomvaart 11, 3500 Hasselt, Belgium
| | - Pascal Legroux
- Department of Information and Communications Technology, Jessa Hospital, Stadsomvaart 11, 3500 Hasselt, Belgium
| | - Hanne Kindermans
- Faculty of Medicine and Life Sciences, Hasselt University, Agoralaan gebouw D, 3590 Diepenbeek, Hasselt, Belgium
| | - Peter Sinnaeve
- Department of Cardiology, KULeuven, Faculty of Medicine, Herestraat 49, 3000 Leuven, Belgium
| | - Frank Neven
- Data Science Institute, Hasselt University, Agoralaan gebouw D, 3590 Diepenbeek, Hasselt, Belgium
| | - Paul Dendale
- Faculty of Medicine and Life Sciences, Hasselt University, Agoralaan gebouw D, 3590 Diepenbeek, Hasselt, Belgium
- Heart Centre Hasselt, Jessa Hospital, Stadsomvaart 11, 3500 Hasselt, Belgium
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Marcou Q, Berti-Equille L, Novelli N. Creating a computer assisted ICD coding system: Performance metric choice and use of the ICD hierarchy. J Biomed Inform 2024; 152:104617. [PMID: 38432534 DOI: 10.1016/j.jbi.2024.104617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 02/23/2024] [Accepted: 02/24/2024] [Indexed: 03/05/2024]
Abstract
OBJECTIVE Machine learning methods hold the promise of leveraging available data and generating higher-quality data while alleviating the data collection burden on healthcare professionals. International Classification of Diseases (ICD) diagnoses data, collected globally for billing and epidemiological purposes, represents a valuable source of structured information. However, ICD coding is a challenging task. While numerous previous studies reported promising results in automatic ICD classification, they often describe input data specific model architectures, that are heterogeneously evaluated with different performance metrics and ICD code subsets. This study aims to explore the evaluation and construction of more effective Computer Assisted Coding (CAC) systems using generic approaches, focusing on the use of ICD hierarchy, medication data and a feed forward neural network architecture. METHODS We conduct comprehensive experiments using the MIMIC-III clinical database, mapped to the OMOP data model. Our evaluations encompass various performance metrics, alongside investigations into multitask, hierarchical, and imbalanced learning for neural networks. RESULTS We introduce a novel metric, , tailored to the ICD coding task, which offers interpretable insights for healthcare informatics practitioners, aiding them in assessing the quality of assisted coding systems. Our findings highlight that selectively cherry-picking ICD codes diminish retrieval performance without performance improvement over the selected subset. We show that optimizing for metrics such as NDCG and AUPRC outperforms traditional F1-based metrics in ranking performance. We observe that Neural Network training on different ICD levels simultaneously offers minor benefits for ranking and significant runtime gains. However, our models do not derive benefits from hierarchical or class imbalance correction techniques for ICD code retrieval. CONCLUSION This study offers valuable insights for researchers and healthcare practitioners interested in developing and evaluating CAC systems. Using a straightforward sequential neural network model, we confirm that medical prescriptions are a rich data source for CAC systems, providing competitive retrieval capabilities for a fraction of the computational load compared to text-based models. Our study underscores the importance of metric selection and challenges existing practices related to ICD code sub-setting for model training and evaluation.
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Affiliation(s)
- Quentin Marcou
- Aix-Marseille Université, Faculté des sciences médicales et paramédicales, Marseille, France; Aix-Marseille Université, UMR7020 CNRS, Laboratoire d'Informatique et Systèmes (LIS), Marseille, France.
| | | | - Noël Novelli
- Aix-Marseille Université, UMR7020 CNRS, Laboratoire d'Informatique et Systèmes (LIS), Marseille, France
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Tariq A, Goddard K, Elugunti P, Piorkowski K, Staal J, Viramontes A, Banerjee I, Patel BN. Contrastive diagnostic embedding (CDE) model for automated coding - A case study using emergency department encounters. Int J Med Inform 2023; 179:105212. [PMID: 37729838 DOI: 10.1016/j.ijmedinf.2023.105212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 08/07/2023] [Accepted: 09/01/2023] [Indexed: 09/22/2023]
Abstract
BACKGROUND Billing codes are utilized for medical reimbursement, clinical quality metric valuation and for epidemiologic purposes to report and follow disease trends and outcomes. The current paradigm of manual coding can be expensive, time-consuming, and subject to human error. Though automation of the billing codes has been widely reported in the literature via rule-based and supervised approaches, existing strategies lack generalizability and robustness towards large and constantly changing ICD hierarchical structure. METHOD We propose a weakly supervised training strategy by leveraging contrastive learning, contrastive diagnosis embedding (CDE) to capture the fine semantic variations between the diagnosis codes. The approach consists of a two-phase contrastive training for generating the semantic embedding space adapted to incorporate hierarchical information of ICD-10 vocabulary and a weakly supervised retrieval scheme. Core strength of the proposed method is that it puts no limit on the 70 K ICD-10 codes set and can handle all rare codes for coding the diagnosis. RESULTS Our CDE model outperformed string-based partial matching and ClinicalBERT embedding on three test cases (a retrospective testset, a prospective testset, and external testset) and produced an accurate prediction of rare and newly introduced diagnosis codes. A detailed ablation study showed the importance of each phase of the proposed multi-phase training. Each successive phase of training - ICD-10 group sensitive training (phase 1.1), ICD-10 subgroup sensitive training (phase 1.2), free-text diagnosis description-based training (phase 2) - improved performance beyond the previous phase of training. The model also outperformed existing supervised models like CAML and PLM-ICD and produced satisfactory performance on the rare codes. CONCLUSION Compared to the existing rule-based and supervised models, the proposed weakly supervised contrastive learning overcomes the limitations in terms of generalization capability and increases the robustness of the automated billing. Such a model will allow flexibility through accurate billing code automation for practice convergence and gains efficiencies in a value-based care payment environment.
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Affiliation(s)
- Amara Tariq
- Department of Administration, Mayo Clinic, AZ, USA.
| | - Kris Goddard
- Department of Administration, Mayo Clinic, AZ, USA
| | | | | | - Jared Staal
- Department of Administration, Mayo Clinic, AZ, USA
| | | | - Imon Banerjee
- Department of Radiology, Mayo Clinic, Phoenix, AZ, USA
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Lee YM, Bacchi S, Macri C, Tan Y, Casson RJ, Chan WO. Ophthalmology Operation Note Encoding with Open-Source Machine Learning and Natural Language Processing. Ophthalmic Res 2023; 66:928-939. [PMID: 37231984 PMCID: PMC10308528 DOI: 10.1159/000530954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 04/24/2023] [Indexed: 05/27/2023]
Abstract
INTRODUCTION Accurate assignment of procedural codes has important medico-legal, academic, and economic purposes for healthcare providers. Procedural coding requires accurate documentation and exhaustive manual labour to interpret complex operation notes. Ophthalmology operation notes are highly specialised making the process time-consuming and challenging to implement. This study aimed to develop natural language processing (NLP) models trained by medical professionals to assign procedural codes based on the surgical report. The automation and accuracy of these models can reduce burden on healthcare providers and generate reimbursements that reflect the operation performed. METHODS A retrospective analysis of ophthalmological operation notes from two metropolitan hospitals over a 12-month period was conducted. Procedural codes according to the Medicare Benefits Schedule (MBS) were applied. XGBoost, decision tree, Bidirectional Encoder Representations from Transformers (BERT) and logistic regression models were developed for classification experiments. Experiments involved both multi-label and binary classification, and the best performing model was used on the holdout test dataset. RESULTS There were 1,000 operation notes included in the study. Following manual review, the five most common procedures were cataract surgery (374 cases), vitrectomy (298 cases), laser therapy (149 cases), trabeculectomy (56 cases), and intravitreal injections (49 cases). Across the entire dataset, current coding was correct in 53.9% of cases. The BERT model had the highest classification accuracy (88.0%) in the multi-label classification on these five procedures. The total reimbursement achieved by the machine learning algorithm was $184,689.45 ($923.45 per case) compared with the gold standard of $214,527.50 ($1,072.64 per case). CONCLUSION Our study demonstrates accurate classification of ophthalmic operation notes into MBS coding categories with NLP technology. Combining human and machine-led approaches involves using NLP to screen operation notes to code procedures, with human review for further scrutiny. This technology can allow the assignment of correct MBS codes with greater accuracy. Further research and application in this area can facilitate accurate logging of unit activity, leading to reimbursements for healthcare providers. Increased accuracy of procedural coding can play an important role in training and education, study of disease epidemiology and improve research ways to optimise patient outcomes.
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Affiliation(s)
- Yong Min Lee
- Royal Adelaide Hospital, Adelaide, SA, Australia
- Machine Learning Division, Ophthalmic Research Laboratory, University of Adelaide, Adelaide, SA, Australia
| | - Stephen Bacchi
- Royal Adelaide Hospital, Adelaide, SA, Australia
- Machine Learning Division, Ophthalmic Research Laboratory, University of Adelaide, Adelaide, SA, Australia
| | - Carmelo Macri
- Royal Adelaide Hospital, Adelaide, SA, Australia
- Machine Learning Division, Ophthalmic Research Laboratory, University of Adelaide, Adelaide, SA, Australia
| | - Yiran Tan
- Royal Adelaide Hospital, Adelaide, SA, Australia
- Machine Learning Division, Ophthalmic Research Laboratory, University of Adelaide, Adelaide, SA, Australia
| | - Robert J. Casson
- Royal Adelaide Hospital, Adelaide, SA, Australia
- Machine Learning Division, Ophthalmic Research Laboratory, University of Adelaide, Adelaide, SA, Australia
| | - Weng Onn Chan
- Royal Adelaide Hospital, Adelaide, SA, Australia
- Machine Learning Division, Ophthalmic Research Laboratory, University of Adelaide, Adelaide, SA, Australia
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Venkatesh KP, Raza MM, Kvedar JC. Automating the overburdened clinical coding system: challenges and next steps. NPJ Digit Med 2023; 6:16. [PMID: 36737496 PMCID: PMC9898522 DOI: 10.1038/s41746-023-00768-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 01/27/2023] [Indexed: 02/05/2023] Open
Affiliation(s)
| | - Marium M. Raza
- grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA
| | - Joseph C. Kvedar
- grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA
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Post AR, Burningham Z, Halwani AS. Electronic Health Record Data in Cancer Learning Health Systems: Challenges and Opportunities. JCO Clin Cancer Inform 2022; 6:e2100158. [PMID: 35353547 PMCID: PMC9005105 DOI: 10.1200/cci.21.00158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 01/04/2022] [Accepted: 02/18/2022] [Indexed: 12/21/2022] Open
Affiliation(s)
- Andrew R. Post
- Research Informatics Shared Resource, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT
| | - Zachary Burningham
- Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT
| | - Ahmad S. Halwani
- Division of Hematology and Hematologic Malignancies, Department of Internal Medicine, University of Utah, Salt Lake City, UT
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Shuai Z, Xiaolin D, Jing Y, Yanni H, Meng C, Yuxin W, Wei Z. Comparison of different feature extraction methods for applicable automated ICD coding. BMC Med Inform Decis Mak 2022; 22:11. [PMID: 35022039 PMCID: PMC8756659 DOI: 10.1186/s12911-022-01753-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 01/04/2022] [Indexed: 01/10/2023] Open
Abstract
Abstract
Background
Automated ICD coding on medical texts via machine learning has been a hot topic. Related studies from medical field heavily relies on conventional bag-of-words (BoW) as the feature extraction method, and do not commonly use more complicated methods, such as word2vec (W2V) and large pretrained models like BERT. This study aimed at uncovering the most effective feature extraction methods for coding models by comparing BoW, W2V and BERT variants.
Methods
We experimented with a Chinese dataset from Fuwai Hospital, which contains 6947 records and 1532 unique ICD codes, and a public Spanish dataset, which contains 1000 records and 2557 unique ICD codes. We designed coding tasks with different code frequency thresholds (denoted as $$f_s$$
f
s
), with a lower threshold indicating a more complex task. Using traditional classifiers, we compared BoW, W2V and BERT variants on accomplishing these coding tasks.
Results
When $$f_s$$
f
s
was equal to or greater than 140 for Fuwai dataset, and 60 for the Spanish dataset, the BERT variants with the whole network fine-tuned was the best method, leading to a Micro-F1 of 93.9% for Fuwai data when $$f_s=200$$
f
s
=
200
, and a Micro-F1 of 85.41% for the Spanish dataset when $$f_s=180$$
f
s
=
180
. When $$f_s$$
f
s
fell below 140 for Fuwai dataset, and 60 for the Spanish dataset, BoW turned out to be the best, leading to a Micro-F1 of 83% for Fuwai dataset when $$f_s=20$$
f
s
=
20
, and a Micro-F1 of 39.1% for the Spanish dataset when $$f_s=20$$
f
s
=
20
. Our experiments also showed that both the BERT variants and BoW possessed good interpretability, which is important for medical applications of coding models.
Conclusions
This study shed light on building promising machine learning models for automated ICD coding by revealing the most effective feature extraction methods. Concretely, our results indicated that fine-tuning the whole network of the BERT variants was the optimal method for tasks covering only frequent codes, especially codes that represented unspecified diseases, while BoW was the best for tasks involving both frequent and infrequent codes. The frequency threshold where the best-performing method varied differed between different datasets due to factors like language and codeset.
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Newman-Griffis D, Camacho Maldonado J, Ho PS, Sacco M, Jimenez Silva R, Porcino J, Chan L. Linking Free Text Documentation of Functioning and Disability to the ICF With Natural Language Processing. FRONTIERS IN REHABILITATION SCIENCES 2021; 2. [PMID: 35694445 PMCID: PMC9180751 DOI: 10.3389/fresc.2021.742702] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Background: Invaluable information on patient functioning and the complex interactions that define it is recorded in free text portions of the Electronic Health Record (EHR). Leveraging this information to improve clinical decision-making and conduct research requires natural language processing (NLP) technologies to identify and organize the information recorded in clinical documentation. Methods: We used natural language processing methods to analyze information about patient functioning recorded in two collections of clinical documents pertaining to claims for federal disability benefits from the U.S. Social Security Administration (SSA). We grounded our analysis in the International Classification of Functioning, Disability, and Health (ICF), and used the Activities and Participation domain of the ICF to classify information about functioning in three key areas: mobility, self-care, and domestic life. After annotating functional status information in our datasets through expert clinical review, we trained machine learning-based NLP models to automatically assign ICF categories to mentions of functional activity. Results: We found that rich and diverse information on patient functioning was documented in the free text records. Annotation of 289 documents for Mobility information yielded 2,455 mentions of Mobility activities and 3,176 specific actions corresponding to 13 ICF-based categories. Annotation of 329 documents for Self-Care and Domestic Life information yielded 3,990 activity mentions and 4,665 specific actions corresponding to 16 ICF-based categories. NLP systems for automated ICF coding achieved over 80% macro-averaged F-measure on both datasets, indicating strong performance across all ICF categories used. Conclusions: Natural language processing can help to navigate the tradeoff between flexible and expressive clinical documentation of functioning and standardizable data for comparability and learning. The ICF has practical limitations for classifying functional status information in clinical documentation but presents a valuable framework for organizing the information recorded in health records about patient functioning. This study advances the development of robust, ICF-based NLP technologies to analyze information on patient functioning and has significant implications for NLP-powered analysis of functional status information in disability benefits management, clinical care, and research.
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Affiliation(s)
- Denis Newman-Griffis
- Rehabilitation Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD, United States
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
- *Correspondence: Denis Newman-Griffis
| | - Jonathan Camacho Maldonado
- Rehabilitation Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD, United States
| | - Pei-Shu Ho
- Rehabilitation Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD, United States
| | - Maryanne Sacco
- Rehabilitation Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD, United States
| | - Rafael Jimenez Silva
- Rehabilitation Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD, United States
| | - Julia Porcino
- Rehabilitation Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD, United States
| | - Leighton Chan
- Rehabilitation Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD, United States
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Diao X, Huo Y, Zhao S, Yuan J, Cui M, Wang Y, Lian X, Zhao W. Automated ICD coding for primary diagnosis via clinically interpretable machine learning. Int J Med Inform 2021; 153:104543. [PMID: 34391016 DOI: 10.1016/j.ijmedinf.2021.104543] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 06/24/2021] [Accepted: 07/20/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Computer-assisted clinical coding (CAC) based on automated coding algorithms has been expected to improve the International Classification of Disease, tenth version (ICD-10) coding quality and productivity, whereas studies oriented to primary diagnosis auto-coding are limited in the Chinese context. OBJECTIVE This study aims at developing a machine learning (ML) model for automated primary diagnosis ICD-10 coding. METHODS A total of 71,709 admissions in Fuwai hospital were included to carry out this study, corresponding to 168 primary diagnosis ICD-10 codes. Based on clinical implications, two feature engineering methods were used to process discharge diagnosis and procedure texts into sequential features and sequential grouping features respectively by which two kinds of models were built and compared. One baseline model using one-hot encoding features was considered. Light Gradient Boosting Machine (LightGBM) was adopted as the classifier, and grid search and cross-validation were used to select the optimal hyperparameters. SHapley Additive exPlanations (SHAP) values were applied to give the interpretability of models. RESULTS Our best prediction model was developed based on sequential grouping features. It showed good performance in the test phase with accuracy and macro-averaged F1 (Macro-F1) of 95.2% and 88.3% respectively. The comparison of the models demonstrated the effectiveness of the sequential information and the grouping strategy in boosting model performance (P-value < 0.01). Subgroup analysis of the best model on each individual code manifested that 91.1% of the codes achieved the F1 over 70.0%. CONCLUSIONS Our model has been demonstrated its effectiveness for automated primary diagnosis coding in the Chinese context and its results are interpretable. Hence, it has the potential to assist clinical coders to improve coding efficiency and quality in Chinese inpatient settings.
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Affiliation(s)
- Xiaolin Diao
- Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Yanni Huo
- Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Shuai Zhao
- Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Jing Yuan
- Department of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Meng Cui
- Medical Record Department, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Yuxin Wang
- Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Xiaodan Lian
- Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Wei Zhao
- Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
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