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23rd IEEE International Conference on Information Reuse and Integration for Data Science (IEEE IRI)

作者： Carberry, Joshua Xu, Haiping Univ Massachusetts Dartmouth Comp & Informat Sci Dept Dartmouth MA 02747 USA

ISBN: (数字)9781665466035

ISBN: (纸本)9781665466035

Assigning International Classification of Diseases (icd) codes based on doctors' clinical diagnoses has historically been a difficult task performed by highly trained clinical coding experts. Recently, attempts have been made to use machine learning techniques at a coarse-grained level to automatically generate lists of medical codes from doctors' notes;however, the results are often difficult to interpret and validate. In this paper, we propose a fine-grained approach that focuses on one diagnosis at a time. We use ontology-based human knowledge to extract semantically related sentences from doctor's notes to support the use of deep learning for reliable training and classification. This fine-grained deep learning approach significantly reduces training load and improves scalability while providing users with a rationale for icd code prediction. To demonstrate the effectiveness and advantages of our approach, we apply it to the MIMIC-III dataset and show how icd-9 codes can be automatically assigned to clinical diagnoses.

关键词： doctor's notes icd code ontology deep learning fine-grained code assignment

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Automatic icd code assignment of Chinese clinical notes based on multilayer attention BiRNN

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JOURNAL OF BIOMEDICAL INFORMATICS 2019年 91卷 103114-000页

作者： Yu, Ying Li, Min Liu, Liangliang Fei, Zhihui Wu, Fang-Xiang Wang, Jianxin Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Univ South China Sch Comp Sci & Technol Hengyang 421001 Peoples R China Univ Saskatchewan Div Biomed Engn Saskatoon SK S7N 5A9 Canada Univ Saskatchewan Dept Mech Engn Saskatoon SK S7N 5A9 Canada

International Classification of Diseases (icd) code is an important label of electronic health record. The automatic icd code assignment based on the narrative of clinical documents is an essential task which has drawn much attention recently. When Chinese clinical notes are the input corpus, the nature of Chinese brings some issues that need to be considered, such as the accuracy of word segmentation and the representation of single Chinese characters which contain semantics. Taking the lengthy text of patient notes and the representation of Chinese words into account, we present a multilayer attention bidirectional recurrent neural network (MA-BiRNN) model to implement the assignment of disease codes. A hierarchical approach is used to represent the feature of discharge summaries without manual feature engineering. The combination of character level embedding and word level embedding can improve the representation of words. Attention mechanism is introduced into bidirectional long short term memory networks, which helps to solve the performance dropping problem when plain recurrent neural networks encounter long text sequences. The experiment is carried out on a real-world dataset containing 7732 admission records in Chinese and 1177 unique icd-10 labels. The proposed model achieves 0.639 and 0.766 in F1-score on full-level code and block-level code, respectively. It outperforms the baseline neural network models and achieves the lowest Hamming loss value. Ablation analysis indicates that the multilevel attention mechanism plays a decisive role in the system for dealing with Chinese clinical notes.

关键词： icd code Clinical notes Multilayer attention Character-enhanced

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Welcome to the icd-10 code for sarcopenia

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JOURNAL OF CACHEXIA SARCOPENIA AND MUSCLE 2016年第5期7卷 512-514页

作者： Anker, Stefan D. Morley, John E. von Haehling, Stephan Georg August Univ Univ Gottingen Med Ctr Innovat Clin TrialsDept Cardiol & Pneumol Gottingen Germany St Louis Univ Sch Med Div Geriatr Med 1402 S Grand BlvdM238 St Louis MO 63104 USA St Louis Univ Sch Med Div Endocrinol St Louis MO USA

The new icd-10-CM (M62.84) code for sarcopenia represents a major step forward in recognizing sarcopenia as a disease. This should lead to an increase in availability of diagnostic tools and the enthusiasm for pharmac... 详细信息

关键词： Aging Sarcopenia icd code

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Context-aware automated icd coding: A semantic-driven approach

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INFORMATION SYSTEMS 2025年 132卷

作者： Reshma, O. K. Saleena, N. Nazeer, K. A. Abdul Natl Inst Technol Calicut Dept Comp Sci & Engn Kozhikode 673601 Kerala India

Identifying the exact International Classification of Diseases (icd) codes describing a patient' s health condition is essential in classifying patients with similar disease conditions. Numerous studies have devised automated approaches to retrieve the icd codes from patients' health records. However, majority of these methodologies have considered icd codes solely as alphanumeric codes, overlooking their descriptions and thus neglecting the inherent semantics. Also, these methodologies overlook the one-to-many semantic relationships between diagnosis and assigned icd code descriptions. Subsequently, this constrains these approaches from effectively assigning icd codes with meaningful context. This work addresses these limitations by capturing the semantic similarity between the diagnosis and icd code descriptions, while utilising the inherent one- to-many relationships between them, to accurately assign icd codes. For this, we formulate the icd coding problem as a Semantic Text Similarity task. The proposed approach uses a siamese stacked Bi-LSTM network to learn context-aware representations of diagnoses and icd code descriptions. We transform each patient-visit data into sentence pairs by considering the one-to-many relationships between diagnosis and assigned icd code descriptions. Further, we compute their semantic similarity and classify them as similar or dissimilar. The proposed approach was evaluated using 5-fold cross-validation on MIMIC-III dataset and achieved the highest evaluation metric scores (F1-score 0.66, precision 0.67, recall 0.84) compared with other sequential models. The per-label evaluation demonstrates the performance of the proposed approach for each icd code. Furthermore, the proposed approach outperformed several existing attention-based models, demonstrating the potential use of semantics in automated icd coding.

关键词： Electronic Health Records icd code Semantic Textual Similarity Bidirectional Long Short Term Memory Network Siamese network

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Conceptual-driven classification for coding advise in health insurance reimbursement

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2011年第1期51卷 27-41页

作者： Li, Sheng-Tun Chen, Chih-Chuan Huang, Fernando Natl Cheng Kung Univ Inst Informat Management Dept Ind & Informat Management Tainan 701 Taiwan Taiwan Shoufu Univ Dept Leisure Informat Management Tainan 721 Taiwan

Objective: With the non-stop increases in medical treatment fees, the economic survival of a hospital in Taiwan relies on the reimbursements received from the Bureau of National Health Insurance, which in turn depend on the accuracy and completeness of the content of the discharge summaries as well as the correctness of their International Classification of Diseases (icd) codes. The purpose of this research is to enforce the entire disease classification framework by supporting disease classification specialists in the coding process. Methodology: This study developed an icd code advisory system (icd-AS) that performed knowledge discovery from discharge summaries and suggested icd codes. Natural language processing and information retrieval techniques based on Zipf's Law were applied to process the content of discharge summaries, and fuzzy formal concept analysis was used to analyze and represent the relationships between the medical terms identified by MeSH. In addition, a certainty factor used as reference during the coding process was calculated to account for uncertainty and strengthen the credibility of the outcome. Results: Two sets of 360 and 2579 textual discharge summaries of patients suffering from cerebrovascular disease was processed to build up icd-AS and to evaluate the prediction performance. A number of experiments were conducted to investigate the impact of system parameters on accuracy and compare the proposed model to traditional classification techniques including linear-kernel support vector machines. The comparison results showed that the proposed system achieves the better overall performance in terms of several measures. In addition, some useful implication rules were obtained, which improve comprehension of the field of cerebrovascular disease and give insights to the relationships between relevant medical terms. Conclusion: Our system contributes valuable guidance to disease classification specialists in the process of coding discharge summ

关键词： Knowledge discovery Text mining Fuzzy formal concept analysis Information retrieval icd code Health insurance

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Multimodal Attention Network for Dementia Prediction

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第11期28卷 6918-6930页

作者： Tsai, Hsinhan Yang, Ta-Wei Ou, Kai-Hao Su, Tung-Hung Lin, Che Chou, Cheng-Fu Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei 106319 Taiwan Natl Taiwan Univ Grad Inst Networking & Multimedia Taipei 106319 Taiwan Natl Taiwan Univ Hosp Dept Internal Med Div Gastroenterol & Hepatol Taipei 100229 Taiwan Natl Taiwan Univ Hosp Hepatitis Res Ctr Taipei 100229 Taiwan Natl Taiwan Univ Grad Inst Commun Engn Ctr Computat & Syst Biol Smart Med & Hlth Informat ProgramDept Elect Engn Taipei 106319 Taiwan Natl Taiwan Univ Ctr Biotechnol Taipei 106319 Taiwan

The early identification of an individual's dementia risk is crucial for disease prevention and the design of insurance products in an aging society. This study aims to accurately predict the future incidence risk of dementia in individuals by leveraging the advantages of neural networks. This is, however, complicated by the high dimensionality and sparsity of the International Classification of Diseases (icd) codes when utilizing data from Taiwan's National Health Insurance, which includes individual profiles and medical records. Inspired by the click-through rate (CTR) problem in recommendation systems, where future user behavior is predicted based on their past consumption records, we address these challenges with a multimodal attention network for dementia (MAND), which incorporates an icd code embedding layer and multihead self-attention to encode icd codes and capture interactions among diseases. Additionally, we investigate the applicability of several CTR methods to the dementia prediction problem. MAND achieves an AUC of 0.9010, surpassing traditional CTR models and demonstrating its effectiveness. The highly flexible pipelined design allows for module replacement to meet specific requirements. Furthermore, the analysis of attention scores reveals diseases highly correlated with dementia, aligning with prior research and emphasizing the interpretability of the model. This research deepens our understanding of the diseases associated with dementia, and the accurate prediction provided can serve as an early warning for dementia occurrence, aiding in its prevention.

关键词： Diseases Dementia codes Feature extraction Encoding Predictive models Insurance Attention mechanism click-through rate dementia icd code icd code embedding

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Prevalence of dementia in university hospitals. Analysis of icd-10 coding in 5 university hospitals in Germany over a 2-year period

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ZEITSCHRIFT FUR GERONTOLOGIE UND GERIATRIE 2019年第6期52卷 575-581页

作者： Vogt, Birgit Mai, Tobias Feuchtinger, Johanna Maucher, Helene Strohbuecker, Barbara Flechinger, Christa Alpers, Birgit Roes, Martina Univ Klinikum Hamburg Eppendorf Direkt Patienten & Pflegemanagement Martinistr 52 D-20246 Hamburg Germany Private Univ Witten Herdecke gGmbH Dept Pflegewissensch Fak Gesundheit Witten Germany Deutsch Zentrum Neurodegenerat Erkrankungen DZNE Witten Germany Univ Klinikum Frankfurt Frankfurt Germany Univ Klinikum Freiburg Freiburg Germany Univ & Rehabil Kliniken Ulm gGmbH Ulm Germany Uniklin Koln Cologne Germany

Background Demographic changes result in a higher prevalence of patients suffering from dementia in hospital. In Germany, epidemiological data of this target group are scarce and prevalence rates from university hospitals (UH) are not available. The prevalence rates and distribution were analyzed on the basis of icd-10-GM (German modification) routine data Method A secondary analysis on icd-10-GM main and secondary diagnoses of dementia from 2014 and 2015 from 5 UH was performed. All patients admitted to hospital for at least 24h and >= 18 years old (2014 n = 187,168;2015 n = 189,040) were included. A descriptive analysis for the >69-year-old group was carried out (2014, n = 67,111;2015;n = 67,824). Results The 1-year prevalence (2014/2015) for all 5 UH for patients >= 18 years old was 1.3%/1.4% and for the >69-year-old group, 3.3%/3.5%. The prevalence rates between the five UH varied: for patients >= 18 years the range was 0.44-2.16% (2014) and 0.44-2.77% (2015) and for >69-year-olds 1.16-5.52% (2014) and 1.16-7.06% (2015). Most cases were correlated with major diagnostic categories of traumatology, cardiology, gastroenterology and neurology. Conclusion Analysis of icd-10-GM routine data can provide an indication of the prevalence of dementia in UH. Results of the >69-year-olds varied greatly between participating UH. The reasons for this might be different healthcare tasks, especially with respect to geriatric patients;however, it is also possible that assessment procedures are not standardized and unreliable and therefore the coding is invalid. A standardized procedure for the identification of people suffering from dementia is necessary.

关键词： Dementia Prevalence Inpatient Acute hospital icd code

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Accuracy of veterans administration databases for a diagnosis of rheumatoid arthritis

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ARTHRITIS & RHEUMATISM-ARTHRITIS CARE & RESEARCH 2004年第6期51卷 952-957页

作者： Singh, JA Holmgren, AR Noorbaloochi, S Minneapolis VA Med Ctr Minneapolis MN 55417 USA Univ Minnesota Minneapolis MN USA Abbott NW Hosp Minneapolis MN USA

Objective. To determine the accuracy of International Classification of Diseases (icd) code 714 for rheumatoid arthritis (RA) diagnosis in a Veterans Administration (VA) hospital database and to examine the effects of adding laboratory and pharmacy data to icd code 714 on accuracy of RA diagnosis. Methods. We drew a random sample of patients from all Minneapolis VA rheumatology clinic patients who had at least 1 rheumatology clinic visit between January 2001 and July 2002. Charts of 184 patients were reviewed. The gold standard for RA diagnosis was chart documentation of RA diagnosis by a rheumatologist on greater than or equal to2 visits >6 weeks apart. The data definitions of RA diagnosis included presence of icd code 714 alone or various combinations of icd code 714, a positive rheumatoid factor (RF), and prescription for a disease-modifying antirheumatic drug (DMARD). Accuracy of data definitions of RA was assessed by calculating sensitivity, specificity, positive and negative predictive values, and area under the receiver operator characteristics curve. Results. Diagnosis by icd code 714 had 100% sensitivity, but specificity was only 55% because of a false-positive rate of 34%. The addition of a positive RF and/or a DMARD prescription to icd code 714 dramatically improved specificity to 83-97% and positive predictive value to 81-97%;however, sensitivity decreased to 76-88%. Diagnosis by icd 714 alone had the highest negative predicative value of 100%. The area under the curve was the greatest when both icd code 714 and a positive RF were included, and the least when icd code alone was used. Conclusion. icd code 714 in the VA administrative database is a very sensitive screening tool for identifying patients with RA in the rheumatology clinic population. Addition of the presence of a DMARD prescription and/or a positive RF to selection criteria improves specificity of the diagnosis.

关键词： accuracy icd code rheumatoid arthritis administrative database sensitivity specificity predictive value area under the curve

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Case Ascertainment in Pediatric Heart Failure Using International Classification of Disease Clinical Modification (icd-CM) codes

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PEDIATRIC CARDIOLOGY 2024年 1-7页

作者： May, Lindsay J. Stehlik, Josef Wilkes, Jacob Ou, Zhining Pinto, Nelangi M. Cabrera, Antonio G. Tristani-Firouzi, Martin Keenan, Heather T. Univ Utah Dept Pediat Salt Lake City UT 84141 USA Univ Utah Sch Med Div Cardiovasc Med Salt Lake City UT USA Intermt Hlth Salt Lake City UT USA Univ Utah Sch Med Dept Internal Med Div Epidemiol Salt Lake City UT USA Univ Washington Dept Pediat Seattle WA USA Ohio State Univ Dept Pediat Coll Med Columbus OH USA

Most epidemiological studies in pediatric heart failure (HF) use administrative database sources, defining patient cohorts by presence of a single HF icd code. However, the ability of icd codes to identify true HF patients is unknown in pediatrics. Here we describe the accuracy of HF icd-10-CM code search algorithms, in identifying pediatric patients with HF from electronic data sources. Based on the adult HF literature, search algorithms were designed to incorporate HF icd codes, imaging, and medications. Sensitivity, specificity, positive and negative predictive value and accuracy of the algorithms were tested among children in an advanced HF clinic ("Clinic cohort"). Top-performing algorithms were then tested in a large-scale regional electronic data warehouse (EDW), 01/2017 to 01/2020, generating the "EDW Cohort". False positive cases were identified and characterized by chart review. Within the Clinic Cohort, 78/378 patients (21%) had gold standard HF diagnoses. A search algorithm with one HF icd coded visit was more sensitive but less specific than > 1 HF icd coded visit, (sensitivity 94% and specificity 89% versus 69% and 97%, respectively). Correspondingly, > 1 icd coded visit had a higher PPV than one icd coded visit;84% vs. 69%. Accuracy was similar (90% vs 91%). Presence of 1 HF icd code combined with HF medication had high sensitivity, specificity, PPV, NPV and accuracy, all higher than the single icd code algorithm. The "1 HF coded visit + any medication" algorithm resulted in highest accuracy (93%). Top-performing algorithms were tested in the EDW: the algorithm with > 1 HF icd coded visit, and the algorithm with one HF icd coded visit combined with HF medication. In the EDW Cohort, 133/248 (53.6%) patients had gold standard HF diagnoses though 115/248 (46.3%) were false positive cases;41% of those had pulmonary over-circulation from congenital heart disease. Excluding children < 30 days old and those with a history of an isolated VSD repair, complete

关键词： Pediatrics Heart failure icd code Epidemiology

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Artificial intelligence to differentiate asthma from COPD in medico-administrative databases

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BMC PULMONARY MEDICINE 2022年第1期22卷 1-9页

作者： Joumaa, Hassan Sigogne, Raphael Maravic, Milka Perray, Lucas Bourdin, Arnaud Roche, Nicolas Cochin Hosp Assistance Publ Hop Paris APHP Dept Resp Med Paris France IQVIA La Defense France Hop Lariboisiere Rhumatol Paris France Univ Montpellier PhyMedExp INSERM U1046 CNRS UMR 9214 Montpellier France Arnaud Villeneuve Hosp CHU Montpellier Dept Resp Med Montpellier France Univ Paris 05 EA2511 Paris France

Introduction Discriminating asthma from chronic obstructive pulmonary disease (COPD) using medico-administrative databases is challenging but necessary for medico-economic analyses focusing on respiratory diseases. Artificial intelligence (AI) may improve dedicated algorithms. Objectives To assess performance of different AI-based approaches to distinguish asthmatics from COPD patients in medico-administrative databases where the clinical diagnosis is absent. An "Asthma COPD Overlap" category was defined to further test whether AI can detect complexity. Methods This study included 178,962 patients treated by two "R03" treatment prescriptions at least from January 2016 to December 2018 and managed by either a general practitioner and/or a pulmonologist participating in a permanent longitudinal observatory of prescription in ambulatory medicine (LPD). Clinical diagnoses are available in this database and were used as gold standards to develop diagnostic rules. Three types of AI approaches were explored using data restricted to demographics and treatment dispensations: multinomial regression, gradient boosting and recurrent neural networks (RNN). The best performing model (based on metric properties) was then applied to estimate the size of asthma and COPD populations based on a database (LRx) of treatment dispensations between July, 2018 and June, 2019. Results The best models were obtained with the boosting approach and RNN, with an overall accuracy of 68%. Performance metrics were better for asthma than COPD. Based on LRx data, the extrapolated numbers of patients treated for asthma and COPD in France were 3.7 and 1.2 million, respectively. Asthma patients were younger than COPD patients (mean, 49.9 vs. 72.1 years);COPD occurred mostly in men (68%) compared to asthma (33%). Conclusion AI can provide models with acceptable accuracy to distinguish between asthma, ACO and COPD in medico-administrative databases where the clinical diagnosis is absent. Deep learning and

关键词： Algorithms COPD Chronic obstructive pulmonary disease Asthma icd code Epidemiology Prevalence Healthcare administrative databases

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