
@inproceedings{huang_plm-icd_2022,
	address = {Seattle, WA},
	title = {{PLM}-{ICD}: {Automatic} {ICD} {Coding} with {Pretrained} {Language} {Models}},
	shorttitle = {{PLM}-{ICD}},
	url = {https://aclanthology.org/2022.clinicalnlp-1.2},
	doi = {10.18653/v1/2022.clinicalnlp-1.2},
	abstract = {Automatically classifying electronic health records (EHRs) into diagnostic codes has been challenging to the NLP community. State-of-the-art methods treated this problem as a multi-label classification problem and proposed various architectures to model this problem. However, these systems did not leverage the superb performance of pretrained language models, which achieved superb performance on natural language understanding tasks. Prior work has shown that pretrained language models underperformed on this task with the regular fine-tuning scheme. Therefore, this paper aims at analyzing the causes of the underperformance and developing a framework for automatic ICD coding with pretrained language models. We spotted three main issues through the experiments: 1) large label space, 2) long input sequences, and 3) domain mismatch between pretraining and fine-tuning. We propose PLM-ICD, a framework that tackles the challenges with various strategies. The experimental results show that our proposed framework can overcome the challenges and achieves state-of-the-art performance in terms of multiple metrics on the benchmark MIMIC data. Our source code is available at https://github.com/MiuLab/PLM-ICD.},
	urldate = {2023-04-24},
	booktitle = {Proceedings of the 4th {Clinical} {Natural} {Language} {Processing} {Workshop}},
	publisher = {Association for Computational Linguistics},
	author = {Huang, Chao-Wei and Tsai, Shang-Chi and Chen, Yun-Nung},
	month = jul,
	year = {2022},
	pages = {10--20},
}

@article{zou_finding_2016,
	series = {Big data analytics and applications},
	title = {Finding the {Best} {Classification} {Threshold} in {Imbalanced} {Classification}},
	volume = {5},
	issn = {2214-5796},
	url = {https://www.sciencedirect.com/science/article/pii/S2214579615000611},
	doi = {10.1016/j.bdr.2015.12.001},
	abstract = {Classification with imbalanced class distributions is a major problem in machine learning. Researchers have given considerable attention to the applications in many real-world scenarios. Although several works have utilized the area under the receiver operating characteristic (ROC) curve to select potentially optimal classifiers in imbalanced classifications, limited studies have been devoted to finding the classification threshold for testing or unknown datasets. In general, the classification threshold is simply set to 0.5, which is usually unsuitable for an imbalanced classification. In this study, we analyze the drawbacks of using ROC as the sole measure of imbalance in data classification problems. In addition, a novel framework for finding the best classification threshold is proposed. Experiments with SCOP v.1.53 data reveal that, with the default threshold set to 0.5, our proposed framework demonstrated a 20.63\% improvement in terms of F-score compared with that of more commonly used methods. The findings suggest that the proposed framework is both effective and efficient. A web server and software tools are available via http://datamining.xmu.edu.cn/prht/ or http://prht.sinaapp.com/.},
	language = {en},
	urldate = {2023-04-18},
	journal = {Big Data Research},
	author = {Zou, Quan and Xie, Sifa and Lin, Ziyu and Wu, Meihong and Ju, Ying},
	month = sep,
	year = {2016},
	keywords = {F-score, Imbalance data, Protein remote homology detection, Receiver Operating Characteristic (ROC)},
	pages = {2--8},
}

@article{zhou_training_2006,
	title = {Training cost-sensitive neural networks with methods addressing the class imbalance problem},
	volume = {18},
	issn = {1558-2191},
	doi = {10.1109/TKDE.2006.17},
	abstract = {This paper studies empirically the effect of sampling and threshold-moving in training cost-sensitive neural networks. Both oversampling and undersampling are considered. These techniques modify the distribution of the training data such that the costs of the examples are conveyed explicitly by the appearances of the examples. Threshold-moving tries to move the output threshold toward inexpensive classes such that examples with higher costs become harder to be misclassified. Moreover, hard-ensemble and soft-ensemble, i.e., the combination of above techniques via hard or soft voting schemes, are also tested. Twenty-one UCl data sets with three types of cost matrices and a real-world cost-sensitive data set are used in the empirical study. The results suggest that cost-sensitive learning with multiclass tasks is more difficult than with two-class tasks, and a higher degree of class imbalance may increase the difficulty. It also reveals that almost all the techniques are effective on two-class tasks, while most are ineffective and even may cause negative effect on multiclass tasks. Overall, threshold-moving and soft-ensemble are relatively good choices in training cost-sensitive neural networks. The empirical study also suggests that some methods that have been believed to be effective in addressing the class imbalance problem may, in fact, only be effective on learning with imbalanced two-class data sets.},
	number = {1},
	journal = {IEEE Transactions on Knowledge and Data Engineering},
	author = {Zhou, Zhi-Hua and Liu, Xu-Ying},
	month = jan,
	year = {2006},
	note = {Conference Name: IEEE Transactions on Knowledge and Data Engineering},
	keywords = {Costs, Data mining, Decision trees, Index Terms- Machine learning, Learning systems, Machine learning, Neural networks, Sampling methods, Testing, Training data, Voting, class imbalance learning, cost-sensitive learning, data mining, ensemble learning., neural networks, sampling, threshold-moving},
	pages = {63--77},
}

@article{ferri_experimental_2009,
	title = {An experimental comparison of performance measures for classification},
	volume = {30},
	issn = {0167-8655},
	url = {https://www.sciencedirect.com/science/article/pii/S0167865508002687},
	doi = {10.1016/j.patrec.2008.08.010},
	abstract = {Performance metrics in classification are fundamental in assessing the quality of learning methods and learned models. However, many different measures have been defined in the literature with the aim of making better choices in general or for a specific application area. Choices made by one metric are claimed to be different from choices made by other metrics. In this work, we analyse experimentally the behaviour of 18 different performance metrics in several scenarios, identifying clusters and relationships between measures. We also perform a sensitivity analysis for all of them in terms of several traits: class threshold choice, separability/ranking quality, calibration performance and sensitivity to changes in prior class distribution. From the definitions and experiments, we make a comprehensive analysis of the relationships between metrics, and a taxonomy and arrangement of them according to the previous traits. This can be useful for choosing the most adequate measure (or set of measures) for a specific application. Additionally, the study also highlights some niches in which new measures might be defined and also shows that some supposedly innovative measures make the same choices (or almost) as existing ones. Finally, this work can also be used as a reference for comparing experimental results in pattern recognition and machine learning literature, when using different measures.},
	language = {en},
	number = {1},
	urldate = {2023-04-18},
	journal = {Pattern Recognition Letters},
	author = {Ferri, C. and Hernández-Orallo, J. and Modroiu, R.},
	month = jan,
	year = {2009},
	keywords = {Calibration, Classification, Performance measures, Ranking},
	pages = {27--38},
}

@article{branco_survey_2016,
	title = {A {Survey} of {Predictive} {Modeling} on {Imbalanced} {Domains}},
	volume = {49},
	issn = {0360-0300},
	url = {https://doi.org/10.1145/2907070},
	doi = {10.1145/2907070},
	abstract = {Many real-world data-mining applications involve obtaining predictive models using datasets with strongly imbalanced distributions of the target variable. Frequently, the least-common values of this target variable are associated with events that are highly relevant for end users (e.g., fraud detection, unusual returns on stock markets, anticipation of catastrophes, etc.). Moreover, the events may have different costs and benefits, which, when associated with the rarity of some of them on the available training data, creates serious problems to predictive modeling techniques. This article presents a survey of existing techniques for handling these important applications of predictive analytics. Although most of the existing work addresses classification tasks (nominal target variables), we also describe methods designed to handle similar problems within regression tasks (numeric target variables). In this survey, we discuss the main challenges raised by imbalanced domains, propose a definition of the problem, describe the main approaches to these tasks, propose a taxonomy of the methods, summarize the conclusions of existing comparative studies as well as some theoretical analyses of some methods, and refer to some related problems within predictive modeling.},
	number = {2},
	urldate = {2023-04-18},
	journal = {ACM Computing Surveys},
	author = {Branco, Paula and Torgo, Luís and Ribeiro, Rita P.},
	month = aug,
	year = {2016},
	keywords = {Imbalanced domains, classification, performance metrics, rare cases, regression},
	pages = {31:1--31:50},
}

@article{lee_biobert_2020,
	title = {{BioBERT}: a pre-trained biomedical language representation model for biomedical text mining},
	volume = {36},
	issn = {1367-4803},
	shorttitle = {{BioBERT}},
	url = {https://doi.org/10.1093/bioinformatics/btz682},
	doi = {10.1093/bioinformatics/btz682},
	abstract = {Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora.We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62\% F1 score improvement), biomedical relation extraction (2.80\% F1 score improvement) and biomedical question answering (12.24\% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts.We make the pre-trained weights of BioBERT freely available at https://github.com/naver/biobert-pretrained, and the source code for fine-tuning BioBERT available at https://github.com/dmis-lab/biobert.},
	number = {4},
	urldate = {2023-04-18},
	journal = {Bioinformatics},
	author = {Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo},
	month = feb,
	year = {2020},
	pages = {1234--1240},
}

@inproceedings{alsentzer_publicly_2019,
	address = {Minneapolis, Minnesota, USA},
	title = {Publicly {Available} {Clinical} {BERT} {Embeddings}},
	url = {https://aclanthology.org/W19-1909},
	doi = {10.18653/v1/W19-1909},
	abstract = {Contextual word embedding models such as ELMo and BERT have dramatically improved performance for many natural language processing (NLP) tasks in recent months. However, these models have been minimally explored on specialty corpora, such as clinical text; moreover, in the clinical domain, no publicly-available pre-trained BERT models yet exist. In this work, we address this need by exploring and releasing BERT models for clinical text: one for generic clinical text and another for discharge summaries specifically. We demonstrate that using a domain-specific model yields performance improvements on 3/5 clinical NLP tasks, establishing a new state-of-the-art on the MedNLI dataset. We find that these domain-specific models are not as performant on 2 clinical de-identification tasks, and argue that this is a natural consequence of the differences between de-identified source text and synthetically non de-identified task text.},
	urldate = {2023-04-18},
	booktitle = {Proceedings of the 2nd {Clinical} {Natural} {Language} {Processing} {Workshop}},
	publisher = {Association for Computational Linguistics},
	author = {Alsentzer, Emily and Murphy, John and Boag, William and Weng, Wei-Hung and Jindi, Di and Naumann, Tristan and McDermott, Matthew},
	month = jun,
	year = {2019},
	pages = {72--78},
}

@article{li_icd_2020,
	title = {{ICD} {Coding} from {Clinical} {Text} {Using} {Multi}-{Filter} {Residual} {Convolutional} {Neural} {Network}},
	volume = {34},
	copyright = {Copyright (c) 2020 Association for the Advancement of Artificial Intelligence},
	issn = {2374-3468},
	url = {https://ojs.aaai.org/index.php/AAAI/article/view/6331},
	doi = {10.1609/aaai.v34i05.6331},
	abstract = {Automated ICD coding, which assigns the International Classification of Disease codes to patient visits, has attracted much research attention since it can save time and labor for billing. The previous state-of-the-art model utilized one convolutional layer to build document representations for predicting ICD codes. However, the lengths and grammar of text fragments, which are closely related to ICD coding, vary a lot in different documents. Therefore, a flat and fixed-length convolutional architecture may not be capable of learning good document representations. In this paper, we proposed a Multi-Filter Residual Convolutional Neural Network (MultiResCNN) for ICD coding. The innovations of our model are two-folds: it utilizes a multi-filter convolutional layer to capture various text patterns with different lengths and a residual convolutional layer to enlarge the receptive field. We evaluated the effectiveness of our model on the widely-used MIMIC dataset. On the full code set of MIMIC-III, our model outperformed the state-of-the-art model in 4 out of 6 evaluation metrics. On the top-50 code set of MIMIC-III and the full code set of MIMIC-II, our model outperformed all the existing and state-of-the-art models in all evaluation metrics. The code is available at https://github.com/foxlf823/Multi-Filter-Residual-Convolutional-Neural-Network.},
	language = {en},
	number = {05},
	urldate = {2023-04-18},
	journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
	author = {Li, Fei and Yu, Hong},
	month = apr,
	year = {2020},
	note = {Number: 05},
	pages = {8180--8187},
}

@inproceedings{luo_fusion_2021,
	address = {Online},
	title = {Fusion: {Towards} {Automated} {ICD} {Coding} via {Feature} {Compression}},
	shorttitle = {Fusion},
	url = {https://aclanthology.org/2021.findings-acl.184},
	doi = {10.18653/v1/2021.findings-acl.184},
	urldate = {2023-04-18},
	booktitle = {Findings of the {Association} for {Computational} {Linguistics}: {ACL}-{IJCNLP} 2021},
	publisher = {Association for Computational Linguistics},
	author = {Luo, Junyu and Xiao, Cao and Glass, Lucas and Sun, Jimeng and Ma, Fenglong},
	month = aug,
	year = {2021},
	pages = {2096--2101},
}

@inproceedings{vu_label_2020,
	title = {A {Label} {Attention} {Model} for {ICD} {Coding} from {Clinical} {Text}},
	volume = {4},
	url = {https://www.ijcai.org/proceedings/2020/461},
	doi = {10.24963/ijcai.2020/461},
	abstract = {Electronic proceedings of IJCAI 2020},
	language = {en},
	urldate = {2023-04-18},
	author = {Vu, Thanh and Nguyen, Dat Quoc and Nguyen, Anthony},
	month = jul,
	year = {2020},
	note = {ISSN: 1045-0823},
	pages = {3335--3341},
}

@article{zeng_automatic_2019,
	series = {Deep {Learning} for {Biological}/{Clinical} {Data}},
	title = {Automatic {ICD}-9 coding via deep transfer learning},
	volume = {324},
	issn = {0925-2312},
	url = {https://www.sciencedirect.com/science/article/pii/S0925231218306246},
	doi = {10.1016/j.neucom.2018.04.081},
	abstract = {ICD-9 codes have been widely used to describe a patient's diagnosis. Accurate automatic ICD-9 coding is important because manual coding is expensive, time-consuming. Inspired by the recent successes of deep transfer learning, in this study, we propose a deep transfer learning framework for automatic ICD-9 coding. Our proposed method makes use of transferring MeSH domain knowledge to improve automatic ICD-9 coding. We demonstrate its effectiveness by achieving state-of-the-art performance with a value of 0.420 for Micro-average F-measure on MIMIC-III dataset, which indicates that our method outperforms hierarchy-based SVM and flat-SVM. Furthermore, we analyze the deep neural network structure to discover the vital elements in the success of our proposed method. Our experimental results indicate that transfer learning is the key component to improve the performance of automatic ICD-9 coding and deep learning approach is the foundation in the success of our proposed model. In addition, to explore the best network architecture, we also compare the performance of multi-scale and sequential network architectures and find that using multi-scale network is better. Finally, we investigate the effects of transferring different percentage of samples on transfer learning and the results show that the best performance of target domain task can be obtained when 100\% number samples are transferred.},
	language = {en},
	urldate = {2023-04-08},
	journal = {Neurocomputing},
	author = {Zeng, Min and Li, Min and Fei, Zhihui and Yu, Ying and Pan, Yi and Wang, Jianxin},
	month = jan,
	year = {2019},
	keywords = {Automatic ICD-9 coding, Deep learning, MeSH, Transfer learning},
	pages = {43--50},
}

@article{teng_explainable_2020,
	title = {Explainable {Prediction} of {Medical} {Codes} {With} {Knowledge} {Graphs}},
	volume = {8},
	issn = {2296-4185},
	url = {https://www.frontiersin.org/articles/10.3389/fbioe.2020.00867},
	abstract = {International Classification of Diseases (ICD) is an authoritative health care classification system of different diseases. It is widely used for disease and health records, assisted medical reimbursement decisions, and collecting morbidity and mortality statistics. The most existing ICD coding models only translate the simple diagnosis descriptions into ICD codes. And it obscures the reasons and details behind specific diagnoses. Besides, the label (code) distribution is uneven. And there is a dependency between labels. Based on the above considerations, the knowledge graph and attention mechanism were expanded into medical code prediction to improve interpretability. In this study, a new method called G\_Coder was presented, which mainly consists of Multi-CNN, graph presentation, attentional matching, and adversarial learning. The medical knowledge graph was constructed by extracting entities related to ICD-9 from freebase. Ontology contains 5 entity classes, which are disease, symptom, medicine, surgery, and examination. The result of G\_Coder on the MIMIC-III dataset showed that the micro-F1 score is 69.2\% surpassing the state of art. The following conclusions can be obtained through the experiment: G\_Coder integrates information across medical records using Multi-CNN and embeds knowledge into ICD codes. Adversarial learning is used to generate the adversarial samples to reconcile the writing styles of doctor. With the knowledge graph and attention mechanism, most relevant segments of medical codes can be explained. This suggests that the knowledge graph significantly improves the precision of code prediction and reduces the working pressure of the human coders.},
	urldate = {2023-04-08},
	journal = {Frontiers in Bioengineering and Biotechnology},
	author = {Teng, Fei and Yang, Wei and Chen, Li and Huang, LuFei and Xu, Qiang},
	year = {2020},
}

@article{yan_survey_2022,
	title = {A survey of automated {International} {Classification} of {Diseases} coding: development, challenges, and applications},
	volume = {2},
	issn = {2667-1026},
	shorttitle = {A survey of automated {International} {Classification} of {Diseases} coding},
	url = {https://www.sciencedirect.com/science/article/pii/S2667102622000092},
	doi = {10.1016/j.imed.2022.03.003},
	abstract = {The International Classification of Diseases (ICD) is an international standard and tool for epidemiological investigation, health management, and clinical diagnosis with a fundamental role in intelligent medical care. The assignment of ICD codes to health-related documents has become a focus of academic research, and numerous studies have developed the process of ICD coding from manual to automated work. In this survey, we review the developmental history of this task in recent decades in depth, from the rules-based stage, through the traditional machine learning stage, to the neural-network-based stage. Various methods have been introduced to solve this problem by using different techniques, and we report a performance comparison of different methods on the publicly available Medical Information Mart for Intensive Care dataset. Next, we summarize four major challenges of this task: (1) the large label space, (2) the unbalanced label distribution, (3) the long text of documents, and (4) the interpretability of coding. Various solutions that have been proposed to solve these problems are analyzed. Further, we discuss the applications of ICD coding, from mortality statistics to payments based on disease-related groups and hospital performance management. In addition, we discuss different ways of considering and evaluating this task, and how it has been transformed into a learnable problem. We also provide details of the commonly used datasets. Overall, this survey aims to provide a reference and possible prospective directions for follow-up research work.},
	language = {en},
	number = {3},
	urldate = {2023-04-08},
	journal = {Intelligent Medicine},
	author = {Yan, Chenwei and Fu, Xiangling and Liu, Xien and Zhang, Yuanqiu and Gao, Yue and Wu, Ji and Li, Qiang},
	month = aug,
	year = {2022},
	keywords = {Clinical coding, Disease classification, Electronic medical record, Health-related document, International Classification of Diseases coding, Medical record management},
	pages = {161--173},
}

@inproceedings{mullenbach_explainable_2018,
	address = {New Orleans, Louisiana},
	title = {Explainable {Prediction} of {Medical} {Codes} from {Clinical} {Text}},
	url = {https://aclanthology.org/N18-1100},
	doi = {10.18653/v1/N18-1100},
	abstract = {Clinical notes are text documents that are created by clinicians for each patient encounter. They are typically accompanied by medical codes, which describe the diagnosis and treatment. Annotating these codes is labor intensive and error prone; furthermore, the connection between the codes and the text is not annotated, obscuring the reasons and details behind specific diagnoses and treatments. We present an attentional convolutional network that predicts medical codes from clinical text. Our method aggregates information across the document using a convolutional neural network, and uses an attention mechanism to select the most relevant segments for each of the thousands of possible codes. The method is accurate, achieving precision@8 of 0.71 and a Micro-F1 of 0.54, which are both better than the prior state of the art. Furthermore, through an interpretability evaluation by a physician, we show that the attention mechanism identifies meaningful explanations for each code assignment.},
	urldate = {2023-04-08},
	booktitle = {Proceedings of the 2018 {Conference} of the {North} {American} {Chapter} of the {Association} for {Computational} {Linguistics}: {Human} {Language} {Technologies}, {Volume} 1 ({Long} {Papers})},
	publisher = {Association for Computational Linguistics},
	author = {Mullenbach, James and Wiegreffe, Sarah and Duke, Jon and Sun, Jimeng and Eisenstein, Jacob},
	month = jun,
	year = {2018},
	pages = {1101--1111},
}

@inproceedings{wang_automatic_2020,
	title = {Automatic {Emergency} {Diagnosis} with {Knowledge}-{Based} {Tree} {Decoding}},
	volume = {4},
	url = {https://www.ijcai.org/proceedings/2020/471},
	doi = {10.24963/ijcai.2020/471},
	abstract = {Electronic proceedings of IJCAI 2020},
	language = {en},
	urldate = {2023-04-08},
	author = {Wang, Ke and Chen, Xuyan and Chen, Ning and Chen, Ting},
	month = jul,
	year = {2020},
	note = {ISSN: 1045-0823},
	pages = {3407--3414},
}

@inproceedings{bai_improving_2019,
	address = {New York, NY, USA},
	series = {{WWW} '19},
	title = {Improving {Medical} {Code} {Prediction} from {Clinical} {Text} via {Incorporating} {Online} {Knowledge} {Sources}},
	isbn = {978-1-4503-6674-8},
	url = {https://dl.acm.org/doi/10.1145/3308558.3313485},
	doi = {10.1145/3308558.3313485},
	abstract = {Clinical notes contain detailed information about health status of patients for each of their encounters with a health system. Developing effective models to automatically assign medical codes to clinical notes has been a long-standing active research area. Despite a great recent progress in medical informatics fueled by deep learning, it is still a challenge to find the specific piece of evidence in a clinical note which justifies a particular medical code out of all possible codes. Considering the large amount of online disease knowledge sources, which contain detailed information about signs and symptoms of different diseases, their risk factors, and epidemiology, there is an opportunity to exploit such sources. In this paper we consider Wikipedia as an external knowledge source and propose Knowledge Source Integration (KSI), a novel end-to-end code assignment framework, which can integrate external knowledge during training of any baseline deep learning model. The main idea of KSI is to calculate matching scores between a clinical note and disease related Wikipedia documents, and combine the scores with output of the baseline model. To evaluate KSI, we experimented with automatic assignment of ICD-9 diagnosis codes to the emergency department clinical notes from MIMIC-III data set, aided by Wikipedia documents corresponding to the ICD-9 codes. We evaluated several baseline models, ranging from logistic regression to recently proposed deep learning models known to achieve the state-of-the-art accuracy on clinical notes. The results show that KSI consistently improves the baseline models and that it is particularly successful in assignment of rare codes. In addition, by analyzing weights of KSI models, we can gain understanding about which words in Wikipedia documents provide useful information for predictions.},
	urldate = {2023-04-08},
	booktitle = {The {World} {Wide} {Web} {Conference}},
	publisher = {Association for Computing Machinery},
	author = {Bai, Tian and Vucetic, Slobodan},
	month = may,
	year = {2019},
	keywords = {Multi-label classification, attention mechanism, document similarity learning, healthcare},
	pages = {72--82},
}

@article{sun_classification_2009,
	title = {Classification of imbalanced data: a review},
	volume = {23},
	issn = {0218-0014},
	shorttitle = {Classification of imbalanced data},
	url = {https://www.worldscientific.com/doi/abs/10.1142/S0218001409007326},
	doi = {10.1142/S0218001409007326},
	abstract = {Classification of data with imbalanced class distribution has encountered a significant drawback of the performance attainable by most standard classifier learning algorithms which assume a relatively balanced class distribution and equal misclassification costs. This paper provides a review of the classification of imbalanced data regarding: the application domains; the nature of the problem; the learning difficulties with standard classifier learning algorithms; the learning objectives and evaluation measures; the reported research solutions; and the class imbalance problem in the presence of multiple classes.},
	number = {04},
	urldate = {2023-04-08},
	journal = {International Journal of Pattern Recognition and Artificial Intelligence},
	author = {Sun, Yanmin and Wong, Andrew K. C. and Kamel, Mohamed S.},
	month = jun,
	year = {2009},
	note = {Publisher: World Scientific Publishing Co.},
	keywords = {Classification, class imbalance problem},
	pages = {687--719},
}

@inproceedings{jeni_facing_2013,
	title = {Facing {Imbalanced} {Data}–{Recommendations} for the {Use} of {Performance} {Metrics}},
	doi = {10.1109/ACII.2013.47},
	abstract = {Recognizing facial action units (AUs) is important for situation analysis and automated video annotation. Previous work has emphasized face tracking and registration and the choice of features classifiers. Relatively neglected is the effect of imbalanced data for action unit detection. While the machine learning community has become aware of the problem of skewed data for training classifiers, little attention has been paid to how skew may bias performance metrics. To address this question, we conducted experiments using both simulated classifiers and three major databases that differ in size, type of FACS coding, and degree of skew. We evaluated influence of skew on both threshold metrics (Accuracy, F-score, Cohen's kappa, and Krippendorf's alpha) and rank metrics (area under the receiver operating characteristic (ROC) curve and precision-recall curve). With exception of area under the ROC curve, all were attenuated by skewed distributions, in many cases, dramatically so. While ROC was unaffected by skew, precision-recall curves suggest that ROC may mask poor performance. Our findings suggest that skew is a critical factor in evaluating performance metrics. To avoid or minimize skew-biased estimates of performance, we recommend reporting skew-normalized scores along with the obtained ones.},
	booktitle = {2013 {Humaine} {Association} {Conference} on {Affective} {Computing} and {Intelligent} {Interaction}},
	author = {Jeni, László A. and Cohn, Jeffrey F. and De La Torre, Fernando},
	month = sep,
	year = {2013},
	note = {ISSN: 2156-8111},
	keywords = {Accuracy, Databases, Gold, Measurement, Pain, Shape, Three-dimensional displays, action unit detection, imbalanced data, performance metrics, skew normalization},
	pages = {245--251},
}

@article{luque_impact_2019,
	title = {The impact of class imbalance in classification performance metrics based on the binary confusion matrix},
	volume = {91},
	issn = {0031-3203},
	url = {https://www.sciencedirect.com/science/article/pii/S0031320319300950},
	doi = {10.1016/j.patcog.2019.02.023},
	abstract = {A major issue in the classification of class imbalanced datasets involves the determination of the most suitable performance metrics to be used. In previous work using several examples, it has been shown that imbalance can exert a major impact on the value and meaning of accuracy and on certain other well-known performance metrics. In this paper, our approach goes beyond simply studying case studies and develops a systematic analysis of this impact by simulating the results obtained using binary classifiers. A set of functions and numerical indicators are attained which enables the comparison of the behaviour of several performance metrics based on the binary confusion matrix when they are faced with imbalanced datasets. Throughout the paper, a new way to measure the imbalance is defined which surpasses the Imbalance Ratio used in previous studies. From the simulation results, several clusters of performance metrics have been identified that involve the use of Geometric Mean or Bookmaker Informedness as the best null-biased metrics if their focus on classification successes (dismissing the errors) presents no limitation for the specific application where they are used. However, if classification errors must also be considered, then the Matthews Correlation Coefficient arises as the best choice. Finally, a set of null-biased multi-perspective Class Balance Metrics is proposed which extends the concept of Class Balance Accuracy to other performance metrics.},
	language = {en},
	urldate = {2023-04-08},
	journal = {Pattern Recognition},
	author = {Luque, Amalia and Carrasco, Alejandro and Martín, Alejandro and de las Heras, Ana},
	month = jul,
	year = {2019},
	keywords = {Class Balance Metrics, Classification, Imbalanced datasets, Performance measures},
	pages = {216--231},
}

@article{johnson_survey_2019,
	title = {Survey on deep learning with class imbalance},
	volume = {6},
	issn = {2196-1115},
	url = {https://doi.org/10.1186/s40537-019-0192-5},
	doi = {10.1186/s40537-019-0192-5},
	abstract = {The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in many real-world applications, e.g., fraud detection and cancer detection. Moreover, highly imbalanced data poses added difficulty, as most learners will exhibit bias towards the majority class, and in extreme cases, may ignore the minority class altogether. Class imbalance has been studied thoroughly over the last two decades using traditional machine learning models, i.e. non-deep learning. Despite recent advances in deep learning, along with its increasing popularity, very little empirical work in the area of deep learning with class imbalance exists. Having achieved record-breaking performance results in several complex domains, investigating the use of deep neural networks for problems containing high levels of class imbalance is of great interest. Available studies regarding class imbalance and deep learning are surveyed in order to better understand the efficacy of deep learning when applied to class imbalanced data. This survey discusses the implementation details and experimental results for each study, and offers additional insight into their strengths and weaknesses. Several areas of focus include: data complexity, architectures tested, performance interpretation, ease of use, big data application, and generalization to other domains. We have found that research in this area is very limited, that most existing work focuses on computer vision tasks with convolutional neural networks, and that the effects of big data are rarely considered. Several traditional methods for class imbalance, e.g. data sampling and cost-sensitive learning, prove to be applicable in deep learning, while more advanced methods that exploit neural network feature learning abilities show promising results. The survey concludes with a discussion that highlights various gaps in deep learning from class imbalanced data for the purpose of guiding future research.},
	number = {1},
	urldate = {2023-04-08},
	journal = {Journal of Big Data},
	author = {Johnson, Justin M. and Khoshgoftaar, Taghi M.},
	month = mar,
	year = {2019},
	keywords = {Big data, Class imbalance, Deep learning, Deep neural networks},
	pages = {27},
}

@inproceedings{rieger_corrloss_2022,
	title = {{CorrLoss}: {Integrating} {Co}-{Occurrence} {Domain} {Knowledge} for {Affect} {Recognition}},
	shorttitle = {{CorrLoss}},
	doi = {10.1109/ICPR56361.2022.9956319},
	abstract = {Neural networks are widely adopted, yet the integration of domain knowledge is still underutilized. We propose to integrate domain knowledge about co-occurring facial movements as a constraint in the loss function to enhance the training of neural networks for affect recognition. As the co-ccurrence patterns tend to be similar across datasets, applying our method can lead to a higher generalizability of models and a lower risk of overfitting. We demonstrate this by showing performance increases in cross-dataset testing for various datasets. We also show the applicability of our method for calibrating neural networks to different facial expressions.},
	booktitle = {2022 26th {International} {Conference} on {Pattern} {Recognition} ({ICPR})},
	author = {Rieger, Ines and Pahl, Jaspar and Finzel, Bettina and Schmid, Ute},
	month = aug,
	year = {2022},
	note = {ISSN: 2831-7475},
	keywords = {Face recognition, Knowledge engineering, Neural networks, Testing, Training},
	pages = {798--804},
}

@misc{liu_roberta_2019,
	title = {{RoBERTa}: {A} {Robustly} {Optimized} {BERT} {Pretraining} {Approach}},
	shorttitle = {{RoBERTa}},
	url = {http://arxiv.org/abs/1907.11692},
	doi = {10.48550/arXiv.1907.11692},
	abstract = {Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code.},
	urldate = {2023-04-08},
	publisher = {arXiv},
	author = {Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Stoyanov, Veselin},
	month = jul,
	year = {2019},
	note = {arXiv:1907.11692 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@article{johnson_mimic-iii_2016,
	title = {{MIMIC}-{III}, a freely accessible critical care database},
	volume = {3},
	copyright = {2016 The Author(s)},
	issn = {2052-4463},
	url = {https://www.nature.com/articles/sdata201635},
	doi = {10.1038/sdata.2016.35},
	abstract = {MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework.},
	language = {en},
	number = {1},
	urldate = {2023-04-08},
	journal = {Scientific Data},
	author = {Johnson, Alistair E. W. and Pollard, Tom J. and Shen, Lu and Lehman, Li-wei H. and Feng, Mengling and Ghassemi, Mohammad and Moody, Benjamin and Szolovits, Peter and Anthony Celi, Leo and Mark, Roger G.},
	month = may,
	year = {2016},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Diagnosis, Health care, Medical research, Outcomes research, Prognosis},
	pages = {160035},
}

@misc{beltagy_longformer_2020,
	title = {Longformer: {The} {Long}-{Document} {Transformer}},
	shorttitle = {Longformer},
	url = {http://arxiv.org/abs/2004.05150},
	doi = {10.48550/arXiv.2004.05150},
	abstract = {Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer. Longformer's attention mechanism is a drop-in replacement for the standard self-attention and combines a local windowed attention with a task motivated global attention. Following prior work on long-sequence transformers, we evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on WikiHop and TriviaQA. We finally introduce the Longformer-Encoder-Decoder (LED), a Longformer variant for supporting long document generative sequence-to-sequence tasks, and demonstrate its effectiveness on the arXiv summarization dataset.},
	urldate = {2023-04-08},
	publisher = {arXiv},
	author = {Beltagy, Iz and Peters, Matthew E. and Cohan, Arman},
	month = dec,
	year = {2020},
	note = {arXiv:2004.05150 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@article{yu_automatic_2019,
	title = {Automatic {ICD} code assignment of {Chinese} clinical notes based on multilayer attention {BiRNN}},
	volume = {91},
	issn = {1532-0464},
	url = {https://www.sciencedirect.com/science/article/pii/S1532046419300322},
	doi = {10.1016/j.jbi.2019.103114},
	abstract = {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.},
	language = {en},
	urldate = {2023-04-08},
	journal = {Journal of Biomedical Informatics},
	author = {Yu, Ying and Li, Min and Liu, Liangliang and Fei, Zhihui and Wu, Fang-Xiang and Wang, Jianxin},
	month = mar,
	year = {2019},
	keywords = {Character-enhanced, Clinical notes, ICD code, Multilayer attention},
	pages = {103114},
}

@article{yan_survey_2022-1,
	title = {A survey of automated {International} {Classification} of {Diseases} coding: development, challenges, and applications},
	volume = {2},
	issn = {2667-1026},
	shorttitle = {A survey of automated {International} {Classification} of {Diseases} coding},
	url = {https://www.sciencedirect.com/science/article/pii/S2667102622000092},
	doi = {10.1016/j.imed.2022.03.003},
	abstract = {The International Classification of Diseases (ICD) is an international standard and tool for epidemiological investigation, health management, and clinical diagnosis with a fundamental role in intelligent medical care. The assignment of ICD codes to health-related documents has become a focus of academic research, and numerous studies have developed the process of ICD coding from manual to automated work. In this survey, we review the developmental history of this task in recent decades in depth, from the rules-based stage, through the traditional machine learning stage, to the neural-network-based stage. Various methods have been introduced to solve this problem by using different techniques, and we report a performance comparison of different methods on the publicly available Medical Information Mart for Intensive Care dataset. Next, we summarize four major challenges of this task: (1) the large label space, (2) the unbalanced label distribution, (3) the long text of documents, and (4) the interpretability of coding. Various solutions that have been proposed to solve these problems are analyzed. Further, we discuss the applications of ICD coding, from mortality statistics to payments based on disease-related groups and hospital performance management. In addition, we discuss different ways of considering and evaluating this task, and how it has been transformed into a learnable problem. We also provide details of the commonly used datasets. Overall, this survey aims to provide a reference and possible prospective directions for follow-up research work.},
	language = {en},
	number = {3},
	urldate = {2023-04-08},
	journal = {Intelligent Medicine},
	author = {Yan, Chenwei and Fu, Xiangling and Liu, Xien and Zhang, Yuanqiu and Gao, Yue and Wu, Ji and Li, Qiang},
	month = aug,
	year = {2022},
	keywords = {Clinical coding, Disease classification, Electronic medical record, Health-related document, International Classification of Diseases coding, Medical record management},
	pages = {161--173},
}

@inproceedings{zhang_bert-xml_2020,
	address = {Online},
	title = {{BERT}-{XML}: {Large} {Scale} {Automated} {ICD} {Coding} {Using} {BERT} {Pretraining}},
	shorttitle = {{BERT}-{XML}},
	url = {https://aclanthology.org/2020.clinicalnlp-1.3},
	doi = {10.18653/v1/2020.clinicalnlp-1.3},
	abstract = {ICD coding is the task of classifying and cod-ing all diagnoses, symptoms and proceduresassociated with a patient's visit. The process isoften manual, extremely time-consuming andexpensive for hospitals as clinical interactionsare usually recorded in free text medical notes.In this paper, we propose a machine learningmodel, BERT-XML, for large scale automatedICD coding of EHR notes, utilizing recentlydeveloped unsupervised pretraining that haveachieved state of the art performance on a va-riety of NLP tasks. We train a BERT modelfrom scratch on EHR notes, learning with vo-cabulary better suited for EHR tasks and thusoutperform off-the-shelf models. We furtheradapt the BERT architecture for ICD codingwith multi-label attention. We demonstratethe effectiveness of BERT-based models on thelarge scale ICD code classification task usingmillions of EHR notes to predict thousands ofunique codes.},
	urldate = {2023-04-08},
	booktitle = {Proceedings of the 3rd {Clinical} {Natural} {Language} {Processing} {Workshop}},
	publisher = {Association for Computational Linguistics},
	author = {Zhang, Zachariah and Liu, Jingshu and Razavian, Narges},
	month = nov,
	year = {2020},
	pages = {24--34},
}

@inproceedings{tsai_leveraging_2019,
	address = {Hong Kong},
	title = {Leveraging {Hierarchical} {Category} {Knowledge} for {Data}-{Imbalanced} {Multi}-{Label} {Diagnostic} {Text} {Understanding}},
	url = {https://aclanthology.org/D19-6206},
	doi = {10.18653/v1/D19-6206},
	abstract = {Clinical notes are essential medical documents to record each patient's symptoms. Each record is typically annotated with medical diagnostic codes, which means diagnosis and treatment. This paper focuses on predicting diagnostic codes given the descriptive present illness in electronic health records by leveraging domain knowledge. We investigate various losses in a convolutional model to utilize hierarchical category knowledge of diagnostic codes in order to allow the model to share semantics across different labels under the same category. The proposed model not only considers the external domain knowledge but also addresses the issue about data imbalance. The MIMIC3 benchmark experiments show that the proposed methods can effectively utilize category knowledge and provide informative cues to improve the performance in terms of the top-ranked diagnostic codes which is better than the prior state-of-the-art. The investigation and discussion express the potential of integrating the domain knowledge in the current machine learning based models and guiding future research directions.},
	urldate = {2023-04-08},
	booktitle = {Proceedings of the {Tenth} {International} {Workshop} on {Health} {Text} {Mining} and {Information} {Analysis} ({LOUHI} 2019)},
	publisher = {Association for Computational Linguistics},
	author = {Tsai, Shang-Chi and Chang, Ting-Yun and Chen, Yun-Nung},
	month = nov,
	year = {2019},
	pages = {39--43},
}

@inproceedings{pascual_towards_2021,
	address = {Online},
	title = {Towards {BERT}-based {Automatic} {ICD} {Coding}: {Limitations} and {Opportunities}},
	shorttitle = {Towards {BERT}-based {Automatic} {ICD} {Coding}},
	url = {https://aclanthology.org/2021.bionlp-1.6},
	doi = {10.18653/v1/2021.bionlp-1.6},
	abstract = {Automatic ICD coding is the task of assigning codes from the International Classification of Diseases (ICD) to medical notes. These codes describe the state of the patient and have multiple applications, e.g., computer-assisted diagnosis or epidemiological studies. ICD coding is a challenging task due to the complexity and length of medical notes. Unlike the general trend in language processing, no transformer model has been reported to reach high performance on this task. Here, we investigate in detail ICD coding using PubMedBERT, a state-of-the-art transformer model for biomedical language understanding. We find that the difficulty of fine-tuning the model on long pieces of text is the main limitation for BERT-based models on ICD coding. We run extensive experiments and show that despite the gap with current state-of-the-art, pretrained transformers can reach competitive performance using relatively small portions of text. We point at better methods to aggregate information from long texts as the main need for improving BERT-based ICD coding.},
	urldate = {2023-04-08},
	booktitle = {Proceedings of the 20th {Workshop} on {Biomedical} {Language} {Processing}},
	publisher = {Association for Computational Linguistics},
	author = {Pascual, Damian and Luck, Sandro and Wattenhofer, Roger},
	month = jun,
	year = {2021},
	pages = {54--63},
}

@inproceedings{devlin_bert_2019,
	address = {Minneapolis, Minnesota},
	title = {{BERT}: {Pre}-training of {Deep} {Bidirectional} {Transformers} for {Language} {Understanding}},
	shorttitle = {{BERT}},
	url = {https://aclanthology.org/N19-1423},
	doi = {10.18653/v1/N19-1423},
	abstract = {We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7\% (4.6\% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).},
	urldate = {2023-04-08},
	booktitle = {Proceedings of the 2019 {Conference} of the {North} {American} {Chapter} of the {Association} for {Computational} {Linguistics}: {Human} {Language} {Technologies}, {Volume} 1 ({Long} and {Short} {Papers})},
	publisher = {Association for Computational Linguistics},
	author = {Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
	month = jun,
	year = {2019},
	pages = {4171--4186},
}

@article{yan_survey_2022-2,
	title = {A survey of automated {International} {Classification} of {Diseases} coding: development, challenges, and applications},
	volume = {2},
	issn = {2667-1026},
	shorttitle = {A survey of automated {International} {Classification} of {Diseases} coding},
	url = {https://www.sciencedirect.com/science/article/pii/S2667102622000092},
	doi = {10.1016/j.imed.2022.03.003},
	abstract = {The International Classification of Diseases (ICD) is an international standard and tool for epidemiological investigation, health management, and clinical diagnosis with a fundamental role in intelligent medical care. The assignment of ICD codes to health-related documents has become a focus of academic research, and numerous studies have developed the process of ICD coding from manual to automated work. In this survey, we review the developmental history of this task in recent decades in depth, from the rules-based stage, through the traditional machine learning stage, to the neural-network-based stage. Various methods have been introduced to solve this problem by using different techniques, and we report a performance comparison of different methods on the publicly available Medical Information Mart for Intensive Care dataset. Next, we summarize four major challenges of this task: (1) the large label space, (2) the unbalanced label distribution, (3) the long text of documents, and (4) the interpretability of coding. Various solutions that have been proposed to solve these problems are analyzed. Further, we discuss the applications of ICD coding, from mortality statistics to payments based on disease-related groups and hospital performance management. In addition, we discuss different ways of considering and evaluating this task, and how it has been transformed into a learnable problem. We also provide details of the commonly used datasets. Overall, this survey aims to provide a reference and possible prospective directions for follow-up research work.},
	language = {en},
	number = {3},
	urldate = {2023-04-08},
	journal = {Intelligent Medicine},
	author = {Yan, Chenwei and Fu, Xiangling and Liu, Xien and Zhang, Yuanqiu and Gao, Yue and Wu, Ji and Li, Qiang},
	month = aug,
	year = {2022},
	keywords = {Clinical coding, Disease classification, Electronic medical record, Health-related document, International Classification of Diseases coding, Medical record management},
	pages = {161--173},
}
