Improving Medical Code Prediction from Clinical Text via Incorporating Online Knowledge Sources

Bai, Tian; Vučetić, Slobodan

doi:10.1145/3308558.3313485

Cited by 36 publications

(31 citation statements)

References 20 publications

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“…The document similarity measures are used to find the similarity between healthcare documents. For example, to detect medical codes of the documents the authors have used an attention mechanism which targets the most informative parts of the documents [33]. In another research, Jaccard distance measure was used to compute the similarity between medical documents using a Non-negative matrix factorization algorithm [34].…”

Section: B Document Similarity In Healthcarementioning

confidence: 99%

Healthcare Misinformation Detection and Fact-Checking: A Novel Approach

Barve¹,

Saini²

2021

IJACSA

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Section: B Document Similarity In Healthcarementioning

confidence: 99%

Healthcare Misinformation Detection and Fact-Checking: A Novel Approach

Barve¹,

Saini²

2021

IJACSA

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“…CAML (Mullenbach et al, 2018) used CNN with multiple filters and label attention. adopted the doc2vec embedding and CNN architecture, and Bai and Vucetic (2019) incorporated online knowledge sources. The recent MultiResCNN model (Li and Yu, 2020) extensively concatenated and stacked CNNs with multi-filter convolution and residual learning.…”

Section: Related Workmentioning

confidence: 99%

Medical Code Assignment with Gated Convolution and Note-Code Interaction

Ji¹,

Pan²,

Marttinen³

2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Medical code assignment from clinical text is a fundamental task in clinical information system management. As medical notes are typically lengthy and the medical coding system's code space is large, this task is a longstanding challenge. Recent work applies deep neural network models to encode the medical notes and assign medical codes to clinical documents. However, these methods are still ineffective as they do not fully encode and capture the lengthy and rich semantic information of medical notes nor explicitly exploit the interactions between the notes and codes. We propose a novel method, gated convolutional neural networks, and a note-code interaction (GatedCNN-NCI), for automatic medical code assignment to overcome these challenges. Our methods capture the rich semantic information of the lengthy clinical text for better representation by utilizing embedding injection and gated information propagation in the medical note encoding module. With a novel note-code interaction design and a graph message passing mechanism, we explicitly capture the underlying dependency between notes and codes, enabling effective code prediction. A weight sharing scheme is further designed to decrease the number of trainable parameters. Empirical experiments on real-world clinical datasets show that our proposed model outperforms state-of-the-art models in most cases, and our model size is on par with light-weighted baselines.

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“…To ensure that other experimental results can be reproduced, we perform experiments using the same dataset and the same splitting method. In the previous study [32], the dataset was divided into a training set, a validation set, and a test set of 0.7, 0.1, and 0.2, respectively. In our experiment, we keep the same splitting of dataset and compared with the previous studies.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…The results are shown in Table 4. In [32], Bai et al presented a model that incorporates the external resources of Wikipedia knowledge framework (KSI) into the model to predict ICD codes and implements the optimal performance on the MIMIC dataset. In [16], Mullenbach et al presented Compared to other deep neural network-based approaches, our method achieves the highest Micro F1 and Macro F1 scores of 67.5% and 26.5% for the MIMIC-III dataset.…”

Section: Comparison With Related Workmentioning

confidence: 99%

Medical code prediction via capsule networks and ICD knowledge

Bao

Lin

Zhang

et al. 2021

BMC Med Inform Decis Mak

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Background Clinical notes record the health status, clinical manifestations and other detailed information of each patient. The International Classification of Diseases (ICD) codes are important labels for electronic health records. Automatic medical codes assignment to clinical notes through the deep learning model can not only improve work efficiency and accelerate the development of medical informatization but also facilitate the resolution of many issues related to medical insurance. Recently, neural network-based methods have been proposed for the automatic medical code assignment. However, in the medical field, clinical notes are usually long documents and contain many complex sentences, most of the current methods cannot effective in learning the representation of potential features from document text. Methods In this paper, we propose a hybrid capsule network model. Specifically, we use bi-directional LSTM (Bi-LSTM) with forwarding and backward directions to merge the information from both sides of the sequence. The label embedding framework embeds the text and labels together to leverage the label information. We then use a dynamic routing algorithm in the capsule network to extract valuable features for medical code prediction task. Results We applied our model to the task of automatic medical codes assignment to clinical notes and conducted a series of experiments based on MIMIC-III data. The experimental results show that our method achieves a micro F1-score of 67.5% on MIMIC-III dataset, which outperforms the other state-of-the-art methods. Conclusions The proposed model employed the dynamic routing algorithm and label embedding framework can effectively capture the important features across sentences. Both Capsule networks and domain knowledge are helpful for medical code prediction task.

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Improving Medical Code Prediction from Clinical Text via Incorporating Online Knowledge Sources

Cited by 36 publications

References 20 publications

Healthcare Misinformation Detection and Fact-Checking: A Novel Approach

Healthcare Misinformation Detection and Fact-Checking: A Novel Approach

Medical Code Assignment with Gated Convolution and Note-Code Interaction

Medical code prediction via capsule networks and ICD knowledge

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