RRN orm: A Novel Framework for C hinese Disease Diagnoses Normalization via LLM -Driven Terminology Component Recognition and Reconstruction (2024)

Abstract: The Clinical Terminology Normalization aims at finding standard terms from a given termbase for mentions extracted from clinical texts. However, we found that extracted mentions suffer from the multi-implication problem, especially disease diagnoses. The reason for this is that physicians often use abbreviations, conjunctions, and juxtapositions when writing diagnoses, and it is difficult to manually decompose. To address this problem, we propose a Terminology Component Recognition and Reconstruction strategy that leverages the reasoning capability of large language models (LLMs) to recognize the components of terms, enabling automated decomposition and transforming original mentions into multiple atomic mentions. Furthermore, we adopt the mainstream “Recall and Rank” framework to apply the benefits of the above strategy to the task flow. By leveraging the LLM incorporating the advanced sampling strategies, we design a sampling algorithm for atomic mentions and train the recall model using contrastive learning. Besides the information about the components is also used as knowledge to guide the final term ranking and selection. The experimental results show that our proposed strategy effectively improves the performance of the terminology normalization task and our proposed approach achieves state-of-the-art on the experimental dataset. We release our code and data on the repository https://github.com/yuugaochyan/RRNorm.

## introduction clinical terminology normalization (ctn) plays an important role in clinical natural language processing @xcite . ctn aims at mapping non-standard clinical mentions such as some fragments extracted from clinical texts, to standard terms within the certain knowledge base @xcite such as the international classification of diseases and related health problems 10-th revision (icd-10), which provides the foundation for downstream tasks in the clinical domain. standardized clinical terms, which are the standard expression of a terminology concept, are applied in tasks such as medical research, data analysis, and healthcare quality improvement that utilize electronic health record information. more specifically, in the chinese medical domain, the ctn task faces the challenge of "multiimplication" @xcite which means that one mention contains multiple meanings. the non-standardized writing habits such as abbreviation, hyphenation, and juxtaposition lead to this issue and cause inconsistent granularity problems, which affects the effectiveness of traditional term normalization methods based on the embedding model. previous work has primarily focused on situations where uni-implication mentions that correspond to only one standard term, are in the majority, such as the chip 2019 task that used the international statistical classification of diseases and related health problems 9-th revision (icd-9) as the knowledge base @xcite . in chip 2019, multi-implication mentions constituted only 4% of the total. their approaches to solving such problems include two-stage methods involving the "recall and re-rank" framework as well as the generative approach. however, previous studies rarely researched the original mentions when facing datasets with the "multi-implication" problem accounting for the majority. multiple meanings can lead to semantic ambiguity when compared to uni-implication terms, and make it difficult to train the model. if the intermediate results after decomposition can be obtained, transforming the original task into multiple normalization tasks with uni-implications, the difficulty of the task will decrease. figure 1 presents the difference between our method and the traditional method. figure 2 representative multi-implication examples of the chinese clinical normalization task from the chip-cdn dataset. case 1 shows the simplest scenario: there are clear separation between different atomic mentions. the multi-implication problem in case 1 can be resolved using traditional word segmentation methods or term recognition methods based on sequence labeling. conversely, the following two cases cannot be segmented or recognized using traditional methods. in case 2, the original mention omitted the repeated occurrence of the affected body part knee. both word segmentation and term recognition result in information loss. the most challenging case, case 3 denotes a semantic implication relation, which cannot be accomplished by direct splitting. to address the defect caused by the multiimplication problem mentioned above, we propose a "terminology component recognition and reconstruction" strategy based on llm, which helps identify the atomic mentions implied within a long chinese mention. this module primarily consists of two main steps, first, we utilized prompt engineering technology to recognize the specific types of components by llm, the components include such as affected body parts and disease content are identified at a finer granularity. then we manually crafted specific rules to restructure the components obtained in the recognition step. the order of components appeared in the original mention and the type of components were taken into consideration during reconstruction. datasets separable inseparable multiimplication train 946 2318 3264 validation 288 741 1029 furthermore, we propose a new framework for terminology normalization based on the terminology component recognition and reconstruction (rr) strategy, named rr-norm. in addition to designing the "rr" module, we apply the gains from "rr" to the "recall and re-rank" process, implementing the "atomic-sampling-based contrastive learning" module and "knowledge-guided term selection" module to finish the ctn task. additionally, we constructed a new dataset "chip-cdn-rr" primarily composed of uniimplication mentions during the implementation of the atomic sampling part, which aligns more closely with traditional term normalization tasks and effectively reduces the complexity of the original task. we demonstrated its effectiveness by applying this dataset to various baseline methods. overall, our contributions are as follows. the most traditional method of clinical terminology normalization is accomplished through retrieval methods, such as bm25, and edit distance. with the development of deep learning, the majority of text similarity approaches are calculated by generating word vectors. @xcite employs a similarity search based on lucene's implementation of levenshtein and n-gram distances. @xcite proposed a linear pairwise model for the representation of medical terms. many of the term normalization studies consider the task of normalizing the terminology to involve a multi-classification problem as well. @xcite introduced the convolutional and recurrent neural network architecture. @xcite presented a multi-task character-level attentional network that learned character structure features. methods based only on retrieval or classification are not very accurate, hence the recall and re-rank method was introduced, which means another model is trained to re-rank the candidate terms obtained by the recall model. @xcite first conducted the bm25 scores as the recall evaluation and proposed a term normalization architecture by fine-tuning the existing bert models. @xcite proposed an architecture consisting of a candidate generator and a list-wise ranker based on bert. @xcite ) provided an abtsbm method for icd-9 terminology standardization. however, it has been found that there are multiimplication issues in the field of chinese healthcare. to solve this problem, @xcite ; @xcite have added several prediction modules to the original normalization framework. among them, using multiple classification methods inevitably encounters long tail problems. @xcite suggested a sequence generative framework to directly generate all the corresponding medical procedure terms. the generative method achieves this task well by avoiding number prediction, but the method tends to be inefficient. @xcite considered introducing a tagging task when predicting the implication number. inspired by this, when considering introducing word segmentation to solve problems, we found that the method based on rule @xcite is difficult to cope with the complexity of chinese medical terminology @xcite . the sequence annotation method @xcite relies on high-quality datasets and cannot address the ellipsis problem of chinese medical terminology. ## methods in this section, we will introduce the proposed framework for chinese terminology normalization based on terminology component recognition and reconstruction strategy, including the "rr" module to pre-process the original mention, the recall module to get candidates, and the selection module to find the appropriate standard terms, shown in figure 3 . ## conclusion in this paper, to address the hindrance caused by the multi-implication problem, we propose the terminology component recognition and reconstruction (rr) strategy based on the llm. furthermore, we propose a competitive terminology standardization framework that uses the benefits of the strategy and achieves state-of-the-art performance. specifically, we design the "mention decomposition" module to leverage an llm to decompose a raw mention into multiple atomic mentions. the "atomic sampling" algorithm for atomic mentions is then designed, combining online negative sampling and llm reasoning to obtain positive and negative samples of atomic mentions, and the recall model is trained using contrastive learning. finally, use the term components as knowledge and use the attention mechanism to train a "knowledge-guided term selection" model. experimentally we verified that the "rr" strategy mitigates the semantic ambiguity defects brought by the multiimplication problem and improves the performance of normalization.