CRF -based recognition of invasive fungal infection concepts in CHIFIR clinical reports (2023)

Abstract: Named entity recognition (NER) in clinical documentation is often hindered by the use of highly specialised terminology, variation in language used to express medical findings and general scarcity of high-quality data available for training. This short paper compares a Conditional Random Fields model to the previously established dictionary-based approach and evaluates its ability to extract information from a small corpus of annotated pathology reports. The results suggest that including token descriptors as well as contextual features significantly improves precision on several concept categories while maintaining the same level of recall.

## introduction invasive fungal infections (ifis) are a significant medical concern, particularly, among immunocompromised individuals. these infections, caused by fungal pathogens that breach the body's primary barriers and infiltrate deeper tissues or disseminate through the bloodstream, can lead to severe morbidity and heightened mortality rates. early detection and appropriate antifungal treatment are paramount, but they may be difficult to identify in clinical populations @xcite . to support ifi surveillance, @xcite sought to establish an automated system to identify markers of ifi in cytology and histopathology reports. the authors introduced a corpus called chifir @xcite , the cytology and histopathology invasive fungal infection reports, to support the development and evaluation of nlp methods for concept recognition of clinical concepts relevant to ifis.they constructed an annotation framework to detect specific terms directly indicative of a confirmed ifi diagnosis. central to their methods was a dictionary-based approach, which relied on exact term matches in texts. however, the dictionary-based approach has several limitations: in contrast, machine learning (ml) algorithms, when compared with dictionary methods, present a promising alternative. these algorithms have the capability to learn the patterns of usage of relevant concepts or entities, based on consideration of the context of words. in this work, we aim to explore the effectiveness of the ml approach by applying conditional random fields @xcite to the chifir dataset and comparing its performance with the original dictionary-based solution. the following sections will delve deeper into the methodology and outline the results of this comparison, highlighting the advantages of crf over the dictionary approach. ## background histopathology reports are structured documents that outline findings from microscopic examination of biopsied tissue. the language used is specialized, often employing a combination of medical terminology, abbreviations, and sometimes subjective descriptions based on the pathologist's observations and interpretations. the complexity and variability of the narrative, which can differ between pathologists and institutions, make standardization difficult. subtle nuances and contextually driven interpretations are pivotal in histopathology, making it challenging for algorithms to consistently interpret and draw accurate conclusions. moreover, the occasional use of ambiguous or equivocal terms to describe uncertain or borderline findings can further complicate machine interpretation. extracting relevant concepts from clinical reports is part of a broader field of information extraction (ie). several rule-based systems and dictionary-based entity recognition tools have been proposed offering more flexibility to combat the inherent variability in language. for instance, @xcite based system with metamap @xcite . while these methods offer reliability and precision, they still lack the ability to make context-specific interpretations. in this paper, we will focus on applying conditional random fields (crfs) @xcite @xcite , which have been successfully applied to the related task of named entity recognition. crfs are a class of statistical modeling methods and are particularly well-suited for sequence labeling tasks. crfs consider the entire sequence, allowing for a more comprehensive contextual understanding. additionally, crfs are capable of ingesting a diverse set of features which can be helpful in dealing with linguistic nuances and inconsistencies across different reports. the model's flexibility enables it to effectively handle ambiguities in clinical narratives. one successful example of crf implementation for biomedical entity recognition is banner @xcite . it is worth noting that the rapid advancements in deep learning have led to the emergence of more sophisticated models, such as lstms and transformer-based architectures. such models can automatically extract features and have demonstrated superior performance across a variety of nlp tasks @xcite . recent literature has suggested that the use of contextualised lexical representations (e.g. in bert @xcite ) as well as the ability to capture long-range dependencies and semantic relationships in text @xcite may be particularly useful in the complex and nuanced domain of histopathology reports. however, such models might not be effective in learning ifispecific terms because of the small and specialised nature of the chifir dataset. ## conclusion in conclusion, we have seen that the crf model performes better and, in particular, is more successful in tackling the lexical diversity and variation present in the chifir corpus than the previous dictionary-based method. although the model performance still suffers from the small sample size and challenging lexical diversity cases, we demonstrated that incorporation of context through the crf-based concept recognition model benefits development of clinical concept recognition tools for this corpus. it would also be worth exploring and comparing this crf-based approach with more advanced machine learning methods, which might be able to learn richer representations from data, and overcome challenges posed by the variability and linguistic nuances in histopathology texts better.