Annotated and Normalized Causal Relation Extraction Corpus for Improving Health Informatics (2023)

Abstract: In the ever-expanding landscape of biomedical research, development of new cancer drugs has increased the likelihood of adverse drug reactions (ADRs). However, information about these ADRs is often buried in unstructured data, requiring the conversion of this data into a structured and labeled dataset to identify potential ADRs and associations between them, making the extraction of entities and the analysis of causal relations a pivotal task. Machine learning methods have been used to identify ADRs, but current literature has several gaps in coverage, superficial manual annotation, and a lack of a labeled ADR corpus specific to cancer and normalized entities. Current datasets are generated manually on the abstracts, limiting their scope. To address these limitations, the paper presents an algorithm that automatically constructs, annotates, normalizes entities specific to cancer and identifies causal relationships among entities using linguistics and grammatical properties, MetaMap and UMLS tools enabling efficient information retrieval. A further knowledge graph was created for a case report to visualize the causal relationships.

## introduction in the dynamic landscape of healthcare, adverse drug reactions continue to be a challenge especially given the increasing complexity of therapeutics, terminologies, and the growth of unstructured medical literature. significant amount of medical literature encoded in natural language, is available in the public domain through pubmed, including biomedical literature, discharge summaries, clinical trials, health reviews, electronic health records, and others. however, the main obstacle is the lack of tools and the difficulty in parsing and extracting relevant and useful information related to adr from the available data due to its unstructured and ambiguous nature which impedes the creation of a tools and approaches for creating a structured, unambiguous, annotated dataset for machine learning algorithms for entity and causal relation extraction. some of the available datasets for disease are ncbi @xcite and raredis @xcite , while mimic-ii @xcite , nyph @xcite , and ge her @xcite datasets consist of clinical observations and discharge summaries. the stockholm epr @xcite corpus and omop @xcite provide drug exposures and patient data. to the best of our knowledge, no automatically annotated dataset contains entities specific to cancer drug and their side effects. the proposed dataset has been developed and annotated automatically and contains four entities: disease, dysn, side effects, and drug along with entity cui, normalized entity, and entity type. in succession, causal relationships were elicited among these entities. this step is essential for constructing structured knowledge repositories, and enabling efficient information retrieval. (figure 1 ) shows the workflow of proposed method. section 2 elucidates the proposed methodology and algorithm. section 3 expounds the results and section 4 evinces the conclusion and future scope. ## proposed work this study presents the algorithm proposed for the automatic construction of a labeled semantic adr dataset specifically to detect adverse drug reactions during treatment of cancer and its types. we ob-served that most of the datasets available for adr prediction are based on the abstracts of pubmed articles, a part of which are unlabeled and unstructured and requires expert knowledge, which limits their coverage of entities and makes it difficult to extract latent information. these limitations were addressed by the proposed algorithm (figure 2 ) discussed briefly in this section. defining entities of interest lays the foundation as it define the scope of the dataset. for our corpus, four types of entities were defined ( proposed algorithm can be divided into four parts where first part find phrases in every sentence by calling 'identify_phrase()' function. now for each phrase, in second part, all possible variants are generated and best variant 'v' based on the threshold mechanism is selected by calling the 'find_variants()' function. in third part, annotation and normalization is performed by calling the 'annotate_and_normalize()' function. after annotation, relations are extracted in the form of triplets by calling 'identify_relation()' function followed by assigning cui and property to the entity in fourth part of algorithm. entities of interest label cancer types neop drugs drug adverse drug reaction adr dysn dysn ## result and analysis this paper contributes to medical society by meeting the challenges and research gaps identified in the literature with the aid of natural language processing and providing the algorithm for the automatic construction of semantic, labeled, and normalized dataset by extracting entities and their relations in the form of triplets. further, as a final point, a knowledge graph was created. proposed approach for entity extraction was compared with the three state of art approaches: hunflair, scispacy and pubtator. the proposed dataset can be utilized for model training for adr prediction specific to cancer. further knowledge graph was constructed which can assist healthcare professionals in making informed decision and identifying relevant treatments. in future, this corpus can be enlarged by adding more case reports. more entity types can be extracted and corpus can be used for training predictive models.