Enhancing Medical Image Retrieval with UMLS-Integrated CNN-Based Text Indexing.

In recent years, Convolutional Neural Network (CNN) models have demonstrated notable advancements in various domains such as image classification and Natural Language Processing (NLP). Despite their success in image classification tasks, their potential impact on medical image retrieval, particularly in text-based medical image retrieval (TBMIR) tasks, has not yet been fully realized. This could be attributed to the complexity of the ranking process, as there is ambiguity in treating TBMIR as an image retrieval task rather than a traditional information retrieval or NLP task. To address this gap, our paper proposes a novel approach to re-ranking medical images using a Deep Matching Model (DMM) and Medical-Dependent Features (MDF). These features incorporate categorical attributes such as medical terminologies and imaging modalities. Specifically, our DMM aims to generate effective representations for query and image metadata using a personalized CNN, facilitating matching between these representations. By using MDF, a semantic similarity matrix based on Unified Medical Language System (UMLS) meta-thesaurus, and a set of personalized filters taking into account some ranking features, our deep matching model can effectively consider the TBMIR task as an image retrieval task, as previously mentioned. To evaluate our approach, we performed experiments on the medical ImageCLEF datasets from 2009 to 2012. The experimental results show that the proposed model significantly enhances image retrieval performance compared to the baseline and state-of-the-art approaches.

Context-Enriched Learning Models for Aligning Biomedical Vocabularies at Scale in the UMLS Metathesaurus.

The Unified Medical Language System (UMLS) Metathesaurus construction process mainly relies on lexical algorithms and manual expert curation for integrating over 200 biomedical vocabularies. A lexical-based learning model (LexLM) was developed to predict synonymy among Metathesaurus terms and largely outperforms a rule-based approach (RBA) that approximates the current construction process. However, the LexLM has the potential for being improved further because it only uses lexical information from the source vocabularies, while the RBA also takes advantage of contextual information. We investigate the role of multiple types of contextual information available to the UMLS editors, namely source synonymy (SS), source semantic group (SG), and source hierarchical relations (HR), for the UMLS vocabulary alignment (UVA) problem. In this paper, we develop multiple variants of context-enriched learning models (ConLMs) by adding to the LexLM the types of contextual information listed above. We represent these context types in context-enriched knowledge graphs (ConKGs) with four variants ConSS, ConSG, ConHR, and ConAll. We train these ConKG embeddings using seven KG embedding techniques. We create the ConLMs by concatenating the ConKG embedding vectors with the word embedding vectors from the LexLM. We evaluate the performance of the ConLMs using the UVA generalization test datasets with hundreds of millions of pairs. Our extensive experiments show a significant performance improvement from the ConLMs over the LexLM, namely +5.0% in precision (93.75%), +0.69% in recall (93.23%), +2.88% in F1 (93.49%) for the best ConLM. Our experiments also show that the ConAll variant including the three context types takes more time, but does not always perform better than other variants with a single context type. Finally, our experiments show that the pairs of terms with high lexical similarity benefit most from adding contextual information, namely +6.56% in precision (94.97%), +2.13% in recall (93.23%), +4.35% in F1 (94.09%) for the best ConLM. The pairs with lower degrees of lexical similarity also show performance improvement with +0.85% in F1 (96%) for low similarity and +1.31% in F1 (96.34%) for no similarity. These results demonstrate the importance of using contextual information in the UVA problem.

Biomedical Vocabulary Alignment at Scale in the UMLS Metathesaurus.

With 214 source vocabularies, the construction and maintenance process of the UMLS (Unified Medical Language System) Metathesaurus terminology integration system is costly, time-consuming, and error-prone as it primarily relies on (1) lexical and semantic processing for suggesting groupings of synonymous terms, and (2) the expertise of UMLS editors for curating these synonymy predictions. This paper aims to improve the UMLS Metathesaurus construction process by developing a novel supervised learning approach for improving the task of suggesting synonymous pairs that can scale to the size and diversity of the UMLS source vocabularies. We evaluate this deep learning (DL) approach against a rule-based approach (RBA) that approximates the current UMLS Metathesaurus construction process. The key to the generalizability of our approach is the use of various degrees of lexical similarity in negative pairs during the training process. Our initial experiments demonstrate the strong performance across multiple datasets of our DL approach in terms of recall (91-92%), precision (88-99%), and F1 score (89-95%). Our DL approach largely outperforms the RBA method in recall (+23%), precision (+2.4%), and F1 score (+14.1%). This novel approach has great potential for improving the UMLS Metathesaurus construction process by providing better synonymy suggestions to the UMLS editors.

A Neuro-ontology for the neurological examination.

BACKGROUND: The use of clinical data in electronic health records for machine-learning or data analytics depends on the conversion of free text into machine-readable codes. We have examined the feasibility of capturing the neurological examination as machine-readable codes based on UMLS Metathesaurus concepts. METHODS: We created a target ontology for capturing the neurological examination using 1100 concepts from the UMLS Metathesaurus. We created a dataset of 2386 test-phrases based on 419 published neurological cases. We then mapped the test-phrases to the target ontology. RESULTS: We were able to map all of the 2386 test-phrases to 601 unique UMLS concepts. A neurological examination ontology with 1100 concepts has sufficient breadth and depth of coverage to encode all of the neurologic concepts derived from the 419 test cases. Using only pre-coordinated concepts, component ontologies of the UMLS, such as HPO, SNOMED CT, and OMIM, do not have adequate depth and breadth of coverage to encode the complexity of the neurological examination. CONCLUSION: An ontology based on a subset of UMLS has sufficient breadth and depth of coverage to convert deficits from the neurological examination into machine-readable codes using pre-coordinated concepts. The use of a small subset of UMLS concepts for a neurological examination ontology offers the advantage of improved manageability as well as the opportunity to curate the hierarchy and subsumption relationships.

Siamese KG-LSTM: A deep learning model for enriching UMLS Metathesaurus synonymy.

The Unified Medical Language System, or UMLS, is a repository of medical terminology developed by the U.S. National Library of Medicine for improving the computer system's ability of understanding the biomedical and health languages. The UMLS Metathesaurus is one of the three UMLS knowledge sources, containing medical terms and their relationships. Due to the rapid increase in the number of medical terms recently, the current construction of UMLS Metathesaurus, which heavily depends on lexical tools and human editors, is error-prone and time-consuming. This paper takes advantages of the emerging deep learning models for learning to predict the synonyms and non-synonyms between the pairs of biomedical terms in the Metathesaurus. Our learning approach focuses a subset of specific terms instead of the whole Metathesaurus corpus. Particularly, we train the models with biomedical terms from the Disorders semantic group. To strengthen the models, we enrich the inputs with different strategies, including synonyms and hierarchical relationships from source vocabularies. Our deep learning model adopts the Siamese KG-LSTM (Siamese Knowledge Graph - Long Short-Term Memory) in the architecture. The experimental results show that this approach yields excellent performance when handling the task of synonym detection for Disorders semantic group in the Metathesaurus. This shows the potential of applying machine learning techniques in the UMLS Metathesaurus construction process. Although the work in this paper focuses only on specific semantic group of Disorders, we believe that the proposed method can be applied to other semantic groups in the UMLS Metathesaurus.

RysannMD: A biomedical semantic annotator balancing speed and accuracy.

Recently, both researchers and practitioners have explored the possibility of semantically annotating large and continuously evolving collections of biomedical texts such as research papers, medical reports, and physician notes in order to enable their efficient and effective management and use in clinical practice or research laboratories. Such annotations can be automatically generated by biomedical semantic annotators - tools that are specifically designed for detecting and disambiguating biomedical concepts mentioned in text. The biomedical community has already presented several solid automated semantic annotators. However, the existing tools are either strong in their disambiguation capacity, i.e., the ability to identify the correct biomedical concept for a given piece of text among several candidate concepts, or they excel in their processing time, i.e., work very efficiently, but none of the semantic annotation tools reported in the literature has both of these qualities. In this paper, we present RysannMD (Ryerson Semantic Annotator for Medical Domain), a biomedical semantic annotation tool that strikes a balance between processing time and performance while disambiguating biomedical terms. In other words, RysannMD provides reasonable disambiguation performance when choosing the right sense for a biomedical term in a given context, and does that in a reasonable time. To examine how RysannMD stands with respect to the state of the art biomedical semantic annotators, we have conducted a series of experiments using standard benchmarking corpora, including both gold and silver standards, and four modern biomedical semantic annotators, namely cTAKES, MetaMap, NOBLE Coder, and Neji. The annotators were compared with respect to the quality of the produced annotations measured against gold and silver standards using precision, recall, and F1 measure and speed, i.e., processing time. In the experiments, RysannMD achieved the best median F1 measure across the benchmarking corpora, independent of the standard used (silver/gold), biomedical subdomain, and document size. In terms of the annotation speed, RysannMD scored the second best median processing time across all the experiments. The obtained results indicate that RysannMD offers the best performance among the examined semantic annotators when both quality of annotation and speed are considered simultaneously.

Exploiting the UMLS Metathesaurus for extracting and categorizing concepts representing signs and symptoms to anatomically related organ systems.

OBJECTIVE: To develop a method to exploit the UMLS Metathesaurus for extracting and categorizing concepts found in clinical text representing signs and symptoms to anatomically related organ systems. The overarching goal is to classify patient reported symptoms to organ systems for population health and epidemiological analyses. MATERIALS AND METHODS: Using the concepts' semantic types and the inter-concept relationships as guidance, a selective portion of the concepts within the UMLS Metathesaurus was traversed starting from the concepts representing the highest level organ systems. The traversed concepts were chosen, filtered, and reviewed to obtain the concepts representing clinical signs and symptoms by blocking deviations, pruning superfluous concepts, and manual review. The mapping process was applied to signs and symptoms annotated in a corpus of 750 clinical notes. RESULTS: The mapping process yielded a total of 91,000 UMLS concepts (with approximately 300,000 descriptions) possibly representing physical and mental signs and symptoms that were extracted and categorized to the anatomically related organ systems. Of 1864 distinct descriptions of signs and symptoms found in the 750 document corpus, 1635 of these (88%) were successfully mapped to the set of concepts extracted from the UMLS. Of 668 unique concepts mapped, 603 (90%) were correctly categorized to their organ systems. CONCLUSION: We present a process that facilitates mapping of signs and symptoms to their organ systems. By providing a smaller set of UMLS concepts to use for comparing and matching patient records, this method has the potential to increase efficiency of information extraction pipelines.