Complementary and Integrative Health Information in the literature: its lexicon and named entity recognition.

OBJECTIVE: To construct an exhaustive Complementary and Integrative Health (CIH) Lexicon (CIHLex) to help better represent the often underrepresented physical and psychological CIH approaches in standard terminologies, and to also apply state-of-the-art natural language processing (NLP) techniques to help recognize them in the biomedical literature. MATERIALS AND METHODS: We constructed the CIHLex by integrating various resources, compiling and integrating data from biomedical literature and relevant sources of knowledge. The Lexicon encompasses 724 unique concepts with 885 corresponding unique terms. We matched these concepts to the Unified Medical Language System (UMLS), and we developed and utilized BERT models comparing their efficiency in CIH named entity recognition to well-established models including MetaMap and CLAMP, as well as the large language model GPT3.5-turbo. RESULTS: Of the 724 unique concepts in CIHLex, 27.2% could be matched to at least one term in the UMLS. About 74.9% of the mapped UMLS Concept Unique Identifiers were categorized as "Therapeutic or Preventive Procedure." Among the models applied to CIH named entity recognition, BLUEBERT delivered the highest macro-average F1-score of 0.91, surpassing other models. CONCLUSION: Our CIHLex significantly augments representation of CIH approaches in biomedical literature. Demonstrating the utility of advanced NLP models, BERT notably excelled in CIH entity recognition. These results highlight promising strategies for enhancing standardization and recognition of CIH terminology in biomedical contexts.

Automated Knowledge Modeling for Cancer Clinical Practice Guidelines.

Clinical Practice Guidelines (CPGs) for cancer diseases evolve rapidly due to new evidence generated by active research. Currently, CPGs are primarily published in a document format that is ill-suited for managing this developing knowledge. A knowledge model of the guidelines document suitable for programmatic interaction is required. This work proposes an automated method for extraction of knowledge from National Comprehensive Cancer Network (NCCN) CPGs in Oncology and generating a structured model containing the retrieved knowledge. The proposed method was tested using two versions of NCCN Non-Small Cell Lung Cancer (NSCLC) CPG to demonstrate the effectiveness in faithful extraction and modeling of knowledge. Three enrichment strategies using Cancer staging information, Unified Medical Language System (UMLS) Metathesaurus & National Cancer Institute thesaurus (NCIt) concepts, and Node classification are also presented to enhance the model towards enabling programmatic traversal and querying of cancer care guidelines. The Node classification was performed using a Support Vector Machine (SVM) model, achieving a classification accuracy of 0.81 with 10-fold cross-validation.

Discovering novel drug-supplement interactions using SuppKG generated from the biomedical literature.

OBJECTIVE: Develop a novel methodology to create a comprehensive knowledge graph (SuppKG) to represent a domain with limited coverage in the Unified Medical Language System (UMLS), specifically dietary supplement (DS) information for discovering drug-supplement interactions (DSI), by leveraging biomedical natural language processing (NLP) technologies and a DS domain terminology. MATERIALS AND METHODS: We created SemRepDS (an extension of an NLP tool, SemRep), capable of extracting semantic relations from abstracts by leveraging a DS-specific terminology (iDISK) containing 28,884 DS terms not found in the UMLS. PubMed abstracts were processed using SemRepDS to generate semantic relations, which were then filtered using a PubMedBERT model to remove incorrect relations before generating SuppKG. Two discovery pathways were applied to SuppKG to identify potential DSIs, which are then compared with an existing DSI database and also evaluated by medical professionals for mechanistic plausibility. RESULTS: SemRepDS returned 158.5% more DS entities and 206.9% more DS relations than SemRep. The fine-tuned PubMedBERT model (significantly outperformed other machine learning and BERT models) obtained an F1 score of 0.8605 and removed 43.86% of semantic relations, improving the precision of the relations by 26.4% over pre-filtering. SuppKG consists of 56,635 nodes and 595,222 directed edges with 2,928 DS-specific nodes and 164,738 edges. Manual review of findings identified 182 of 250 (72.8%) proposed DS-Gene-Drug and 77 of 100 (77%) proposed DS-Gene1-Function-Gene2-Drug pathways to be mechanistically plausible. DISCUSSION: With added DS terminology to the UMLS, SemRepDS has the capability to find more DS-specific semantic relationships from PubMed than SemRep. The utility of the resulting SuppKG was demonstrated using discovery patterns to find novel DSIs. CONCLUSION: For the domain with limited coverage in the traditional terminology (e.g., UMLS), we demonstrated an approach to leverage domain terminology and improve existing NLP tools to generate a more comprehensive knowledge graph for the downstream task. Even this study focuses on DSI, the method may be adapted to other domains.

Assessing the enrichment of dietary supplement coverage in the Unified Medical Language System.

OBJECTIVE: We sought to assess the need for additional coverage of dietary supplements (DS) in the Unified Medical Language System (UMLS) by investigating (1) the overlap between the integrated DIetary Supplements Knowledge base (iDISK) DS ingredient terminology and the UMLS and (2) the coverage of iDISK and the UMLS over DS mentions in the biomedical literature. MATERIALS AND METHODS: We estimated the overlap between iDISK and the UMLS by mapping iDISK to the UMLS using exact and normalized strings. The coverage of iDISK and the UMLS over DS mentions in the biomedical literature was evaluated via a DS named-entity recognition (NER) task within PubMed abstracts. RESULTS: The coverage analysis revealed that only 30% of iDISK terms can be matched to the UMLS, although these cover over 99% of iDISK concepts. A manual review revealed that a majority of the unmatched terms represented new synonyms, rather than lexical variants. For NER, iDISK nearly doubles the precision and achieves a higher F1 score than the UMLS, while maintaining a competitive recall. DISCUSSION: While iDISK has significant concept overlap with the UMLS, it contains many novel synonyms. Furthermore, almost 3000 of these overlapping UMLS concepts are missing a DS designation, which could be provided by iDISK. The NER experiments show that the specialization of iDISK is useful for identifying DS mentions. CONCLUSIONS: Our results show that the DS representation in the UMLS could be enriched by adding DS designations to many concepts and by adding new synonyms.

iDISK: the integrated DIetary Supplements Knowledge base.

OBJECTIVE: To build a knowledge base of dietary supplement (DS) information, called the integrated DIetary Supplement Knowledge base (iDISK), which integrates and standardizes DS-related information from 4 existing resources. MATERIALS AND METHODS: iDISK was built through an iterative process comprising 3 phases: 1) establishment of the content scope, 2) development of the data model, and 3) integration of existing resources. Four well-regarded DS resources were integrated into iDISK: The Natural Medicines Comprehensive Database, the "About Herbs" page on the Memorial Sloan Kettering Cancer Center website, the Dietary Supplement Label Database, and the Natural Health Products Database. We evaluated the iDISK build process by manually checking that the data elements associated with 50 randomly selected ingredients were correctly extracted and integrated from their respective sources. RESULTS: iDISK encompasses a terminology of 4208 DS ingredient concepts, which are linked via 6 relationship types to 495 drugs, 776 diseases, 985 symptoms, 605 therapeutic classes, 17 system organ classes, and 137 568 DS products. iDISK also contains 7 concept attribute types and 3 relationship attribute types. Evaluation of the data extraction and integration process showed average errors of 0.3%, 2.6%, and 0.4% for concepts, relationships and attributes, respectively. CONCLUSION: We developed iDISK, a publicly available standardized DS knowledge base that can facilitate more efficient and meaningful dissemination of DS knowledge.

Personalized Guideline-Based Treatment Recommendations Using Natural Language Processing Techniques.

Clinical guidelines and clinical pathways are accepted and proven instruments for quality assurance and process optimization. Today, electronic representation of clinical guidelines exists as unstructured text, but is not well-integrated with patient-specific information from electronic health records. Consequently, generic content of the clinical guidelines is accessible, but it is not possible to visualize the position of the patient on the clinical pathway, decision support cannot be provided by personalized guidelines for the next treatment step. The Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) provides common reference terminology as well as the semantic link for combining the pathways and the patient-specific information. This paper proposes a model-based approach to support the development of guideline-compliant pathways combined with patient-specific structured and unstructured information using SNOMED CT. To identify SNOMED CT concepts, a software was developed to extract SNOMED CT codes out of structured and unstructured German data to map these with clinical pathways annotated in accordance with the systematized nomenclature.

Term Coverage of Dietary Supplements Ingredients in Product Labels.

As the clinical application and consumption of dietary supplements has grown, their side effects and possible interactions with prescribed medications has become a serious issue. Information extraction of dietary supplement related information is a critical need to support dietary supplement research. However, there currently is not an existing terminology for dietary supplements, placing a barrier for informatics research in this field. The terms related to dietary supplement ingredients should be collected and normalized before a terminology can be established to facilitate convenient search on safety information and control possible adverse effects of dietary supplements. In this study, the Dietary Supplement Label Database (DSLD) was chosen as the data source from which the ingredient information was extracted and normalized. The distribution based on the product type and the ingredient type of the dietary supplements were analyzed. The ingredient terms were then mapped to the existing terminologies, including UMLS, RxNorm and NDF-RT by using MetaMap and RxMix. The large gap between existing terminologies and ingredients were found: only 14.67%, 19.65%, and 12.88% of ingredient terms were covered by UMLS, RxNorm and NDF-RT, respectively.

Evaluation of Herbal and Dietary Supplement Resource Term Coverage.

The use of Complementary and Alternative Medicine (CAM) is increasingly popular in places like North America and Europe where western medicine is primarily practiced. People are consuming herbal and dietary supplements along with western medications simultaneously. Sometimes, supplements and drugs react with one another via antagonistic or potentiation actions of the drug or supplement resulting in an adverse event. Unfortunately, it is not easy to study drug-supplement interactions without a standard terminology to describe herbal and dietary supplements. This pilot study investigated coverage of supplement databases to one another as well as coverage by the Unified Medical Language System (UMLS) and RxNorm for supplement terms. We found that none of the supplement databases completely covers supplement terms. UMLS, MeSH, SNOMED CT, RxNorm and NDF-RT cover 54%, 40%, 32%, 22% and 14% of supplement concepts, respectively. NDF-RT provides some value for grouping supplements into drug classes. Enhancing our understanding of the gap between the traditional biomedical terminology systems and supplement terms could lead to the development of a comprehensive terminology resources for supplements, and other secondary uses such as better detection and extraction of drug-supplement interactions.

Neurolinguistic approach to natural language processing with applications to medical text analysis.

Understanding written or spoken language presumably involves spreading neural activation in the brain. This process may be approximated by spreading activation in semantic networks, providing enhanced representations that involve concepts not found directly in the text. The approximation of this process is of great practical and theoretical interest. Although activations of neural circuits involved in representation of words rapidly change in time snapshots of these activations spreading through associative networks may be captured in a vector model. Concepts of similar type activate larger clusters of neurons, priming areas in the left and right hemisphere. Analysis of recent brain imaging experiments shows the importance of the right hemisphere non-verbal clusterization. Medical ontologies enable development of a large-scale practical algorithm to re-create pathways of spreading neural activations. First concepts of specific semantic type are identified in the text, and then all related concepts of the same type are added to the text, providing expanded representations. To avoid rapid growth of the extended feature space after each step only the most useful features that increase document clusterization are retained. Short hospital discharge summaries are used to illustrate how this process works on a real, very noisy data. Expanded texts show significantly improved clustering and may be classified with much higher accuracy. Although better approximations to the spreading of neural activations may be devised a practical approach presented in this paper helps to discover pathways used by the brain to process specific concepts, and may be used in large-scale applications.

Developing a web site to support the implementation of medical home.

The MedHome Portal is a web site aimed at providing information and resources to primary care physicians and families to improve care of children with special health care needs and support the Medical Home model of comprehensive care. Its development is a collaborative effort among a University's Department of Pediatrics and its Health Sciences Library, the state Department of Health, a family advocacy organization, and others. The Portal's development to date, unique features, results in terms of content and utilization, lessons learned, and future directions are detailed.

Ontology development for unified traditional Chinese medical language system.

Traditional Chinese medicine (TCM) as a complete knowledge system researches into human health conditions via a different approach compared to orthodox medicine. We are developing a unified traditional Chinese medical language system (UTCMLS) through an ontology approach that will support TCM language knowledge storage, concept-based information retrieval and information integration. UTCMLS is a huge knowledge project, which is a broad collaboration of 16 distributed groups, most of them with no prior experience of formal ontology development. Therefore, the cooperative and comprehensive ontology engineering is crucial. We use Protégé 2000 for ontology development of concepts and relationships that represent the domain and that will permit storage of TCM knowledge. This paper focuses on the methodology, design and development of ontology for UTCMLS.

Text-based discovery in biomedicine: the architecture of the DAD-system.

Current scientific research takes place in highly specialized contexts with poor communication between disciplines as a likely consequence. Knowledge from one discipline may be useful for the other without researchers knowing it. As scientific publications are a condensation of this knowledge, literature-based discovery tools may help the individual scientist to explore new useful domains. We report on the development of the DAD-system, a concept-based Natural Language Processing system for PubMed citations that provides the biomedical researcher such a tool. We describe the general architecture and illustrate its operation by a simulation of a well-known text-based discovery: The favorable effects of fish oil on patients suffering from Raynaud's disease [1].

An information sources map for Occupational and Environmental Medicine: guidance to network-based information through domain-specific indexing.

This paper describes a prototype information sources map (ISM), an on-line information source finder, for Occupational and Environmental Medicine (OEM). The OEM ISM was built as part of the Unified Medical Language System (UMLS) project of the National Library of Medicine. It allows a user to identify sources of on-line information appropriate to a specific OEM question, and connect to the sources. In the OEM ISM we explore a domain-specific method of indexing information source contents, and also a domain-specific user interface. The indexing represents a domain expert's opinion of the specificity of an information source in helping to answer specific types of domain questions. For each information source, an index field represents whether a source might provide useful information in an occupational, industrial, or environmental category. Additional fields represent the degree of specificity of a source in individual question types in each category. The paper discusses the development, design, and implementation of the prototype OEM ISM.