OBJECTIVES: To develop and evaluate a human-LLM (Large Language Model) collaborative approach for systematic ontology updating, demonstrated with the Dietary Lifestyle Ontology (DILON). MATERIALS AND METHODS: One hundred dietary questionnaire items from English and Korean sources were semantically annotated by 4 state-of-the-art language models, which generated candidate concepts for inclusion into DILON. Outputs were refined through cross-model reconciliation, followed by expert review. The model curated the concept within DILON and experts reviewed and refined the outputs in Protégé to ensure accuracy and consistency. RESULTS: Claude Sonnet 4 effectively supported local tasks, including harvesting new concepts, detecting redundancies, and refining hierarchical segments. Global optimization of ontology, however, required systematic examination by human experts. DISCUSSION: These findings highlight the complementary strengths of LLMs and humans: LLMs accelerate repetitive and local updates, whereas humans maintain overall structural integrity. CONCLUSION: Human-LLM collaboration improves efficiency, scalability, and sustainability in ontology engineering, supporting the maintenance of complex biomedical ontologies.

OBJECTIVE: This study aims to facilitate the creation of quality standardized nursing statements in South Korea's hospitals using algorithmic generation based on the International Classifications of Nursing Practice (ICNP) and evaluation through Large Language Models. MATERIALS AND METHODS: We algorithmically generated 15 972 statements related to acute respiratory care using 117 concepts and concept composition models of ICNP. Human reviewers, Generative Pre-trained Transformers 4.0 (GPT-4.0), and Bio_Clinical Bidirectional Encoder Representations from Transformers (BERT) evaluated the generated statements for validity. The evaluation by GPT-4.0 and Bio_ClinicalBERT was conducted with and without contextual information and training. RESULTS: Of the generated statements, 2207 were deemed valid by expert reviewers. GPT-4.0 showed a zero-shot AUC of 0.857, which aggravated with contextual information. Bio_ClinicalBERT, after training, significantly improved, reaching an AUC of 0.998. CONCLUSION: Bio_ClinicalBERT effectively validates auto-generated nursing statements, offering a promising solution to enhance and streamline healthcare documentation processes.

OBJECTIVE: With the increased routine use of advanced imaging in clinical diagnosis and treatment, it has become imperative to provide patients with a means to view and understand their imaging studies. We illustrate the feasibility of a patient portal that automatically structures and integrates radiology reports with corresponding imaging studies according to several information orientations tailored for the layperson. METHODS: The imaging patient portal is composed of an image processing module for the creation of a timeline that illustrates the progression of disease, a natural language processing module to extract salient concepts from radiology reports (73% accuracy, F1 score of 0.67), and an interactive user interface navigable by an imaging findings list. The portal was developed as a Java-based web application and is demonstrated for patients with brain cancer. RESULTS AND DISCUSSION: The system was exhibited at an international radiology conference to solicit feedback from a diverse group of healthcare professionals. There was wide support for educating patients about their imaging studies, and an appreciation for the informatics tools used to simplify images and reports for consumer interpretation. Primary concerns included the possibility of patients misunderstanding their results, as well as worries regarding accidental improper disclosure of medical information. CONCLUSIONS: Radiologic imaging composes a significant amount of the evidence used to make diagnostic and treatment decisions, yet there are few tools for explaining this information to patients. The proposed radiology patient portal provides a framework for organizing radiologic results into several information orientations to support patient education.

Imaging has become a prevalent tool in the diagnosis and treatment of many diseases, providing a unique in vivo, multi-scale view of anatomic and physiologic processes. With the increased use of imaging and its progressive technical advances, the role of imaging informatics is now evolving--from one of managing images, to one of integrating the full scope of clinical information needed to contextualize and link observations across phenotypic and genotypic scales. Several challenges exist for imaging informatics, including the need for methods to transform clinical imaging studies and associated data into structured information that can be organized and analyzed. We examine some of these challenges in establishing imaging-based observational databases that can support the creation of comprehensive disease models. The development of these databases and ensuing models can aid in medical decision making and knowledge discovery and ultimately, transform the use of imaging to support individually-tailored patient care.

Due to the increasingly data-intensive clinical environment, physicians now have unprecedented access to detailed clinical information from a multitude of sources. However, applying this information to guide medical decisions for a specific patient case remains challenging. One issue is related to presenting information to the practitioner: displaying a large (irrelevant) amount of information often leads to information overload. Next-generation interfaces for the electronic health record (EHR) should not only make patient data easily searchable and accessible, but also synthesize fragments of evidence documented in the entire record to understand the etiology of a disease and its clinical manifestation in individual patients. In this paper, we describe our efforts toward creating a context-based EHR, which employs biomedical ontologies and (graphical) disease models as sources of domain knowledge to identify relevant parts of the record to display. We hypothesize that knowledge (e.g., variables, relationships) from these sources can be used to standardize, annotate, and contextualize information from the patient record, improving access to relevant parts of the record and informing medical decision making. To achieve this goal, we describe a framework that aggregates and extracts findings and attributes from free-text clinical reports, maps findings to concepts in available knowledge sources, and generates a tailored presentation of the record based on the information needs of the user. We have implemented this framework in a system called Adaptive EHR, demonstrating its capabilities to present and synthesize information from neurooncology patients. This paper highlights the challenges and potential applications of leveraging disease models to improve the access, integration, and interpretation of clinical patient data.

Episode creation is the task of classifying medical events and related clinical data to high-level concepts, such as diseases. Challenges in episode creation result in part because of data, in the patient record, only implicitly being associated with their respective episodes. Furthermore, traditional approaches have been limited to using feature-poor claims records to generate episodes. The accurate correlation of data to their episodes is valuable in health outcomes research to discern resource utilization with respect to medical conditions. This paper describes a combinatorial optimization approach for constructing episodes, which supports the incorporation of heterogeneous data types. Aspects of this approach include an episode model for characterizing the generation of data elements as a result of a process, a methodology for identifying the relationships between implicit processes and the data elements generated by the processes, a measure for evaluating candidate episode configurations, and an energy-minimization methodology for addressing episode creation. An implementation of this work, called Episode Creation Version 2 (EC2), has been applied on patient records with various episode types, which present with knee pain. EC2 demonstrated data element classification precision and recall scores of 78% and 82%, respectively. Significant improvements in precision and recall were observed over a traditional healthcare services approach.

Natural language processing (NLP) is being applied for several information extraction tasks in the biomedical domain. The unique nature of clinical information requires the need for developing an NLP system designed specifically for the clinical domain. We describe a method to identify semantically coherent phrases within clinical reports. This is an important step towards full syntactic parsing within a clinical NLP system. We use this semantic phrase chunker to identify anatomical phrases within radiology reports related to the genitourinary domain. A discriminative classifier based on support vector machines was used to classify words into one of five phrase classification categories. Training of the classifier was performed using 1000 hand-tagged sentences from a corpus of genitourinary radiology reports. Features used by the classifier include n-grams, syntactic tags and semantic labels. Evaluation was conducted on a blind test set of 250 sentences from the same domain. The system achieved overall performance scores of 0.87 (precision), 0.91 (recall) and 0.89 (balanced f-score). Anatomical phrase extraction can be rapidly and accurately accomplished

The development of comprehensive picture archive and communication systems (PACS) has mainly been limited to proprietary developments by vendors, though a number of freely available software projects have addressed specific image management tasks. The openSourcePACS project aims to provide an open source, common foundation upon which not only can a basic PACS be readily implemented, but to also support the evolution of new PACS functionality through the development of novel imaging applications and services. openSourcePACS consists of four main software modules: 1) image order entry, which enables the ordering and tracking of structured image requisitions; 2) an agent-based image server framework that coordinates distributed image services including routing, image processing, and querying beyond the present digital image and communications in medicine (DICOM) capabilities; 3) an image viewer, supporting standard display and image manipulation tools, DICOM presentation states, and structured reporting; and 4) reporting and result dissemination, supplying web-based widgets for creating integrated reports. All components are implemented using Java to encourage cross-platform deployment. To demonstrate the usage of openSourcePACS, a preliminary application supporting primary care/specialist communication was developed and is described herein. Ultimately, the goal of openSourcePACS is to promote the wide-scale development and usage of PACS and imaging applications within academic and research communities.

A parser for medical free text reports has been developed that is based on a chemistry/physics inspired "field theory" for word-word sentence-level dependencies. The transition from the linguistic world to the world of interacting particles with potential energies is guided by a psycholinguistics thought experiment related to the amount of "work" required to bring a reference word into an anchored configuration of words. Calibration experiments involving four and five grams were conducted. Data from these experiments were used as a knowledge source for estimating field conditions for words in sentences sampled from a corpus of medical reports. The result of the parser is a dependency tree that represents the global minimum energy state of the system of words for a given sentence. The system was trained and tested on a corpus of radiology reports. Preliminary performance, as quantified by link recall and precision statistics, is 84.9% and 89.9%, respectively.

A knowledge-based approach to retrieve medical images by feature and content with spatial and temporal constructs is developed. Selected objects of interest in an image are segmented and contours are generated. Features and content are extracted and stored in a database. Knowledge about image features can be expressed as a type abstraction hierarchy (TAH), the high-level nodes of which represent the most general concepts. Traversing TAH nodes allows approximate matching by feature and content if an exact match is not available. TAHs can be generated automatically by clustering algorithms based on feature values in the databases and hence are scalable to large collections of image features. Since TAHs are generated based on user classes and applications, they are context- and user-sensitive. A knowledge-based semantic image model is proposed to represent the various aspects of an image object's characteristics. The model provides a mechanism for accessing and processing spatial, evolutionary and temporal queries. A knowledge-based spatial temporal query language (KSTL) has been developed that extends ODMG's OQL and supports approximate matching of features and content, conceptual terms and temporal logic predicates. Further, a visual query language has been developed that accepts point-click-and-drag visual iconic input on the screen that is then translated into KSTL. User models are introduced to provide default parameter values for specifying query conditions. We have implemented the KMeD (Knowledge-based Medical Database) system using these concepts.

A knowledge based approach is introduced for retrieving images by content. It supports the answering of conceptual image queries involving similar-to predicates, spatial semantic operators, and references to conceptual terms. Interested objects in the images are represented by contours segmented from images. Image content such as shapes and spatial relationships are derived from object contours according to domain specific image knowledge. A three layered model is proposed for integrating image representations, extracted image features, and image semantics. With such a model, images can be retrieved based on the features and content specified in the queries. The knowledge based query processing is based on a query relaxation technique. The image features are classified by an automatic clustering algorithm and represented by Type Abstraction Hierarchies (TAHs) for knowledge based query processing. Since the features selected for TAH generation are based on context and user profile, and the TAHs can be generated automatically by a clustering algorithm from the feature database, our proposed image retrieval approach is scalable and context sensitive. The performance of the proposed knowledge based query processing is also discussed.

PICQUERY/sup +/, a high-level domain-independent query language for pictorial and alphanumeric database management, is introduced. The PICQUERY/sup +/ language and its underlying stacked image data model are enhanced with major advances that include: convenient specification of the data domain space among a multimedia database federation, visualization of underlying data models, knowledge-based hierarchies, and domain rules, understanding of high-level abstract data types, ability to perform data object matches based on imprecise or fuzzy descriptors, imprecise relational correlators, and temporal and object evolutionary events, specification of alphanumeric and image processing algorithms on data, and specification of alphanumeric and image visualization methods for user presentation. The power of PICQUERY/sup +/ is illustrated using examples drawn from the medical imaging domain. A graphical menu-driven user interface is demonstrated for this domain as an example of the menu interface capabilities of PICQUERY/sup +/.>

The authors present a temporal evolutionary object-oriented data model (TEDM) for modeling medical images. The intelligent medical image management system (IMIS) lies on top of a picture archive and communication system (PACS) infrastructure. The IMIS can retrieve medical images (e.g. X-rays, computed tomography scans, magnetic resonance scans, etc.) by image features and contents rather than by traditional artificial keys such as a patient hospital identification number. As a result, solutions to queries which associate the radiographic findings of an image, the disease pathology and the categorical patient subpopulation can be obtained. The proposed model and language constructs can also be applied to other domains that exemplify the evolutionary transformations of objects, such as modeling the growth of brain tumors.>