OBJECTIVE: Accurately characterizing research trends is critical for identifying cutting-edge scientific breakthroughs in their infancy and informing strategic priorities. This research contributes a pipeline that utilizes generative AI technologies to develop research topic taxonomies from publication keywords and analyze keyword evolution within topics, methodological and domain trends, and topic co-occurrences. We demonstrated the pipeline by conducting a retrospective analysis of biomedical informatics research trends in the Journal of Biomedical Informatics (JBI). METHODS: We identified the JBI publications with keywords available on PubMed, spanning 2011-2025. We downloaded all the keywords and categorized them into methodological innovations and health domains, identified topics, assigned topic names, and constructed their hierarchies, all using large-language models (LLMs). We introduced an automated method for evaluating topics, leveraging MeSH terminology as the underlying knowledge base. RESULTS: Using 6,930 unique keywords from 2,427 publications, we derived 1,028 distinct topics related to methodological innovations, with each topic associated with medians of four keywords (Q1: 2, Q3: 13) and six publications (Q1: 2, Q3: 19). We identified 904 topics related to health domains, with each topic associated with three keywords (Q1: 1, Q3: 11) and four publications (Q1: 1, Q3: 15). Based on the topics, we analyzed the prominent research areas, trends in publication volume, evolution of keyword distributions within each topic, and patterns of co-occurring topics. Among the 2,379 eligible publications, 2,009 (84.4%) exhibited overlap between the keyword-derived MeSH terms and the MeSH terms assigned to the publication by the National Library of Medicine. CONCLUSION: This study presents a method that leverages modern generative AI technologies for retrospective analysis of a scientific field to identify emerging topics and to detect shifts in scholarly focus. Illustrated by data for JBI and correlated with historical background events and policy changes, our findings demonstrate the effectiveness and utility of the methods while providing a powerful lens to understand the evolution of biomedical informatics research priorities in JBI.

Designing effective theory-driven digital behaviour change interventions (DBCI) is a challenging task. To ease the design process, and assist with knowledge sharing and evaluation of the DBCI, we propose the SATO (IDEAS expAnded wiTh BCIO) design workflow based on the IDEAS (Integrate, Design, Assess, and Share) framework and aligned with the Behaviour Change Intervention Ontology (BCIO). BCIO is a structural representation of the knowledge in behaviour change domain supporting evaluation of behaviour change interventions (BCIs) but it is not straightforward to utilise it during DBCI design. IDEAS (Integrate, Design, Assess, and Share) framework guides multi-disciplinary teams through the mobile health (mHealth) application development life-cycle but it is not aligned with BCIO entities. SATO couples BCIO entities with workflow steps and extends IDEAS Integrate stage with consideration of customisation and personalisation. We provide a checklist of the activities that should be performed during intervention planning with concrete examples and a tutorial accompanied with case studies from the Cancer Better Life Experience (CAPABLE) European project. In the process of creating this workflow, we found the necessity to extend the BCIO to support the scenarios of multiple clinical goals in the same application. To ensure the SATO steps are easy to follow for the incomers to the field, we performed a preliminary evaluation of the workflow with two knowledge engineers, working on novel mHealth app design tasks.

The CAPABLE project aims to improve the wellbeing of cancer patients managed at home via a mobile Coaching System recommending physical and mental health interventions. Patient reported outcomes are important for evaluation of the efficacy of these interventions. Nevertheless a large number of surveys might be overwhelming to patients. To understand the cognitive demand caused by the surveys and to find the adequate time to prompt patients to complete them we carried out a feasibility study. In this study we developed a machine learning cognitive load detector from blood volume pulse (BVP) captured by a photoplethysmography (PPG) signal. PPG sensors are available on consumer-grade smartwatches, which we will use in our Coaching System. We found that personalised 1D convolutional neural networks trained on raw BVP signal performed better in binary high vs low cognitive load classification than the personalised Support Vector Machines trained with heart rate variability and BVP features. We investigated if the further improvements can be obtained by teacher-student semi-supervised model training, nevertheless the performance gains were not notable. In the future we will include additional context information that might aid cognitive load estimation and drive both survey design as well as the timing of the prompts.

INTRODUCTION: Thanks to improvement of care, cancer has become a chronic condition. But due to the toxicity of treatment, the importance of supporting the quality of life (QoL) of cancer patients increases. Monitoring and managing QoL relies on data collected by the patient in his/her home environment, its integration, and its analysis, which supports personalization of cancer management recommendations. We review the state-of-the-art of computerized systems that employ AI and Data Science methods to monitor the health status and provide support to cancer patients managed at home. OBJECTIVE: Our main objective is to analyze the literature to identify open research challenges that a novel decision support system for cancer patients and clinicians will need to address, point to potential solutions, and provide a list of established best-practices to adopt. METHODS: We designed a review study, in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzing studies retrieved from PubMed related to monitoring cancer patients in their home environments via sensors and self-reporting: what data is collected, what are the techniques used to collect data, semantically integrate it, infer the patient's state from it and deliver coaching/behavior change interventions. RESULTS: Starting from an initial corpus of 819 unique articles, a total of 180 papers were considered in the full-text analysis and 109 were finally included in the review. Our findings are organized and presented in four main sub-topics consisting of data collection, data integration, predictive modeling and patient coaching. CONCLUSION: Development of modern decision support systems for cancer needs to utilize best practices like the use of validated electronic questionnaires for quality-of-life assessment, adoption of appropriate information modeling standards supplemented by terminologies/ontologies, adherence to FAIR data principles, external validation, stratification of patients in subgroups for better predictive modeling, and adoption of formal behavior change theories. Open research challenges include supporting emotional and social dimensions of well-being, including PROs in predictive modeling, and providing better customization of behavioral interventions for the specific population of cancer patients.

A Tribute to

OBJECTIVE: We show how the HL7 Virtual Medical Record (vMR) standard can be used to design and implement a data integrator (DI) component that collects patient information from heterogeneous sources and stores it into a personal health record, from which it can then retrieve data. Our working hypothesis is that the HL7 vMR standard in its release 1 version can properly capture the semantics needed to drive evidence-based clinical decision support systems. MATERIALS AND METHODS: To achieve seamless communication between the personal health record and heterogeneous data consumers, we used a three-pronged approach. First, the choice of the HL7 vMR as a message model for all components accompanied by the use of medical vocabularies eases their semantic interoperability. Second, the DI follows a service-oriented approach to provide access to system components. Third, an XML database provides the data layer.Results The DI supports requirements of a guideline-based clinical decision support system implemented in two clinical domains and settings, ensuring reliable and secure access, high performance, and simplicity of integration, while complying with standards for the storage and processing of patient information needed for decision support and analytics. This was tested within the framework of a multinational project (www.mobiguide-project.eu) aimed at developing a ubiquitous patient guidance system (PGS). DISCUSSION: The vMR model with its extension mechanism is demonstrated to be effective for data integration and communication within a distributed PGS implemented for two clinical domains across different healthcare settings in two nations.

Situation-Based Access Control (SitBAC) is a conceptual model for representing access control policies of healthcare organizations by characterizing situations of access to patient data. The SitBAC model enables formal representation of access situations as an ontology of concepts (Patient, Data Requestor, EHR, Task, and Response) along with their attributes and relationships. A competing access control model is the Contextual Role-Based Access Control (Context) model. The Context model uses logical expressions (rules) that specify contextual authorizations (i.e., characteristics of access requests that are available at access time). Open questions that relate to formal representation of scenarios involving access to patient data are: 1) which of the two models yields a formal representation that is easier to comprehend; 2) which of the two models facilitates the synthesis of correct models, and how does the task complexity affect the performance of comprehension and synthesis. In this study, we address these questions through a controlled experiment. The results of the experiment suggest that while there are no differences between the two models when it comes to comprehending or synthesizing simple scenarios of data access, for complex scenarios, there is a significant advantage to the SitBAC model in terms of both comprehension and synthesis.

Access control is a central problem in privacy management. A common practice in controlling access to sensitive data, such as electronic health records (EHRs), is Role-Based Access Control (RBAC). RBAC is limited as it does not account for the circumstances under which access to sensitive data is requested. Following a qualitative study that elicited access scenarios, we used Object-Process Methodology to structure the scenarios and conceive a Situation-Based Access Control (SitBAC) model. SitBAC is a conceptual model, which defines scenarios where patient's data access is permitted or denied. The main concept underlying this model is the Situation Schema, which is a pattern consisting of the entities Data-Requestor, Patient, EHR, Access Task, Legal-Authorization, and Response, along with their properties and relations. The various data access scenarios are expressed via Situation Instances. While we focus on the medical domain, the model is generic and can be adapted to other domains.

OBJECTIVES: Languages used to specify computer-interpretable guidelines (CIGs) differ in their approaches to addressing particular modeling challenges. The main goals of this article are: (1) to examine the expressive power of CIG modeling languages, and (2) to define the differences, from the control-flow perspective, between process languages in workflow management systems and modeling languages used to design clinical guidelines. DESIGN: The pattern-based analysis was applied to guideline modeling languages Asbru, EON, GLIF, and PROforma. We focused on control-flow and left other perspectives out of consideration. MEASUREMENTS: We evaluated the selected CIG modeling languages and identified their degree of support of 43 control-flow patterns. We used a set of explicitly defined evaluation criteria to determine whether each pattern is supported directly, indirectly, or not at all. RESULTS: PROforma offers direct support for 22 of 43 patterns, Asbru 20, GLIF 17, and EON 11. All four directly support basic control-flow patterns, cancellation patterns, and some advance branching and synchronization patterns. None support multiple instances patterns. They offer varying levels of support for synchronizing merge patterns and state-based patterns. Some support a few scenarios not covered by the 43 control-flow patterns. CONCLUSION: CIG modeling languages are remarkably close to traditional workflow languages from the control-flow perspective, but cover many fewer workflow patterns. CIG languages offer some flexibility that supports modeling of complex decisions and provide ways for modeling some decisions not covered by workflow management systems. Workflow management systems may be suitable for clinical guideline applications.

Petri Nets (PNs) and their extensions are promising methods for modeling and simulating biological systems. We surveyed PN formalisms and tools and compared them based on their mathematical capabilities as well as by their appropriateness to represent typical biological processes. We measured the ability of these tools to model specific features of biological systems and answer a set of biological questions that we defined. We found that different tools are required to provide all capabilities that we assessed. We created software to translate a generic PN model into most of the formalisms and tools discussed. We have also made available three models and suggest that a library of such models would catalyze progress in qualitative modeling via PNs. Development and wide adoption of common formats would enable researchers to share models and use different tools to analyze them without the need to convert to proprietary formats.

InterMed is a collaboration among research groups from Stanford, Harvard, and Columbia Universities. The primary goal of InterMed has been to develop a sharable language that could serve as a standard for modeling computer-interpretable guidelines (CIGs). This language, called GuideLine Interchange Format (GLIF), has been developed in a collaborative manner and in an open process that has welcomed input from the larger community. The goals and experiences of the InterMed project and lessons that the authors have learned may contribute to the work of other researchers who are developing medical knowledge-based tools. The lessons described include (1) a work process for multi-institutional research and development that considers different viewpoints, (2) an evolutionary lifecycle process for developing medical knowledge representation formats, (3) the role of cognitive methodology to evaluate and assist in the evolutionary development process, (4) development of an architecture and (5) design principles for sharable medical knowledge representation formats, and (6) a process for standardization of a CIG modeling language.

OBJECTIVES: Many groups are developing computer-interpretable clinical guidelines (CIGs) for use during clinical encounters. CIGs use "Task-Network Models" for representation but differ in their approaches to addressing particular modeling challenges. We have studied similarities and differences between CIGs in order to identify issues that must be resolved before a consensus on a set of common components can be developed. DESIGN: We compared six models: Asbru, EON, GLIF, GUIDE, PRODIGY, and PROforma. Collaborators from groups that created these models represented, in their own formalisms, portions of two guidelines: American College of Chest Physicians cough guidelines [correction] and the Sixth Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. MEASUREMENTS: We compared the models according to eight components that capture the structure of CIGs. The components enable modelers to encode guidelines as plans that organize decision and action tasks in networks. They also enable the encoded guidelines to be linked with patient data-a key requirement for enabling patient-specific decision support. RESULTS: We found consensus on many components, including plan organization, expression language, conceptual medical record model, medical concept model, and data abstractions. Differences were most apparent in underlying decision models, goal representation, use of scenarios, and structured medical actions. CONCLUSION: We identified guideline components that the CIG community could adopt as standards. Some of the participants are pursuing standardization of these components under the auspices of HL7.

MOTIVATION: Biological processes can be considered at many levels of detail, ranging from atomic mechanism to general processes such as cell division, cell adhesion or cell invasion. The experimental study of protein function and gene regulation typically provides information at many levels. The representation of hierarchical process knowledge in biology is therefore a major challenge for bioinformatics. To represent high-level processes in the context of their component functions, we have developed a graphical knowledge model for biological processes that supports methods for qualitative reasoning. RESULTS: We assessed eleven diverse models that were developed in the fields of software engineering, business, and biology, to evaluate their suitability for representing and simulating biological processes. Based on this assessment, we combined the best aspects of two models: Workflow/Petri Net and a biological concept model. The Workflow model can represent nesting and ordering of processes, the structural components that participate in the processes, and the roles that they play. It also maps to Petri Nets, which allow verification of formal properties and qualitative simulation. The biological concept model, TAMBIS, provides a framework for describing biological entities that can be mapped to the workflow model. We tested our model by representing malaria parasites invading host erythrocytes, and composed queries, in five general classes, to discover relationships among processes and structural components. We used reachability analysis to answer queries about the dynamic aspects of the model. AVAILABILITY: The model is available at http://smi.stanford.edu/projects/helix/pubs/process-model/.

The exponential growth in the volume of biological information available makes it difficult for researchers to assemble the details into coherent models. Although an accurate model is ideal, full details are not generally available and are gained only incrementally. Therefore, as a first step toward integration of information, we propose a knowledge model for the qualitative representation of the relationships between mutations in genes and their effects at molecular cellular and clinical phenotypic levels. Our framework combines and extends two components: 1) a workflow model that allows hierarchical process and participant specifications; 2) Transparent Access to Multiple Bioinformatics Information Sources and the Unified Medical Language System, which serve as controlled biological and medical terminologies. By mapping our framework to Petri nets, we can perform qualitative simulations to validate models, and aid in predicting system behavior in the presence of dysfunctional components. This can be a step toward accurate quantitative models. Our application domain is the role of transfer ribonucleic acid molecules in protein translation-related disease. As an initial evaluation, we show that Petri nets derived from the historic and current views of the translation process yield different dynamic behavior. Our model is available at http://smi.stanford.edu/projects/helix/pubs/process-model/.

The object-process methodology (OPM) specifies both graphically and textually the system's static-structural and behavioral-procedural aspects through a single unifying model. This model singularity is contrasted with the multimodel approach applied by existing object oriented system analysis methods. These methods usually employ at least three distinct models for specifying various system aspects: mainly structure, function, and behavior. Object modeling technique (OMT), the main ancestor of the unified modeling language (UML), extended with timed statecharts, represents a family of such multimodal object oriented methods. Two major open questions related to model multiplicity vs. model singularity have been: 1) whether or not a single model, rather than a combination of several models, enables the synthesis of a better system specification; and 2) which of the two alternative approaches yields a specification that is easier to comprehend. The authors address these questions through a double-blind controlled experiment. To obtain conclusive results, real time systems, which exhibit a more complex dynamic behavior than nonreal time systems were selected as the focus of the experiment. We establish empirically that a single model methodology, OPM, is more effective than a multimodel one, OMT, in terms of synthesis. We pinpoint specific issues in which significant diiferences between the two methodologies were found. The specification comprehension results show that there were significant differences between the two methods in specific issues.