This paper introduces two interoperable ontologies-SOLO (Solitude Ontology) and GERO (Gerotranscendence Ontology)-developed to formalize key psychological constructs relevant to healthy aging. Grounded in the Behavioral Change Intervention Ontology, the ontologies clarify distinctions between terms such as solitude, loneliness, self-transcendence, and gerotranscendence, and encode their realization conditions across the lifespan. By integrating validated psychometric instruments and supporting structured responses to competency questions, the ontologies enable semantic reasoning, cross-disciplinary data integration, and development of ontology-driven tools for aging research and intervention design.
In natural language processing, named entity recognition (NER) is a crucial task involving finding and categorizing text entities. The biomedical domain presents substantial hurdles due to the complex structure of the language and the existence of nested entities. This paper introduces an innovative method for Nested NER by utilizing a multilayer bidirectional encoder representation transformer (BERT)-based model, notably employing pretrained PubMedBERT. Our proposed model is designed to manage nested entities' complexities effectively. We combined the robust contextual embeddings from PubMedBERT with a multilayer tagging process. This approach allowed the model to precisely differentiate between overlapping items, a frequent occurrence in biomedical literature. To assess the effectiveness of our Multilayer NER Model (MultilayerNERModel), we conducted thorough experiments on the BioNNE English Dataset, a dataset for a shared task of BioASQ competition. The findings suggest that employing a multilayer approach enhances the model's ability to identify nested entities, resulting in the thorough detection of entities in biomedical texts. It earned the highest overall performance in English oriented track, with an F1 score of 67.30% and a macro F1 score of 56.36%. These results demonstrate the significant impact of utilizing a multilayer approach in Nested NER tasks, especially in the biomedical domain. The use of UMLS dictionaries, along with the MultilayerNERModel, further enhances the model's performance in biomedical entity recognition.
A knowledge gap exists regarding the impact of organizational parameters of trauma centers and patient outcomes. This is partially due to such organizational parameters being understudied. The Ontology of Organizational Structures of Trauma Centers and Trauma Systems (OOSTT) provides a controlled vocabulary to study that specific area. It is used in tools created by the TIPTOE project to provide trauma stakeholders with novel insights on role of organizational parameters and patient outcomes. This paper reports the extension of OOSTT to cover relevant patient outcome measures.
Ontology development involves a top-down approach where ontology engineers and domain experts collaboratively define and evaluate ontological elements and axioms. Translating ontology axioms into natural language can significantly aid in ontology evaluation by making the content more understandable to subject matter experts who may lack a background in knowledge engineering. In this preliminary study, we investigate the potential of large language models (LLMs) in axiom translation from ontologies to facilitate ontology evaluation. We utilize Llama 3 to translate 1,192 ontology axioms across 19 distinct axiom types from five published ontologies. Results show that 163 (13.67%) of the Llama 3 translation of the axiom are accurately represented, 268 (22.48%) are not accurately represented, and 761 (63.84%) are partially accurate. Our manual evaluation of the Llama 3 translation indicates some competency in producing hierarchical natural language equivalents while revealing some limitations when translating complex axioms. Nonetheless, there are opportunities to improve the results with few-shot training or using LLMs to provide support in knowledge engineering for ontologies.
The proposed method focuses on speaker disentanglement in the context of depression detection from speech signals. Previous approaches require patient/speaker labels, encounter instability due to loss maximization, and introduce unnecessary parameters for adversarial domain prediction. In contrast, the proposed unsupervised approach reduces cosine similarity between latent spaces of depression and pre-trained speaker classification models. This method outperforms baseline models, matches or exceeds adversarial methods in performance, and does so without relying on speaker labels or introducing additional model parameters, leading to a reduction in model complexity. The higher the speaker de-identification score (DeID), the better the depression detection system is in masking a patient's identity thereby enhancing the privacy attributes of depression detection systems. On the DAIC-WOZ dataset with ComparE16 features and an LSTM-only model, our method achieves an F1-Score of 0.776 and a DeID score of 92.87%, outperforming its adversarial counterpart which has an F1Score of 0.762 and 68.37% DeID, respectively. Furthermore, we demonstrate that speaker-disentanglement methods are complementary to text-based approaches, and a score-level fusion with a Word2vec-based depression detection model further enhances the overall performance to an F1-Score of 0.830.
Addressing the critical shortage of mental health resources for effective screening, diagnosis, and treatment remains a significant challenge. This scarcity underscores the need for innovative solutions, particularly in enhancing the accessibility and efficacy of therapeutic support. Embodied agents with advanced interactive capabilities emerge as a promising and cost-effective supplement to traditional caregiving methods. Crucial to these agents' effectiveness is their ability to simulate non-verbal behaviors, like backchannels, that are pivotal in establishing rapport and understanding in therapeutic contexts but remain under-explored. To improve the rapport-building capabilities of embodied agents we annotated backchannel smiles in videos of intimate face-to-face conversations over topics such as mental health, illness, and relationships. We hypothesized that both speaker and listener behaviors affect the duration and intensity of backchannel smiles. Using cues from speech prosody and language along with the demographics of the speaker and listener, we found them to contain significant predictors of the intensity of backchannel smiles. Based on our findings, we introduce backchannel smile production in embodied agents as a generation problem. Our attention-based generative model suggests that listener information offers performance improvements over the baseline speaker-centric generation approach. Conditioned generation using the significant predictors of smile intensity provides statistically significant improvements in empirical measures of generation quality. Our user study by transferring generated smiles to an embodied agent suggests that agent with backchannel smiles is perceived to be more human-like and is an attractive alternative for non-personal conversations over agent without backchannel smiles.
Adequately representing kinship relations is crucial for a variety of medical and biomedical applications. Several kinship ontologies have been proposed but none of them have been designed thus far in line with the Basic Formal Ontology. In this paper, we propose a novel kinship ontology that exhibits the following characteristics: (1) it is fully axiomatized in First Order Logic following the rules governing predicate formation as proposed in BFO2020-FOL, (2) it is modularized in 6 separate files written in the Common Logic Interface Format (CLIF) each one of which can be imported based on specific needs, (3) it provides bridging axioms to and from SNOMED CT, and (4) it contains an extra module with axioms which would not be literally true when phrased naively but are crafted in such a way that they highlight the unusual kinship relations they represent and can be used to generate alerts on possible data entry mistakes. We describe design considerations and challenges encountered.
