Blueprint Personas in Digital Health Transformation

This paper presents a work in progress on the application of Blueprint Personas as a foundational tool for advancing the digital transformation of health and care services in an aging society. The paper presents how artificial intelligence (AI) and intelligent agents can support patients, caregivers, and healthcare professionals through customized, patient-centered care. By synthesizing detailed patient profiles, including medical, social, and personal factors, the aim is to enhance the interaction between healthcare technologies and users. Additionally, the work introduces the use of ontologies to structure knowledge in e-health systems, emphasizing the integration of a Reference Ontology of Trust to ensure the reliability and transparency of AI-driven care solutions. This ongoing research aims to contribute to a more empathetic and effective digital health ecosystem

Towards Empathetic Care Robots

Care Robots are the future of healthcare giving for patients with chronic diseases and disabilities. For improving the quality of care-giving and the trustworthiness of those robots, they should be equipped with emotion recognition capabilities and empathetic behavior. In this work, we propose an framework for an empathy module to be incorporated in every care robot, and then demonstrate the effectiveness of our proposal by means of an example

From pure Prolog to logic Agent-Oriented Programming Languages

We motivate and compare Agent-Oriented Logic Programming languages (AOLPs), showing that they are not really a departure from the basic logic programming paradigm, but rather constitute an extension to account for abstractions that are essential to model autonomous agents and multi-agent systems. So, existing AOLPs like AgentSpeak and DALI, which have already been successfully applied in many kinds of applications, can contribute to the spread of logic programming thinking in the next years

Empathy-Aware Behavior Trees for Social Care Decision Systems

There is growing attention on the importance of building intelligent systems where humans and Artificial Intelligence-based systems (AIs) form teams exploiting the potentially synergistic relationships between humans and automation. In the last decade, the computational modeling of empathy has gained increasing attention. Empowering interactive agents with empathic capabilities leads, on the human's side, to more trust, increases engagement, and thus interaction length, helps cope with stress. These findings suggest that agents endowed with empathy may enhance social interaction in educational applications, artificial companions, medical assistants, and gaming applications. This article focuses on modeling the empathic behavior of virtual agents interacting with humans. We propose a formal model that enables virtual agents to exhibit empathic, emotional behavior. Specifically, we extend the modeling of empathy via behavior trees with a new type of node allowing the specification of various kinds of empathy. Using the proposed extension, we show how different agents' reactive behavior can be modeled

Headache in Behçet’s disease: case reports and literature review

Objective: To evaluate the prevalence of headache and its different patterns in patients with Behçet’s disease (BD) with and without neurological involvement and to investigate clinical correlations. Methods: Patients fulfilling the International Study Group criteria for Behçet disease (ISGc) were studied. Patients were invited to fill a “headache questionnaire”, which consisted of two sections: the first one included demographic and anamnestic data, family history for both headache and BD, disease duration and clinical manifestations of BD; the second section included items about headache, investigated accordingly to International Headache Society diagnostic criteria (IHS, 2004). Clinical history and current comorbidities-medications were collected. Each patient underwent a neurological examination to assess neurological involvement (Neuro-BD) and, if necessary, instrumental investigations. One hundred-fifty healthy subjects matched for age and gender were used as control group for comparison. Results: Of the 55 patients diagnosed as BD (ISG criteria) 41 patients adhered and were enrolled into the study. Headache occurred in 29 of BD patients (70,7%) and in 13 of Neuro-BD patients (92,8%). Migraine without aura did prove the most frequent type of headache in BD patients (with and without neurological involvement) and there were no differences in the frequency of the different pattern of headache between BD patients and controls. Conclusions: Headache is a frequent manifestation in BD and primary headache like migraine emerged as the most frequent type of headache. A careful search for headache should be included in the diagnostic work-up of BD since this manifestation may be related to the underlying disease

EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease

This is the final version. Available from Nature Research via the DOI in this record. The dynamics of the resting brain exhibit transitions between a small number of discrete networks, each remaining stable for tens to hundreds of milliseconds. These functional microstates are thought to be the building blocks of spontaneous consciousness. The electroencephalogram (EEG) is a useful tool for imaging microstates, and EEG microstate analysis can potentially give insight into altered brain dynamics underpinning cognitive impairment in disorders such as Alzheimer’s disease (AD). Since EEG is non-invasive and relatively inexpensive, EEG microstates have the potential to be useful clinical tools for aiding early diagnosis of AD. In this study, EEG was collected from two independent cohorts of probable AD and cognitively healthy control participants, and a cohort of mild cognitive impairment (MCI) patients with four-year clinical follow-up. The microstate associated with the frontoparietal working-memory/attention network was altered in AD due to parietal inactivation. Using a novel measure of complexity, we found microstate transitioning was slower and less complex in AD. When combined with a spectral EEG measure, microstate complexity could classify AD with sensitivity and specificity > 80%, which was tested on an independent cohort, and could predict progression from MCI to AD in a small preliminary test cohort of 11 participants. EEG microstates therefore have potential to be a non-invasive functional biomarker of AD.Engineering and Physical Sciences Research Council (EPSRC)Wellcome TrustAlzheimer’s SocietyGarfield Weston FoundationUniversity of BristolUniversity of San Marino and IS

Serum IgG against Simian Virus 40 antigens are hampered by high levels of sHLA-G in patients affected by inflammatory neurological diseases, as multiple sclerosis

Background: Many investigators detected the simian polyomavirus SV40 footprints in human brain tumors and neurologic diseases and recently it has been indicated that SV40 seems to be associated with multiple sclerosis (MS) disease. Interestingly, SV40 interacts with human leukocyte antigen (HLA) class I molecules for cell entry. HLA class I antigens, in particular non-classical HLA-G molecules, characterized by an immune-regulatory function, are involved in MS disease, and the levels of these molecules are modified according with the disease status. Objective: We investigated in serum samples, from Italian patients affected by MS, other inflammatory diseases (OIND), non-inflammatory neurological diseases (NIND) and healthy subjects (HS), SV40-antibody and soluble sHLA-G and the association between SV40-prevalence and sHLA-G levels. Methods: ELISA tests were used for SV40-antibodies detection and sHLA-G quantitation in serum samples. Results: The presence of SV40 antibodies was observed in 6 % of patients affected by MS (N = 4/63), 10 % of OIND (N = 8/77) and 15 % of NIND (N = 9/59), which is suggestive of a lower prevalence in respect to HS (22 %, N = 18/83). MS patients are characterized by higher sHLA-G serum levels (13.9 \ub1 0.9 ng/ml; mean \ub1 St. Error) in comparison with OIND (6.7 \ub1 0.8 ng/ml), NIND (2.9 \ub1 0.4 ng/ml) and HS (2.6 \ub1 0.7 ng/ml) subjects. Interestingly, we observed an inverse correlation between SV40 antibody prevalence and sHLA-G serum levels in MS patients. Conclusion: The data obtained showed a low prevalence of SV40 antibodies in MS patients. These results seems to be due to a generalized status of inability to counteract SV40 infection via antibody production. In particular, we hypothesize that SV40 immune-inhibitory direct effect and the presence of high levels of the immune-inhibitory HLA-G molecules could co-operate in impairing B lymphocyte activation towards SV40 specific peptides

Large Action Models: End-to-End Retrieval-Enhanced Learning for Generating Function Calls from Instruction Manuals

Large action models are agents that leverage the reasoning abilities of large language models (LLMs) to make decisions in real-life scenarios. Existing approaches often fine-tune LLMs for specific instruction-function mappings, which leads to limited generalizability and eventual obsolescence. LLMs that support function calling natively require a list of tools to be passed to the model, usually in JSON format. However, their context length limits the number of supported tools. Additionally, leveraging this functionality with pay-as-you-go closed-source models can result in high inference costs. Moreover, even cutting-edge models can hallucinate or make errors in tool selection when presented with many options. This work evaluates how retrieval-augmented generation could enhance generalization in tool selection and function-calling tasks. Specifically, we treat the LLM as a frozen black box and augment it with a tunable retriever module, trained to find the documentation chunks that maximize the LLM accuracy in tool selection. Our retriever preselects relevant function headers for a given query to mitigate context length restrictions and hallucination phenomena of the LLM to which it is plugged. We conducted extensive evaluations with various models, datasets, and loss functions. For functions already seen during training, using RoBERTa-base, we observed significant performance improvements: Rank@1 increased from 22.5% to 85.0%, Rank@2 from 27.5% to 100.0%, and Rank@3 from 30.0% to 100.0%. For functions not seen during training, Rank@1 improved from 40.0% to 75.0%, Rank@2 from 50.0% to 97.5%, and Rank@3 from 50.0% to 97.5%