Contextualized property market models vs. Generalized mass appraisals: An innovative approach

The present research takes into account the current and widespread need for rational valuation methodologies, able to correctly interpret the available market data. An innovative automated valuation model has been simultaneously implemented to three Italian study samples, each one constituted by two-hundred residential units sold in the years 2016-2017. The ability to generate a "unique" functional form for the three different territorial contexts considered, in which the relationships between the influencing factors and the selling prices are specified by different multiplicative coefficients that appropriately represent the market phenomena of each case study analyzed, is the main contribution of the proposed methodology. The method can provide support for private operators in the assessment of the territorial investment conveniences and for the public entities in the decisional phases regarding future tax and urban planning policies

Information and communication technology solutions for outdoor navigation in dementia

INTRODUCTION: Information and communication technology (ICT) is potentially mature enough to empower outdoor and social activities in dementia. However, actual ICT-based devices have limited functionality and impact, mainly limited to safety. What is an ideal operational framework to enhance this field to support outdoor and social activities? METHODS: Review of literature and cross-disciplinary expert discussion. RESULTS: A situation-aware ICT requires a flexible fine-tuning by stakeholders of system usability and complexity of function, and of user safety and autonomy. It should operate by artificial intelligence/machine learning and should reflect harmonized stakeholder values, social context, and user residual cognitive functions. ICT services should be proposed at the prodromal stage of dementia and should be carefully validated within the life space of users in terms of quality of life, social activities, and costs. DISCUSSION: The operational framework has the potential to produce ICT and services with high clinical impact but requires substantial investment

Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning

Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons

Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

Knowledge Management (KM) has emerged as a possible solution to many of the challenges facing U.S. and international healthcare systems. These challenges include concerns regarding the safety and quality of patient care, critical inefficiency, disparate technologies and information standards, rapidly rising costs and clinical information overload. In this paper, we focus on clinical knowledge management systems (CKMS) research. The objectives of the paper are to evaluate the current state of knowledge management systems diffusion in the clinical setting, assess the present status and focus of CKMS research efforts, and identify research gaps and opportunities for future work across the medical informatics and information systems disciplines. The study analyzes the literature along two dimensions: (1) the knowledge management processes of creation, capture, transfer, and application, and (2) the clinical processes of diagnosis, treatment, monitoring and prognosis. The study reveals that the vast majority of CKMS research has been conducted by the medical and health informatics communities. Information systems (IS) researchers have played a limited role in past CKMS research. Overall, the results indicate that there is considerable potential for IS researchers to contribute their expertise to the improvement of clinical process through technology-based KM approaches

Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review

Background: The progressive aging of populations, primarily in the industrialized western world, is accompanied by the increased incidence of several non-transmittable diseases, including neurodegenerative diseases and adult-onset dementia disorders. To stimulate adequate interventions, including treatment and preventive measures, an early, accurate diagnosis is necessary. Conventional magnetic resonance imaging (MRI) represents a technique quite common for the diagnosis of neurological disorders. Increasing evidence indicates that the association of artificial intelligence (AI) approaches with MRI is particularly useful for improving the diagnostic accuracy of different dementia types. Objectives: In this work, we have systematically reviewed the characteristics of AI algorithms in the early detection of adult-onset dementia disorders, and also discussed its performance metrics. Methods: A document search was conducted with three databases, namely PubMed (Medline), Web of Science, and Scopus. The search was limited to the articles published after 2006 and in English only. The screening of the articles was performed using quality criteria based on the Newcastle-Ottawa Scale (NOS) rating. Only papers with an NOS score ≥ 7 were considered for further review. Results: The document search produced a count of 1876 articles and, because of duplication, 1195 papers were not considered. Multiple screenings were performed to assess quality criteria, which yielded 29 studies. All the selected articles were further grouped based on different attributes, including study type, type of AI model used in the identification of dementia, performance metrics, and data type. Conclusions: The most common adult-onset dementia disorders occurring were Alzheimer's disease and vascular dementia. AI techniques associated with MRI resulted in increased diagnostic accuracy ranging from 73.3% to 99%. These findings suggest that AI should be associated with conventional MRI techniques to obtain a precise and early diagnosis of dementia disorders occurring in old age

Overcoming barriers and increasing independence: service robots for elderly and disabled people

This paper discusses the potential for service robots to overcome barriers and increase independence of elderly and disabled people. It includes a brief overview of the existing uses of service robots by disabled and elderly people and advances in technology which will make new uses possible and provides suggestions for some of these new applications. The paper also considers the design and other conditions to be met for user acceptance. It also discusses the complementarity of assistive service robots and personal assistance and considers the types of applications and users for which service robots are and are not suitable

Why Internal Moral Enhancement Might Be politically Better than External Moral Enhancement

Technology could be used to improve morality but it could do so in different ways. Some technologies could augment and enhance moral behaviour externally by using external cues and signals to push and pull us towards morally appropriate behaviours. Other technologies could enhance moral behaviour internally by directly altering the way in which the brain captures and processes morally salient information or initiates moral action. The question is whether there is any reason to prefer one method over the other? In this article, I argue that there is. Specifically, I argue that internal moral enhancement is likely to be preferable to external moral enhancement, when it comes to the legitimacy of political decision-making processes. In fact, I go further than this and argue that the increasingly dominant forms of external moral enhancement may already be posing a significant threat to political legitimacy, one that we should try to address. Consequently, research and development of internal moral enhancements should be prioritised as a political project

Kaasaegsete kognitiivsete ja sotsiaalsete sekkumistehnikate loomine pediaatrilises neurorehabilitatsioonis ajukahjustusega lastel

Väitekirja elektrooniline versioon ei sisalda publikatsiooneOmandatud ajukahjustusega lapsed vajavad kaasuva kognitiivse ja sotsiaalse defitsiidi diagnostikat ja rehabilitatsiooni, mis on oluline lapse akadeemilise edukuse ja elukvaliteedi parandamisel. Neurorehabilitatsioon on plaanipärane sekkumine, mille eesmärk on kompenseerida või kergendada ajukahjustusest põhjustatud defitsiiti. Antud doktoritöö eesmärk oli uute arvutipõhiste meetodite kasutuselevõtmine omandatud ajukahjustusega laste kognitiivses ja sotsiaalses neurorehabilitatioonis. Treeningdisainid loodi tähelepanu, ruumitaju ja sotsiaalse kompetentsi häirete raviks. Uuringus osales 59 epilepsia, ajutrauma või tikkide diagnoosiga ja 47 tervet kontrollgrupi last vanuses 8–12 aastat. Patsiendid läbisid rehabilitatsiooni (10 treeningut) koos eelneva ja treeningujärgse testimisega. Tähelepanu ja ruumitaju treeningus kasutati arvutipõhist ForamenRehab lastele kohandatud programmi väljatöötatud treeningprotokollidega. Sotsiaalsete häirete raviks loodi esmalt struktureeritud neurorehabilitatsiooni mudel, mis koosnes sotsiaalse kompetentsi olulistest komponentidest, nende hindamismeetoditest ja rehabilitatsiooni vahenditest: puutetundlikud lauad Snowflake Multiteach Tabletop ja Diamond Touch Table, ning virtuaalreaalsuse keskkonnad. Tulemusena esines patsientidel treeningu eelselt väljendunud tähelepanu, ruumitaju ja sotsiaalse kompetentsi defitsiit. Arvutipõhised ja virtuaalreaalsuse programmid olid efektiivsed kognitiivsete häirete ravis. Patsientidel esines treeningute järgselt oluline paranemine kahes tähelepanu komponendis (tähelepanu jagamine ja seiramine) ja kolmes ruumitaju komponendis (visuaal-konstruktiivsed võimed, visuaalne tähelepanu ja nägemis-ruumitaju) ning raviefekt oli säilinud järeltestimisel 1,3 aastat hiljem. Sotsiaalse rehabilitatsiooni järgselt paranesid oluliselt patsientide vaimuteooria (Theory of Mind) ja emotsioonide äratundmine, kasutati rohkem koostööoskuseid, verbaalset ja mitteverbaalset kommunikatsiooni ning pragmaatika oskuseid. Uuringute tugevuseks oli sajaprotsendiline ravisoostumus ning positiivne tagasiside. Olulised on töö käigus välja töötatud teaduspõhised sekkumisprotokollid ja uued tehnoloogiapõhised rehabilitatsioonimeetodid häirunud funktsioonide spetsiifiliseks raviks lastel.Children with acquired brain injury (ABI) need diagnosis of accompanying cognitive and socio-emotional deficits and neurorehabilitation to enhance their future academic success and quality of life. Neurorehabilitation is a systematic intervention designed to compensate for or remediate the impairments caused by brain injury. The main aim of the thesis was implementing new computer-based programs, multitouch-multiuser tabletops and virtual reality in cognitive and social neurorehabilitation for children with ABI. Rehabilitation designs were developed for the treatment of attention, visuospatial, and social competence deficits. 59 children aged 8–12 years with ABI diagnosis (epilepsy, traumatic brain injury or tic disorder) and 47 healthy controls participated. Study group patients completed 10 training sessions guided by therapists. Pre-intervention assessments, and outcome assessments immediately and 1.31 years after the rehabilitation were carried out. ForamenRehab computer-programme was adapted to children and intervention protocols were created for attention and visuospatial function remediation. For social deficit remediation, the structured neurorehabilitation model was created, composed of the main components of social competence with evaluation and intervention tools: Snowflake Multiteach Tabletop, Diamond Touch Table and virtual reality programmes. Pre-intervention assessments showed that children with ABI had significant deficits in attention, visuospatial abilities and social competence functions. Computer-based and virtual reality programs were effective in the remediation of cognitive deficits in patients. After training, the patients had improved performance in two attention (complex attention and tracking) and three visuospatial components (visual organization, visual attention and visuospatial perception). The positive training effect had preserved after 1.3 years in follow-up assessments. Additionally, after social deficit rehabilitation, the patients showed improvements in Theory of Mind and emotion recognition, and they used more cooperation, communication, and pragmatic skills. The patients’ compliance was 100% and feedback was positive for all three interventions. In sum, the developed evidence-based intervention protocols and new technology-based rehabilitation methods are important in the remediation of specific cognitive deficits in children.https://www.ester.ee/record=b528718