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In this article, we survey the applications of Three-way decision theory (TWD) in machine learning (ML), focusing in particular on four tasks: weakly supervised learning and multi-source data management, missing data management, uncertainty quantification in classification, and uncertainty quantification in clustering. For each of these four tasks we present the results of a systematic review of the literature, by which we report on the main characteristics of the current state of the art, as well as on the quality of reporting and reproducibility level of the works found in the literature. To this aim, we discuss the main benefits, limitations and issues found in the reviewed articles, and we give clear indications and directions for quality improvement that are informed by validation, reporting, and reproducibility standards, guidelines and best practice that have recently emerged in the ML field. Finally, we discuss about the more promising and relevant directions for future research in regard to TWD.

Research on wearable devices and sensors applied to older adults has witnessed significant growth, primarily focusing on health monitoring but also on researching real-world applications of urban walking, specifically, walkability. The use of pedestrians' bodily responses to the environment, known as the people-centric sensing strategy, has been demonstrated to be more suitable for detecting challenging environmental conditions and enhancing walkability. However, the extent to which such data may accurately pinpoint environmental distress in a particular demographic, such as older adults, has not been thoroughly investigated. The purpose of this in-progress systematic review of the literature is to outline the strategy employed and highlight some preliminary findings on the use of wearable sensors or sensor-based technologies in gathering the bodily responses of older adults and/or their family caregivers to stimuli originating from real urban walking scenarios. Our preliminary findings showed that the current research on using wearable devices to detect bodily responses in older adults (or informal caregivers) population in relation to walkability in outdoor environments tends to exhibit a uniform strategy, that is, collecting physiological and location data from older adults through controlled outdoor walking routes using wrist-wearable and GPS; training supervised classifiers to differentiate between physiological stress and non-stress signals to environmental conditions of external interaction; and finally, using hotspot analysis to group together individual physiological responses in areas with high-stress interactions with the external environment using GIS. Although the body of literature appears to be still in its early stages, using wearable sensors and a GIS-based approach could be a promising method for spatio-temporally capturing people's direct bodily responses to the environmental stressors, and this offer rooms for potential integration of simulation

Background: The frequency of hip and knee arthroplasty surgeries has been rising steadily in recent decades. This trend is attributed to an aging population, leading to increased demands on healthcare systems. Fast Track (FT) surgical protocols, perioperative procedures designed to expedite patient recovery and early mobilization, have demonstrated efficacy in reducing hospital stays, convalescence periods, and associated costs. However, the criteria for selecting patients for FT procedures have not fully capitalized on the available patient data, including patient-reported outcome measures (PROMs). Methods: Our study focused on developing machine learning (ML) models to support decision making in assigning patients to FT procedures, utilizing data from patients' self-reported health status. These models are specifically designed to predict the potential health status improvement in patients initially selected for FT. Our approach focused on techniques inspired by the concept of controllable AI. This includes eXplainable AI (XAI), which aims to make the model's recommendations comprehensible to clinicians, and cautious prediction, a method used to alert clinicians about potential control losses, thereby enhancing the models' trustworthiness and reliability. Results: Our models were trained and tested using a dataset comprising 899 records from individual patients admitted to the FT program at IRCCS Ospedale Galeazzi-Sant'Ambrogio. After training and selecting hyper-parameters, the models were assessed using a separate internal test set. The interpretable models demonstrated performance on par or even better than the most effective 'black-box' model (Random Forest). These models achieved sensitivity, specificity, and positive predictive value (PPV) exceeding 70%, with an area under the curve (AUC) greater than 80%. The cautious prediction models exhibited enhanced performance while maintaining satisfactory coverage (over 50%). Further, when externally validated on a

Informal or unpaid caregivers, commonly known as family caregivers, are responsible for providing the 80% of long-term care in Europe, which constitutes a significant portion of health and social care services offered to elderly or disabled individuals. However, the demand for informal care among the elderly is expected to outnumber available supply by 2060. The increasing decline in the caregiver-to-patient ratio is expected to lead to a substantial expansion in the integration of intelligent assistance within general care. The aim of this systematic review was to thoroughly investigate the most recent advancements in AI-enabled technologies, as well as those encompassed within the broader category of assistive technology (AT), which are designed with the primary or secondary goal to assist informal carers. The review sought to identify the specific needs that these technologies fulfill in the caregiver’s activities related to the care of older individuals, the identification of caregivers’ needs domains that are currently neglected by the existing AI-supporting technologies and ATs, as well as shedding light on the informal caregiver groups that are primarily targeted by those currently available. Three databases (Scopus, IEEE Xplore, ACM Digital Libraries) were searched. The search yielded 1002 articles, with 24 articles that met the inclusion and exclusion criteria. Our results showed that AI-powered technologies significantly facilitate ambient assisted living (AAL) applications, wherein the integration of home sensors serves to improve remote monitoring for informal caregivers. Additionally, AI solutions contribute to improve care coordination between formal and informal caregivers, that could lead to advanced telehealth assistance. However, limited research on assistive technologies like robots and mHealth apps suggests further exploration. Future AI-based solutions and assistive technologies (ATs) may benefit from a more targeted approach to appeasing specif

Informal or unpaid caregivers, commonly known as family caregivers, are responsible for providing the 80% of long-term care in Europe, which constitutes a significant portion of health and social care services offered to elderly or disabled individuals. However, the demand for informal care among the elderly is expected to outnumber available supply by 2060. The increasing decline in the caregiver-to-patient ratio is expected to lead to a substantial expansion in the integration of intelligent assistance within general care. The aim of this systematic review was to thoroughly investigate the most recent advancements in AI-enabled technologies, as well as those encompassed within the broader category of assistive technology (AT), which are designed with the primary or secondary goal to assist informal carers. The review sought to identify the specific needs that these technologies fulfill in the caregiver’s activities related to the care of older individuals, the identification of caregivers’ needs domains that are currently neglected by the existing AI-supporting technologies and ATs, as well as shedding light on the informal caregiver groups that are primarily targeted by those currently available. Three databases (Scopus, IEEE Xplore, ACM Digital Libraries) were searched. The search yielded 1002 articles, with 24 articles that met the inclusion and exclusion criteria. Our results showed that AI-powered technologies significantly facilitate ambient assisted living (AAL) applications, wherein the integration of home sensors serves to improve remote monitoring for informal caregivers. Additionally, AI solutions contribute to improve care coordination between formal and informal caregivers, that could lead to advanced telehealth assistance. However, limited research on assistive technologies like robots and mHealth apps suggests further exploration. Future AI-based solutions and assistive technologies (ATs) may benefit from a more targeted approach to appeasing specif

This paper discusses the results of an exploratory study aimed at investigating the impact of conversational agents (CAs) and specifically their agential characteristics on collaborative decision-making processes. The study involved 29 participants divided into 8 small teams engaged in a question-and-answer trivia-style game with the support of a text-based CA, characterized by two independent binary variables: personality (gentle and cooperative vs blunt and uncooperative) and gender (female vs male). A semi-structured group interview was conducted at the end of the experimental sessions to investigate the perceived utility and level of satisfaction with the CAs. Our results show that when users interact with a gentle and cooperative CA, their user satisfaction is higher. Furthermore, female CAs are perceived as more useful and satisfying to interact with than male CAs. We show that group performance improves through interaction with the CAs, confirming that a stereotype favoring the female with a gentle and cooperative personality combination exists in regard to perceived satisfaction, even though this does not lead to greater perceived utility. Our study extends the current debate about the possible correlation between CA characteristics and human acceptance and suggests future research to investigate the role of gender bias and related biases in human-AI teaming.

Total hip (THA) and total knee (TKA) arthroplasty procedures have steadily increased over the past few decades, and their use is expected to grow further, mainly due to an increasing number of elderly patients. Cost-containment strategies, supporting a rapid recovery with a positive functional outcomes, high patient satisfaction, and enhanced patient reported outcomes, are needed. A Fast Track surgical procedure (FT) is a coordinated perioperative approach aimed at expediting early mobilization and recovery following surgery and, accordingly, shortening the length of hospital stay (LOS), convalescence and costs. In this view, rapid rehabilitation surgery optimizes traditional rehabilitation methods by integrating evidence-based practices into the procedure. The aim of the present study was to compare the effectiveness of Fast Track versus Care-as-Usual surgical procedures and pathways (including rehabilitation) on a mid-term patient-reported outcome (PROs), the SF12 (with regard both to Physical and Mental Scores), 3 months after hip or knee replacement surgery, with the use of Propensity score-matching (PSM) analysis to address the issue of the comparability of the groups in a non-randomized study. We were interested in the evaluation of the entire pathways, including the postoperative rehabilitation stage, therefore, we only used early home discharge as a surrogate to differentiate between the Fast Track and Care-as-Usual rehabilitation pathways. Our study shows that the entire Fast Track pathway, which includes the post-operative rehabilitation stage, has a significantly positive impact on physical health-related status (SF12 Physical Scores), as perceived by patients 3 months after hip or knee replacement surgery, as opposed to the standardized program, both in terms of the PROs score and the relative improvements observed, as compared with the minimum clinically important difference. This result encourages additional research into the effects of Fast Track reha

Dementia refers to a group of neurodegenerative disorders that impact the cognitive function of an increasing number of individuals. Because of the variety of manifestations, the idea of mixed dementia has recently garnered increased awareness and attention from the scientific community. In this work, we describe a high-quality dataset, as well as the findings of a preliminary analysis devoted to investigating the potential of computational methods that are highly indicative of mixed dementia. We will specifically describe the findings of a phenotypic stratification analysis, based on clustering approaches, that highlights possibly significant aspects of mixed dementia, paving the way for further research devoted to the application of Machine Learning techniques to the robust and early diagnosis of mixed dementia.

Due to aging demographics, prolonged life expectancy, and chronic diseases, European societies' increasing need for care services has led to a shift towards informal care supplied by family members, friends, or neighbors. However, the progressive decrease in the caregiver-to-patient ratio will result in a significant augmentation in incorporating intelligent aid within general care. This study aimed to build upon the authors' previous systematic literature review on technologies for informal caregivers. Specifically, it focused on analyzing AI-based solutions to understand the advantages and challenges of using AI in decision-making support for informal caregivers in elderly care. Three databases (Scopus, IEEE Xplore, ACM Digital Libraries) were searched. The search yielded 1002 articles, with 24 that met the inclusion and exclusion criteria. Within the scope of this study, we will exclusively concentrate on a subset of 11 papers on AI technologies. The study reveals that AI-based solutions have great potential for real-time analysis advancement, explainable AI enhancement, and meta-information semantic refinement. While digital assistants can personalize information for caregivers, security and privacy are key concerns. The rise of more integrated and complicated solutions reveals that these technologies suit aging monitoring and informal care coordination in emergencies or deviations from usual activities. Informal caregiver decision assistance can be improved in this scenario.

Despite the rapid development in very recent years of Artificial Intelligence models to predict poverty, this problem still remains an unsolved open issue especially in a multidimensional perspective. In this work we present our proposal to face multidimensional poverty in case of a fragile population, the older adults, starting from an unlabelled dataset, collected administering a proper questionnaire to about 500 individuals. Firstly a model that allows to label the collected data into three classes of poverty is proposed. Then, XGBoost and Naive Bayes classifiers are considered to solve the classification problem. Finally, after having determined the relative importance of each feature, a novel Naive Bayes model is proposed that relies on new aggregated features that represent five poverty dimensions. These aggregated features are obtained by properly combining the variables collected through the questionnaire with cut-offs defined by a domain expert.

Background: Change is an ongoing process in any organizations. Over years, healthcare organizations have been exposed to multiple external stimuli to change (eg, ageing population, increasing incidence of chronic diseases, ongoing Sars-Cov-2 pandemic) that pointed out the need to convert the current healthcare organizational model. Nowadays, the topic is extremely relevant, rendering organizational change an urgency. The work is structured on a double level of analysis. In the beginning, the paper collects the overall literature on the topic of organisational change in order to identify, on the basis of the citation network, the main existing theoretical approaches. Secondly, the analysis attempts to isolate the scientific production related to the healthcare context, by analysing the body of literature outside the identified citation network, divided by clusters of related studies. Methodology: This review adopted a quantitative-based method that employs jointly systematic literature review and bibliographic network analysis. Specifically, the study applied a citation network analysis (CNA) and a co-occurrence keywords analysis. The CNA allowed detecting the most relevant papers published over time, identifying the research streams in literature. Results: The study showed four main findings. Firstly, consistent with past studies, works reviewed pointed out a convergence on the micro-level perspective for change’s analysis. Secondly, an organic viewpoint whereby individual, organization and change’s outcome contribute to any organizational change’s action has been found in its early stage. Thirdly, works reported change combined with innovation’s concept, although the structure of the relationship has not been outlined. Fourth, interestingly, contributions have been limited within the healthcare context. Conclusion: Human dimension is the primary criticality to be managed to impede failure of the re-organizational path. Individuals are not passive recipients of chan

Objective In vitro diagnostic tests for SARS-COV-2, also known as serological tests, have rapidly spread. However, to date, mostly single-center technical and diagnostic performance's assessments have been carried out without an intralaboratory validation process and a health technology assessment (HTA) systematic approach. Therefore, the rapid HTA for evaluating antibody tests for SARS-COV-2 was applied. Methods The use of rapid HTA is an opportunity to test innovative technology. Unlike traditional HTA (which evaluates the benefits of new technologies after being tested in clinical trials or have been applied in practice for some time), the rapid HTA is performed during the early stages of developing new technology. A multidisciplinary team conducted the rapid HTA following the HTA Core Model® (version 3.0) developed by the European Network for Health Technology Assessment. Results The three methodological and analytical steps used in the HTA applied to the evaluation of antibody tests for SARS-COV-2 are reported: the selection of the tests to be evaluated; the research and collection of information to support the adoption and appropriateness of the technology; and the preparation of the final reports and their dissemination. Finally, the rapid HTA of serological tests for SARS-CoV-2 is summarized in a report that allows its dissemination and communication. Conclusions The rapid-HTA evaluation method, in addition to highlighting the characteristics that differentiate the tests from each other, guarantees a timely and appropriate evaluation, becoming a tool to create a direct link between science and health management.

Patient-reported outcome Measures (PROMs) are validated questionnaires or self-report instruments of the perception of patients about their own health status in response to a medical intervention. In the era of patient-centred care, consideration about the effectiveness of a medical treatment should be grounded not only on the physicians' assessment but also on the increase in PROM scores that is perceived as relevant by the patients, namely the minimum clinically important differences (MCID). In this study, we collected data from the IRCCS Galeazzi Orthopaedic Institute (IOG) of Milan, Italy, to develop a preoperative machine learning model predicting the non-achievement of the MCID threshold in specific PROM scores 6 months after spine surgery, namely the Oswestry Disability Index (ODI) and the Physical Score of the Short Form 36 (SF-36). At IOG, nearly 39% of spinal surgeries do not achieve a minimum clinically important improvement and, of these, 22% are associated with negative outcomes. This is mainly due to the fact that IOG is a tertiary healthcare facility that receives the most critical cases from a vast territory (practically from all over the country) and it is known that spinal deformities and other related problems are difficult to solve, especially in an aging population. In this view, it is important to early identify those patients who will likely not benefit from treatment and, therefore, will not reach the MCID. This would help avoid overdiagnosis, reduce overuse and the related costs for unnecessary treatment, as well as optimise the allocation of resources and support more appropriate choices in shared decision-making.

The rise of personalized medicine and its remarkable advancements have revealed new requirements for the availability of appropriate medical decision-making models. Computer science is an area that plays an essential role in the field of personalized medicine, where one of the goals is to provide algorithms and tools to extrapolate knowledge and improve the decision-support process. The minimum clinically important difference (MCID) is the smallest change in PROM scores that patients perceive as meaningful. Treatment that does not achieve the minimum level of improvement is considered inappropriate as well as a potential waste of resources. Using the MCID threshold to identify patients who fail to achieve the minimum change in PROM that results in a meaningful outcome may aid in pre-surgical shared decision-making. The decision tree algorithm is a method for extracting valuable information and providing further meaningful information to the domain expert that supports the decision-making. In the present study, different tools based on machine learning were developed. On the one hand, we compared three XGBoost models to predict the non-achievement of the MCID at six months post-operation in the SF-12 physical score. The prediction score threshold was set to 0.75 to provide three decision-making areas on the basis of the high confidence (HC) intervals; the minority class was re-balanced by weighting the positive class to penalize the loss function (XGBoost cost-sensitive), oversampling the minority class (XGBoost with SMOTE), and re-sampling the negative class (XGBoost with undersampling). On the other hand, we modeled the data through a decision tree (assessment tree), based on different complexity levels, to identify the hidden pattern and to provide a new way to understand possible relationships between the gathered features and the several outcomes. The results showed that all the proposed models were effective as binary classifiers, as they showed moderate predic

One of the next frontiers in medical research, particularly in orthopaedic surgery, is personalized treatment outcome prediction. In personalized medicine, treatment choices are adjusted for the patient based on the individual’s and their disease’s distinct features. A high-value and patient-centered health care system requires evaluating results that integrate the patient’s viewpoint. Patient-reported outcome measures (PROMs) are widely used to shed light on patients’ perceptions of their health status after an intervention by using validated questionnaires. The aim of this study is to examine whether meteorological or light (night vs. day) conditions affect PROM scores and hence indirectly affect health-related outcomes. We collected scores for PROMs from questionnaires completed by patients (N = 2326) who had undergone hip and knee interventions between June 2017 and May 2020 at the IRCCS Orthopaedic Institute Galeazzi (IOG), Milan, Italy. Nearest neighbour propensity score (PS) matching was applied to ensure the similarity of the groups tested under the different weather-related conditions. The exposure PS was derived through logistic regression. The data were analysed using statistical tests (Student’s t-test and Mann−Whitney U test). According to Cohen’s effect size, weather conditions may affect the scores for PROMs and, indirectly, health-related outcomes via influencing the relative humidity and weather-related conditions. The findings suggest avoiding PROMs’ collection in certain conditions if the odds of outcome-based underperformance are to be minimized. This would ensure a balance between costs for PROMs’ collection and data availability.

The current gold standard for COVID-19 diagnosis, the rRT-PCR test, is hampered by long turnaround times, probable reagent shortages, high false-negative rates and high prices. As a result, machine learning (ML) methods have recently piqued interest, particularly when applied to digital imagery (X-rays and CT scans). In this review, the literature on ML-based diagnostic and prognostic studies grounded on hematochemical parameters has been considered. By doing so, a gap in the current literature was addressed concerning the application of machine learning to laboratory medicine. Sixty-eight articles have been included that were extracted from the Scopus and PubMed indexes. These studies were marked by a great deal of heterogeneity in terms of the examined laboratory test and clinical parameters, sample size, reference populations, ML algorithms, and validation approaches. The majority of research was found to be hampered by reporting and replicability issues: only four of the surveyed studies provided complete information on analytic procedures (units of measure, analyzing equipment), while 29 provided no information at all. Only 16 studies included independent external validation. In light of these findings, we discuss the importance of closer collaboration between data scientists and medical laboratory professionals in order to correctly characterise the relevant population, select the most appropriate statistical and analytical methods, ensure reproducibility, enable the proper interpretation of the results, and gain actual utility by using machine learning methods in clinical practice.

Il saggio introduce il panel dedicato alla sostenibilità e alla governance del patrimonio culturale in termini di nuovi approcci e nuovi paradigmi che richiamano a diverse e più complesse responsabilità pubbliche e private

Il saggio evidenzia l'incapacità di agire in chiave di sistema rispetto al tema del patrimonio culturale, evidenziandone le contraddizioni e riaffermando invece i vantaggi operando con diverse consapevolezze e responsabilità.