FRELSA: A dataset for frailty in elderly people originated from ELSA and evaluated through machine learning models.

BACKGROUND: Frailty is an age-related syndrome characterized by loss of strength and exhaustion and associated with multi-morbidity. Early detection and prediction of the appearance of frailty could help older people age better and prevent them from needing invasive and expensive treatments. Machine learning techniques show promising results in creating a medical support tool for such a task. METHODS: This study aims to create a dataset for machine learning-based frailty studies, using Fried's Frailty Phenotype definition. Starting from a longitudinal study on aging in the UK population, we defined a frailty label for each subject. We evaluated the definition by training seven different models for detecting frailty with data that were contemporary to the ones used for the definition. We then integrated more data from two years before to obtain prediction models with a 24-month horizon. Features selection was performed using the MultiSURF algorithm, which ranks all features in order of relevance to the detection or prediction task. RESULTS: We present a new frailty dataset of 5303 subjects and more than 6500 available features. It is publicly available, provided one has access to the original English Longitudinal Study of Ageing dataset. The dataset is balanced after grouping frailty with pre-frailty, and it is suitable for multiclass or binary classification and prediction problems. The seven tested architectures performed similarly, forming a solid baseline that can be improved with future work. Linear regression achieved the best F-score and AUROC in detection and prediction tasks. CONCLUSIONS: Creating new frailty-annotated datasets of this size is necessary to develop and improve the frailty prediction techniques. We have shown that our dataset can be used to study and test machine learning models to detect and predict frailty. Future work should improve models' architecture and performance, consider explainability, and possibly enrich the dataset with older waves.

Machine learning approaches for frailty detection, prediction and classification in elderly people: A systematic review.

BACKGROUND: Frailty in older people is a syndrome related to aging that is becoming increasingly common and problematic as the average age of the world population increases. Detecting frailty in its early stages or, even better, predicting its appearance can greatly benefit health in later years of life and save the healthcare system from high costs. Machine Learning models fit the need to develop a tool for supporting medical decision-making in detecting or predicting frailty. METHODS: In this review, we followed the PRISMA methodology to conduct a systematic search of the most relevant Machine Learning models that have been developed so far in the context of frailty. We selected 41 publications and compared them according to their purpose, the type of dataset used, the target variables, and the results they obtained, highlighting their shortcomings and strengths. RESULTS: The variety of frailty definitions allows many problems to fall into this field, and it is often challenging to compare results due to the differences in target variables. The data types can be divided into gait data, usually collected with sensors, and medical records, often in the context of aging studies. The most common algorithms are well-known models available from every Machine Learning library. Only one study developed a new framework for frailty classification, and only two considered Explainability. CONCLUSIONS: This review highlights some gaps in the field of Machine Learning applied to the assessment and prediction of frailty, such as the need for a universal quantitative definition. It emphasizes the need for close collaboration between medical professionals and data scientists to unlock the potential of data collected in hospital and clinical settings. As a suggestion for future work, the area of Explainability, which is crucial for models in medicine and health care, was considered in very few studies.

A Bayesian Argumentation Framework for Distributed Fault Diagnosis in Telecommunication Networks.

Traditionally, fault diagnosis in telecommunication network management is carried out by humans who use software support systems. The phenomenal growth in telecommunication networks has nonetheless triggered the interest in more autonomous approaches, capable of coping with emergent challenges such as the need to diagnose faults' root causes under uncertainty in geographically-distributed environments, with restrictions on data privacy. In this paper, we present a framework for distributed fault diagnosis under uncertainty based on an argumentative framework for multi-agent systems. In our approach, agents collaborate to reach conclusions by arguing in unpredictable scenarios. The observations collected from the network are used to infer possible fault root causes using Bayesian networks as causal models for the diagnosis process. Hypotheses about those fault root causes are discussed by agents in an argumentative dialogue to achieve a reliable conclusion. During that dialogue, agents handle the uncertainty of the diagnosis process, taking care of keeping data privacy among them. The proposed approach is compared against existing alternatives using benchmark multi-domain datasets. Moreover, we include data collected from a previous fault diagnosis system running in a telecommunication network for one and a half years. Results show that the proposed approach is suitable for the motivational scenario.

A Participatory Agent-Based Simulation for Indoor Evacuation Supported by Google Glass.

Indoor evacuation systems are needed for rescue and safety management. One of the challenges is to provide users with personalized evacuation routes in real time. To this end, this project aims at exploring the possibilities of Google Glass technology for participatory multiagent indoor evacuation simulations. Participatory multiagent simulation combines scenario-guided agents and humans equipped with Google Glass that coexist in a shared virtual space and jointly perform simulations. The paper proposes an architecture for participatory multiagent simulation in order to combine devices (Google Glass and/or smartphones) with an agent-based social simulator and indoor tracking services.

Electric deflection of middle-size ammonia clusters containing (E(-), Na(+)) pairs.

Effective polarizabilities of Na(NH3)n (n = 8-27) clusters were measured by electric deflection as a function of the particle size. A significant field-induced shift of the beam intensity profile without the occurrence of broadening revealed that the clusters behave as liquidlike polar objects in the conditions of the experiment (cluster temperatures were estimated in the range of 110-145 K). Most of the cluster polarity is attributed to the spontaneous promotion of the alkali atom valence electron to a diffuse state stabilized by the cluster solvent field, with the consequent formation of (e(-), Na(+)) pairs. The average modulus of the dipole of Na-NH3 clusters, µ0, was determined using the Langevin-Debye theory, and the data was compared with previous measurements obtained for Na-H2O clusters. Sodium-doped ammonia clusters exhibit much larger µ0 values and a step size dependence which is not present when the solvent is water. This evidence suggests that while the (e(-), Na(+)) structure is rather compact in Na(H2O)n clusters and remains almost unchanged during the solvation process, in Na(NH3)n the unpaired electron abandons the proximity of the Na(+) ion and gradually extends and occupies new solvent shells.

Electric susceptibility of sodium-doped water clusters by beam deflection.

The electric susceptibility of neutral sodium-doped water clusters Na(H(2)O)(N), N = 6-33, was determined by beam electric deflection. The clusters behave as polarizable particles; their intensity profiles exhibit global shifts toward the high-field region without the occurrence of broadening. In the conditions of the experiment, sodium-water clusters have a "floppy" structure and hence the electric susceptibility presents both electronic and orientacional terms. Measured susceptibilities are somewhat higher than those of pure water clusters, and the contribution per water molecule is similar for both cluster types.