Risk Prediction Models for Depression in Community-Dwelling Older Adults.

OBJECTIVE: To develop streamlined Risk Prediction Models (Manto RPMs) for late-life depression. DESIGN: Prospective study. SETTING: The Survey of Health, Ageing and Retirement in Europe (SHARE) study. PARTICIPANTS: Participants were community residing adults aged 55 years or older. MEASUREMENTS: The outcome was presence of depression at a 2-year follow up evaluation. Risk factors were identified after a literature review of longitudinal studies. Separate RPMs were developed in the 29,116 participants who were not depressed at baseline and in the combined sample of 39,439 of non-depressed and depressed subjects. Models derived from the combined sample were used to develop a web-based risk calculator. RESULTS: The authors identified 129 predictors of late-life depression after reviewing 227 studies. In non-depressed participants at baseline, the RPMs based on regression and Least Absolute Shrinkage and Selection Operator (LASSO) penalty (34 and 58 predictors, respectively) and the RPM based on Artificial Neural Networks (124 predictors) had a similar performance (AUC: 0.730-0.743). In the combined depressed and non-depressed participants at baseline, the RPM based on neural networks (35 predictors; AUC: 0.807; 95% CI: 0.80-0.82) and the model based on linear regression and LASSO penalty (32 predictors; AUC: 0.81; 95% CI: 0.79-0.82) had satisfactory accuracy. CONCLUSIONS: The Manto RPMs can identify community-dwelling older individuals at risk for developing depression over 2 years. A web-based calculator based on the streamlined Manto model is freely available at https://manto.unife.it/ for use by individuals, clinicians, and policy makers and may be used to target prevention interventions at the individual and the population levels.

HABITAT: An IoT Solution for Independent Elderly.

In this work, a flexible and extensive digital platform for Smart Homes is presented, exploiting the most advanced technologies of the Internet of Things, such as Radio Frequency Identification, wearable electronics, Wireless Sensor Networks, and Artificial Intelligence. Thus, the main novelty of the paper is the system-level description of the platform flexibility allowing the interoperability of different smart devices. This research was developed within the framework of the operative project HABITAT (Home Assistance Based on the Internet of Things for the Autonomy of Everybody), aiming at developing smart devices to support elderly people both in their own houses and in retirement homes, and embedding them in everyday life objects, thus reducing the expenses for healthcare due to the lower need for personal assistance, and providing a better life quality to the elderly users.

Developing the FARSEEING Taxonomy of Technologies: Classification and description of technology use (including ICT) in falls prevention studies.

BACKGROUND: Recent Cochrane reviews on falls and fall prevention have shown that it is possible to prevent falls in older adults living in the community and in care facilities. Technologies aimed at fall detection, assessment, prediction and prevention are emerging, yet there has been no consistency in describing or reporting on interventions using technologies. With the growth of eHealth and data driven interventions, a common language and classification is required. OBJECTIVE: The FARSEEING Taxonomy of Technologies was developed as a tool for those in the field of biomedical informatics to classify and characterise components of studies and interventions. METHODS: The Taxonomy Development Group (TDG) comprised experts from across Europe. Through face-to-face meetings and contributions via email, five domains were developed, modified and agreed: Approach; Base; Components of outcome measures; Descriptors of technologies; and Evaluation. Each domain included sub-domains and categories with accompanying definitions. The classification system was tested against published papers and further amendments undertaken, including development of an online tool. Six papers were classified by the TDG with levels of consensus recorded. RESULTS: Testing the taxonomy with papers highlighted difficulties in definitions across international healthcare systems, together with differences of TDG members' backgrounds. Definitions were clarified and amended accordingly, but some difficulties remained. The taxonomy and manual were large documents leading to a lengthy classification process. The development of the online application enabled a much simpler classification process, as categories and definitions appeared only when relevant. Overall consensus for the classified papers was 70.66%. Consensus scores increased as modifications were made to the taxonomy. CONCLUSION: The FARSEEING Taxonomy of Technologies presents a common language, which should now be adopted in the field of biomedical informatics. In developing the taxonomy as an online tool, it has become possible to continue to develop and modify the classification system to incorporate new technologies and interventions.

FRAT-up, a Web-based fall-risk assessment tool for elderly people living in the community.

BACKGROUND: About 30% of people over 65 are subject to at least one unintentional fall a year. Fall prevention protocols and interventions can decrease the number of falls. To be effective, a prevention strategy requires a prior step to evaluate the fall risk of the subjects. Despite extensive research, existing assessment tools for fall risk have been insufficient for predicting falls. OBJECTIVE: The goal of this study is to present a novel web-based fall-risk assessment tool (FRAT-up) and to evaluate its accuracy in predicting falls, within a context of community-dwelling persons aged 65 and up. METHODS: FRAT-up is based on the assumption that a subject's fall risk is given by the contribution of their exposure to each of the known fall-risk factors. Many scientific studies have investigated the relationship between falls and risk factors. The majority of these studies adopted statistical approaches, usually providing quantitative information such as odds ratios. FRAT-up exploits these numerical results to compute how each single factor contributes to the overall fall risk. FRAT-up is based on a formal ontology that enlists a number of known risk factors, together with quantitative findings in terms of odds ratios. From such information, an automatic algorithm generates a rule-based probabilistic logic program, that is, a set of rules for each risk factor. The rule-based program takes the health profile of the subject (in terms of exposure to the risk factors) and computes the fall risk. A Web-based interface allows users to input health profiles and to visualize the risk assessment for the given subject. FRAT-up has been evaluated on the InCHIANTI Study dataset, a representative population-based study of older persons living in the Chianti area (Tuscany, Italy). We compared reported falls with predicted ones and computed performance indicators. RESULTS: The obtained area under curve of the receiver operating characteristic was 0.642 (95% CI 0.614-0.669), while the Brier score was 0.174. The Hosmer-Lemeshow test indicated statistical significance of miscalibration. CONCLUSIONS: FRAT-up is a web-based tool for evaluating the fall risk of people aged 65 or up living in the community. Validation results of fall risks computed by FRAT-up show that its performance is comparable to externally validated state-of-the-art tools. A prototype is freely available through a web-based interface. TRIAL REGISTRATION: ClinicalTrials.gov NCT01331512 (The InChianti Follow-Up Study); http://clinicaltrials.gov/show/NCT01331512 (Archived by WebCite at http://www.webcitation.org/6UDrrRuaR).

Risk Prediction Model for Late Life Depression: Development and Validation on Three Large European Datasets.

Assessing the risk to develop a specific disease is the first step towards prevention, both at individual and population levels. The development and validation of risk prediction models (RPMs) is the norm within different fields of medicine but still underused in psychiatry, despite the global impact of mental disorders. In particular, there is a lack of RPMs to assess the risk of developing depression, the first worldwide cause of disability and harbinger of functional decline in old age. We present the depression risk assessment tool DRAT-up, the first prospective RPM to identify late-life depression among community-dwelling subjects aged 60-75. The development of DRAT-up was based on appraisal of relevant literature, extraction of robust risk estimates, and integration into model parameters. A unique feature is the ability to estimate risk even in the presence of missing values. To assess the properties of DRAT-up, a validation study was conducted on three European cohorts, namely, the English Longitudinal Study of Ageing, the Invecchiare nel Chianti, and the Irish Longitudinal Study on Ageing, with 20 206, 1359, and 3124 eligible samples, respectively. The model yielded accurate risk estimation in the three datasets from a small number of predictors. The Brier scores were 0.054, 0.133, and 0.041, respectively, while the values of area under the curve (AUC) were 0.761, 0.736, and 0.768, respectively. Sensitivity analyses suggest robustness to missing values: setting any individual feature to unknown caused the Brier scores to increase by 0.004 and the AUCs to decrease by 0.045 in the worst cases. DRAT-up can be readily used for clinical purposes and to aid policy-making in the field of mental health.

Compliance checking of cancer-screening Careflows: an approach based on Computational Logic.

Clinical guidelines and Careflow systems have been recently identified as a means to improve and standardize health care services. A number of ICT-based management solutions have been proposed, focussing on several aspects such as specification, process logs verification with respect to specification (compliance), enactment and administration of careflows. In this paper we introduce the GPROVE framework, based on Computational Logic, and focused on the (formal) specification of careflows and on the compliance verification of the process executions w.r.t. the specified models. In particular, we show its application to the Cancer Screening Guideline used by the sanitary organization of the Emilia Romagna region, discussing its formalization in GPROVE and the results of the compliance checking applied to logs of the screening process.