Mapping longitudinal studies to risk factors in an ontology for dementia

Abstract

A common activity carried out by healthcare professionals is to test various hypotheses on longitudinal study data in an effort to develop new and more reliable algorithms that might determine the possibility of developing certain illnesses. The In-MINDD project provides input from a number of European dementia experts to identify the most accurate model of inter-related risk factors which can yield a personalised dementia risk quotient and profile. This model is then validated against the large population-based prospective Maastricht Aging Study (MAAS) dataset. As part of this overall goal, the research presented in this paper demonstrates how we can automate the process of mapping modifiable risk factors against large sections of the aging study and thus, use information technology to provide more powerful query interfaces.

Variable interactions in risk factors for dementia

“…The first element of developing this online system required the definition of a common vocabulary or ontology, in order to share knowledge between relevant stakeholders [18], [19]. Next a multi-factorial model of those elements that pertained to dementia-risk in middle-aged individuals was developed by dementia experts via a systematic literature review [6] and shared with the data scientists via the previously defined system ontology [18], [19]. The system ontology connects the IT and clinical researchers and allows both parties to validate the model -data scientists via the latest machine learning techniques and clinical researchers through more traditional statistical approaches.…”

Developing an ontology for representing the domain knowledge specific to non‐pharmacological treatment for agitation in dementia

“…have developed the mild cognitive impairment (MCI) ontology to assist physicians in diagnosing MCI efficiently. To assess the individual's risk of developing dementia, Roantree et al 38 . built the In‐MINDD ontology, which models the risk factors that can cause dementia.…”

Affine Transformation Based Ontology Sparse Vector Learning Algorithm

“…Acting as a concept semantic framework, ontology works high effectiveness and is widely employed in other engineering applications such as biology science, medical science, pharmaceutical science, material science, mechanical science and chemical science (for instance, see Coronnello et al [2], Vishnu et al. [3], Roantree et al [4], Kim and Park [5], Hinkelmann et al [6], Pesaranghader et al [7], Daly et al [8], Agapito et al [9], Umadevi et al [10] and Cohen [11]).…”