Assessment of the elderly’s functional ability to manage theirmedication: a systematic literature review

Background The evaluation of the elderly’s ability to manage medication through the use of a validated tool can be a significant step in identifying inabilities and needs, with the objective of increasing their self-care skills, and promoting successful aging. Aim of the review To identify studies assessing the elderly’s functional ability to manage their own medication. Method For the search strategy, the PICO method was used: P—Population(elderly), I—Instruments (tools for assessing medication management ability), C—Context (community) and O—Outcomes (functional ability to manage medication). Thefinal search query was run in MEDLINE/PubMed,CINAHL Plus, ISI Web of Science and Scopus. The whole process was developed according to the PRISMA statement. Results The search retrieved 8051 records. In each screening stage, the selection criteria were applied to eliminate records where at least one of the exclusion criteria was verified. At the end of this selection, we obtained a total of 18 papers (17 studies). The results allow the conclusion to be drawn that studies use several different instruments, most of them not validated. The authors agree that medication management abilities decrease as cognitive impairment increases, even if a lot of studies assess only the physical dimension. DRUGS was the instrument most often used. Conclusion Older adults’ ability to manage their medication should be assessed using tools specifically built and validate for the purpose. DRUGS (which uses the real regimen taken by the elderly) was the most widely used assessment instrument in the screened studies.

An Artificial Intelligence Approach to Individuals' Age Assessment

A link between patterns of pelvic growth and human life history is supported by the finding that, cross-culturally, variation in maturation rates of female pelvis are correlated with variation in ages of menarche and first reproduction, i.e., it is well known that the human dimensions of the pelvic bones depend on the gender and vary with the age. Indeed, one feature in which humans appear to be unique is the prolonged growth of the pelvis after the age of sexual maturity. Both the total superoinferior length and mediolateral breadth of the pelvis continues to grow markedly after puberty, and do not reach adult proportions until the late teens years. This continuation of growth is accomplished by relatively late fusion of the separate centers of ossification that form the bones of the pelvis. Hence, in this work we will focus on the development of an intelligent decision support system to predict individual’s age based on a pelvis' dimensions criteria. Some basic image processing techniques were applied in order to extract the relevant features from pelvic X-rays, being the computational framework built on top of a Logic Programming approach to Knowledge Representation and Reasoning that caters for the handling of incomplete, unknown, or even self-contradictory information, complemented with a Case Base approach to computing.