Multi-variable function approximation
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Sum fraction terms can approximate multi-variable functions on the basis of discrete observations, replacing a partial differential equation definition with polynomial elementary data relation descriptions. Artificial neural networks commonly transform the weighted sum of inputs to describe overall similarity relationships of trained and new testing input patterns. Differential polynomial neural networks form a new class of neural networks, which construct and solve an unknown general partial differential equation of a function of interest with selected substitution relative terms using non-linear multi-variable composite polynomials. The layers of the network generate simple and composite relative substitution terms whose convergent series combinations can describe partial dependent derivative changes of the input variables. This regression is based on trained generalized partial derivative data relations, decomposed into a multi-layer polynomial network structure. The sigmoidal function, commonly used as a nonlinear activation of artificial neurons, may transform some polynomial items together with the parameters with the aim to improve the polynomial derivative term series ability to approximate complicated periodic functions, as simple low order polynomials are not able to fully make up for the complete cycles. The similarity analysis facilitates substitutions for differential equations or can form dimensional units from data samples to describe real-world problems.
BACKGROUND: The ongoing "Sarcopenia and Physical fRailty IN older people: multi-componenT Treatment strategies (SPRINTT)" randomized controlled trial (RCT) is testing the efficacy of a multicomponent intervention in the prevention of mobility disability in older adults with physical frailty & sarcopenia (PF&S). Here, we describe the procedures followed for PF&S case finding and screening of candidate participants for the SPRINTT RCT. We also illustrate the main demographic and clinical characteristics of eligible screenees. METHODS: The identification of PF&S was based on the co-occurrence of three defining elements: (1) reduced physical performance (defined as a score on the Short Physical Performance Battery between 3 and 9); (2) low muscle mass according to the criteria released by the Foundation for the National Institutes of Health; and (3) absence of mobility disability (defined as ability to complete the 400-m walk test in 15 min). SPRINTT was advertised through a variety of means. Site-specific case finding strategies were developed to accommodate the variability across centers in catchment area characteristics and access to the target population. A quick "participant profiling" questionnaire was devised to facilitate PF&S case finding. RESULTS: During approximately 22 months, 12,358 prescreening interviews were completed in 17 SPRINTT sites resulting in 6710 clinic screening visits. Eventually, 1566 candidates were found to be eligible for participating in the SPRINTT RCT. Eligible screenees showed substantial physical function impairment and comorbidity burden. In most centers, project advertisement through mass media was the most rewarding case finding strategy. CONCLUSION: PF&S case finding in the community is a challenging, but feasible task. Although largely autonomous in daily life activities, older adults with PF&S suffer from significant functional impairment and comorbidity. This subset of the older population is therefore at high risk for disability and other negative health-related events. Key strategies to consider for successfully intercepting at-risk older adults should focus on mass communication methods.
- MeSH
- analýza nákladů a výnosů MeSH
- cvičení * MeSH
- křehký senior * MeSH
- kvalita života MeSH
- lidé MeSH
- omezení pohyblivosti * MeSH
- posuzování pracovní neschopnosti MeSH
- sarkopenie prevence a kontrola terapie MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- stárnutí MeSH
- úrazy pádem prevence a kontrola MeSH
- výběr pacientů * MeSH
- Check Tag
- lidé MeSH
- mužské pohlaví MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- klinické zkoušky, fáze III MeSH
- multicentrická studie MeSH
- práce podpořená grantem MeSH
- randomizované kontrolované studie MeSH
- Geografické názvy
- Itálie MeSH
