Minimum Set Of Attributes Research Articles

Advance Directives and Code Status Information Exchange: A Consensus Proposal for a Minimum Set of Attributes.

Documentation of code status and advance directives for end-of-life (EOL) care improves care and quality of life, decreases cost of care, and increases the likelihood of an experience desired by the patient and his/her family. However, the use of advance directives and code status remains low and only a few organizations maintain code status in electronic form. Members of the American Medical Informatics Association's Ethics Committee identified a need for a patient's EOL care wishes to be documented correctly and communicated easily through the electronic health record (EHR) using a minimum data set for the storage and exchange of code status information. After conducting an environmental scan that produced multiple resources, Ethics Committee members used multiple conference calls and a shared document to arrive at consensus on the proposed minimum data set. Ethics Committee members developed a minimum required data set with links to the HL7 C_CDA Advance Directives Module. Data categories include information on the organization obtaining the code status information, the patient, any supporting documentation, and finally the desired code status information including mandatory, optional, and conditional elements. The "minimum set of attributes" to exchange advance directive / code status data described in this manuscript enables communication of patient wishes across multiple providers and health care settings. The data elements described serve as a starting point for a dialog among informatics professionals, physicians experienced in EOL care, and EHR vendors, with the goal of developing standards for incorporating this functionality into the EHR systems.

Financial Education Programs in Colombia: Challenges in Assessing Their Effectiveness

Financial education programs enjoy widespread governmental and financial industry support. They are considered an important tool for improving financial literacy, encouraging financial inclusion, and increasing consumer financial protection. Therefore, assessing their effectiveness is important to guarantee that public and private resources are allocated wisely. As we highlight in this paper, the available empirical literature casts serious doubts on the effectiveness of those programs in achieving their main objectives. Even properly designed — from an impact evaluation viewpoint — financial education programs fail to deliver long-run effects on individuals’ financial literacy or financial choices. We highlight the challenges to evaluate the impact of financial education programs and, consequently, their merits. We showcase the international experience in assessing the effectiveness of these programs and draw lessons for Colombia. We offer a set of recommendations regarding the minimum set of attributes that financial education programs should have to allow serious policy evaluation.

Research on Relative Reducts of Rough Set Model in Consistent and Inconsistent Decision Tables

Z. Pawlak’s classical rough set theory has been widely applied in analyzing ordinary information systems and decision tables. In this theory, a relative reduct can be considered as a minimum set of attributes that preserves a certain classification property. This paper investigates three different classification properties, and proposes three distinct definitions accordingly. According to the three classification properties, we can define three distinct definitions respectively. Based on the common structure of the specific definitions of relative reducts and discernibility matrices, general definitions of relative reducts and discernibility matrices are suggested.

Relative reducts in consistent and inconsistent decision tables of the Pawlak rough set model

A relative reduct can be considered as a minimum set of attributes that preserves a certain classification property. This paper investigates three different classification properties, and suggests three distinct definitions accordingly. In the Pawlak rough set model, while the three definitions yield the same set of relative reducts in consistent decision tables, they may result in different sets in inconsistent tables. Relative reduct construction can be carried out based on a discernibility matrix. The study explicitly stresses a fact, that the definition of a discernibility matrix should be tied to a certain property. Regarding the three classification properties, we can define three distinct definitions accordingly. Based on the common structure of the specific definitions of relative reducts and discernibility matrices, general definitions of relative reducts and discernibility matrices are suggested.

Dynamic modular fuzzy neural classifier with tree-based structure identification

Inspired by a modular way of reasoning, we present a subsethood-product fuzzy neural classifier with a novel, robust and dynamic architecture, involving a main module and a number of submodules. The well-known classification and regression trees (CART) algorithm is employed as a fast preprocess of structure identification, which divides the input space into high certainty and low certainty regions, each representing a primary fuzzy rule. These primary fuzzy rules use a minimum set of attributes and are mapped onto the main neuro-fuzzy module. However, the patterns belonging to a low certainty primary rule get further split into a subset of secondary rules that use an extended set of attributes. Each such rule subset is mapped onto an expert-submodule, which gets activated only when a pattern falls into the respective low certainty region. In other words, we create a rule form of “if—then–if” conditional statement, where the first “IF” concerns the main module and the primary rule, while the second “IF” concerns the respective submodule and the secondary rule set. This dynamic resource-allocating model is optimized through a supervised learning procedure. Experiments in benchmark classification tasks prove that this architecture not only does reduce complexity and computational cost, which is its primary goal, but also offers fast and accurate processing during real-time operation. Moreover, it holds certain properties that make it ideal for soft computing applications of high dimension, especially those that adopt user-profiles or require partial re-training.