Theoretical Perspective of Multi-Dividing Ontology Learning Trick in Two-Sample Setting

Zhu, Linli; Hua, Gang

doi:10.1109/access.2020.3041659

“…That is, instead of working with predefined clinical classes, the user would try to assess the impact of the transition using major themes or ontological groups from the descriptions of outlier maps. Many thematic analysis [ 24 ] and ontological learning methods [ 25 , 26 ] are applicable here. For demonstration purposes, a simple graph was constructed and patterns were examined using network algorithms [ 21 , 27 ] (see Fig.…”

Section: Discussionmentioning

confidence: 99%

Entropic measures of complexity in a new medical coding system

Niyirora

¹

2021

BMC Med Inform Decis Mak

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Background Transitioning from an old medical coding system to a new one can be challenging, especially when the two coding systems are significantly different. The US experienced such a transition in 2015. Objective This research aims to introduce entropic measures to help users prepare for the migration to a new medical coding system by identifying and focusing preparation initiatives on clinical concepts with more likelihood of adoption challenges. Methods Two entropic measures of coding complexity are introduced. The first measure is a function of the variation in the alphabets of new codes. The second measure is based on the possible number of valid representations of an old code. Results A demonstration of how to implement the proposed techniques is carried out using the 2015 mappings between ICD-9-CM and ICD-10-CM/PCS. The significance of the resulting entropic measures is discussed in the context of clinical concepts that were likely to pose challenges regarding documentation, coding errors, and longitudinal data comparisons. Conclusion The proposed entropic techniques are suitable to assess the complexity between any two medical coding systems where mappings or crosswalks exist. The more the entropy, the more likelihood of adoption challenges. Users can utilize the suggested techniques as a guide to prioritize training efforts to improve documentation and increase the chances of accurate coding, code validity, and longitudinal data comparisons.

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“…Apart from activities and techniques for ontology management, METHONTOLOGY summarizes separate steps for the developement of an ontology, like “ […] the specification, the conceptualization, the formalization, the implementation and the maintenance […] ” [ 15 ]. A theoretical perspective on ontologies was also considered [ 16 , 17 ].…”

Section: Methodsmentioning

confidence: 99%

OnTARi: an ontology for factors influencing therapy adherence to rehabilitation

Steiner

¹

,

Saalfeld

²

,

Elgert

³

et al. 2021

BMC Med Inform Decis Mak

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Background Adherence and motivation are key factors for successful treatment of patients with chronic diseases, especially in long-term care processes like rehabilitation. However, only a few patients achieve good treatment adherence. The causes are manifold. Adherence-influencing factors vary depending on indications, therapies, and individuals. Positive and negative effects are rarely confirmed or even contradictory. An ontology seems to be convenient to represent existing knowledge in this domain and to make it available for information retrieval. Methods First, a manual data extraction of current knowledge in the domain of treatment adherence in rehabilitation was conducted. Data was retrieved from various sources, including basic literature, scientific publications, and health behavior models. Second, all adherence and motivation factors identified were formalized according to the ontology development methodology METHONTOLOGY. This comprises the specification, conceptualization, formalization, and implementation of the ontology “Ontology for factors influencing therapy adherence to rehabilitation” (OnTARi) in Protégé. A taxonomy-oriented evaluation was conducted by two domain experts. Results OnTARi includes 281 classes implemented in ontology web language, ten object properties, 22 data properties, 1440 logical axioms, 244 individuals, and 1023 annotations. Six higher-level classes are differentiated: (1) Adherence, (2) AdherenceFactors, (3) AdherenceFactorCategory, (4) Rehabilitation, (5) RehabilitationForm, and (6) RehabilitationType. By means of the class AdherenceFactors 227 adherence factors, thereof 49 hard factors, are represented. Each factor involves a proper description, synonyms, possibly existing acronyms, and a German translation. OnTARi illustrates links between adherence factors through 160 influences-relations. Description logic queries implemented in Protégé allow multiple targeted requests, e.g., for the extraction of adherence factors in a specific rehabilitation area. Conclusions With OnTARi, a generic reference model was built to represent potential adherence and motivation factors and their interrelations in rehabilitation of patients with chronic diseases. In terms of information retrieval, this formalization can serve as a basis for implementation and adaptation of conventional rehabilitative measures, taking into account (patient-specific) adherence factors. OnTARi also enables the development of medical assistance systems to increase motivation and adherence in rehabilitation processes.

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“…More recent results on graph factors in various settings can be referred to Gao et al [3,4], Wang and Zhang and Zhou et al [5][6][7][8][9][10], and Zhu et al [11,12].…”

Section: Introductionmentioning

confidence: 99%

Forbidden Restrictions and the Existence of $P_{\geq 2}$ -Factor and

Wu

¹

,

Yuan

²

,

Baskonus

³

et al. 2023

Journal of Function Spaces

0

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The existence of factor and fractional factor in network graph in various settings has raised much attention from both mathematicians and computer scientists. It implies the availability of data transmission and network segmentation in certain special settings. In our paper, we consider P ≥ 2 -factor and P ≥ 3 -factor which are two special cases of general H -factor. Specifically, we study the existence of these two kinds of path factor when some subgraphs are forbidden, and several conclusions on the factor-deleted graph, factor critical-covered graph, and factor uniform graph are given with regards to network parameters. Furthermore, we show that these bounds are best in some sense.

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Theoretical Perspective of Multi-Dividing Ontology Learning Trick in Two-Sample Setting

Cited by 4 publications

References 29 publications

Entropic measures of complexity in a new medical coding system

Entropic measures of complexity in a new medical coding system

OnTARi: an ontology for factors influencing therapy adherence to rehabilitation

Forbidden Restrictions and the Existence of $P_{\geq 2}$ -Factor and

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Theoretical Perspective of Multi-Dividing Ontology Learning Trick in Two-Sample Setting

Cited by 4 publications

References 29 publications

Entropic measures of complexity in a new medical coding system

Entropic measures of complexity in a new medical coding system

OnTARi: an ontology for factors influencing therapy adherence to rehabilitation

Forbidden Restrictions and the Existence of P ≥ 2 -Factor and

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Product

Resources

About

Forbidden Restrictions and the Existence of $P_{\geq 2}$ -Factor and