Consultation system for diagnosis of headache and facial pain: “Rhinos”

Matsumura, Yasushi; Matsunaga, Takashi; Maeda, Yusuke; Tsumoto, Shusaku; Matsumura, Hiroshi; Kimura, Michio

doi:10.1007/3-540-16479-0_28

Cited by 92 publications

(96 citation statements)

References 2 publications

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“…Second, inclusive reasoning suspects a disease in the output of the exclusive process when a patient has symptoms specific to a disease. Based on the discussion with medical experts, these reasoning processes are modeled as two kinds of rules, negative rules (or exclusive rules) and positive rules; the former corresponds to exclusive reasoning, the latter to inclusive reasoning [4]. 1 …”

Section: Focusing Mechanismmentioning

confidence: 99%

Granular formalization of medical diagnostic process

Tsumoto

Hirano

2015

2015 IEEE International Conference on Big Data (Big Data)

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This paper dicusses how to formalize medical diagnostic reasoing from the viewpoint of rule reasoning. Characteristics of rules shows that the rule model is closely related with rough set rule model. The important point is that medical diagnostic reasoning is characterized by focusing mechanism, composed of screening and differential diagnosis, which corresponds to upper approximation and lower approximation of a target concept in rough set theory. Furthremore, this paper focuses on detection of complications, which can be viewed as relations between rules of different diseases.

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Section: Focusing Mechanismmentioning

confidence: 99%

Granular formalization of medical diagnostic process

Tsumoto

Hirano

2015

2015 IEEE International Conference on Big Data (Big Data)

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“…Since the author was involved in developing an expert system for differential diagnosis of headache [3], he focused on the nature of description and frequency as shown in Section III. The informal history about how the author reached rough sets is shown in Appendix 2.…”

Section: B Classification Of Headachementioning

confidence: 99%

“…In this system, a diagnostic model proposed by Matsumura [3] is applied to the domain, which consists of the following three kinds of reasoning processes: exclusive reasoning, inclusive reasoning, and reasoning about complications.…”

Section: Appendix 1:rhinosmentioning

confidence: 99%

Information granules in medical differential diagnosis

Tsumoto

Hirano

2012

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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This paper discusses a correspondence between the core ideas of rough sets and medical differential diagnosis. Classically, a disease is defined as a set of symptoms, each of which gives the degree of confidence and coverage for the diagnosis. Diagnostic procedure mainly consists of the following three procedures: First, focusing mechanism (characterization) selects the candidates of differential diagnosis by using a set of symptoms. Secondly, additional set of symptoms make a differential diagnosis among the selected candidates. Finally, complications of other disease will be considered by symptoms which cannot be explained by the final candidates. This chapter mainly focuses on the first and second process and shows that thiese processes correponds to rules extracted by upper and lower approximation of supporting set of a given disease.

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“…Targeting is promoted by the high mobility of the soft gel particles in the smallest capillaries. As for other nanoparticles, targeting can be effected by the EPR effect (enhanced permeability and retention) observed for tumor tissue [6] and by decoration with specific ligands, (e.g., peptides and glucosides). In contrast to solid nanoparticles, hydrophilic nanogels can be designed to respond to changes in temperature and pH by a change in the degree of swelling.…”

Section: Introductionmentioning

confidence: 98%

Stimuli-Sensitive Microgels from Native Elastin: An Easy Approach for a Drug Release System

Singh

Topuz

Albrecht

et al. 2013

Hierarchical Macromolecular Structures: 60 Years After the Staudinger Nobel Prize II

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Thermo-and pH-responsive microgels were prepared from solubilized native elastin by crosslinking of the elastin lysine residues with poly(ethylene glycol) diglycidyl ether (PEG-DGE) and with bis(sulfosuccinimidyl) suberate (BS3). In the first case, a peptide-PEG conetwork was obtained whereas, in the second case, the elastin peptides were interlinked with hydrophobic bridges. The structure of the microgels was controlled by the ratio of crosslinker to elastin and by performing the crosslinking reaction in an inverse minielemulsion, yielding particles with a diameter in the submicron range. Depending on the degree of crosslinking, the hybrid microgels exhibited a volume change transition at 37 and 35.5 C and pH responsivity in the range of 5-7 for microgels crosslinked with PEG-DE and BS3, respectively. This temperature-and pH-responsive behavior can be assigned to the well-investigated coacervation of elastin peptides, demonstrating that the elastin functionality is abolished only by rather dense crosslinking. In spite of the broad distribution in the molecular weight of the elastin molecules, the microgels remained soluble. Light scattering and sedimentation experiments demonstrated that the coacervation occurred predominantly intramolecularly, i.e., by collapse in the core while the corona stabilized the colloidal dispersion against precipitation. Preliminary experiments were conducted to evaluate the suitability of these microgels for use as a drug-release system and demonstrated

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Consultation system for diagnosis of headache and facial pain: “Rhinos”

Cited by 92 publications

References 2 publications

Granular formalization of medical diagnostic process

Granular formalization of medical diagnostic process

Information granules in medical differential diagnosis

Stimuli-Sensitive Microgels from Native Elastin: An Easy Approach for a Drug Release System

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