No abstract available.
Decision Support Techniques* ; Stroke*

OBJECTIVES: This paper proposes a measurement method to quantify the abnormal characteristics of the broken parts of ribs using local texture and shape features in chest radiographs. METHODS: Our measurement method comprises two steps: a measurement area assignment and sampling step using a spline curve and sampling lines orthogonal to the spline curve, and a fracture-ness measurement step with three measures, asymmetry and gray-level co-occurrence matrix based measures (contrast and homogeneity). They were designed to quantify the regional shape and texture features of ribs along the centerline. The discriminating ability of our method was evaluated through region of interest (ROI) analysis and rib fracture classification test using support vector machine. RESULTS: The statistically significant difference was found between the measured values from fracture and normal ROIs; asymmetry (p < 0.0001), contrast (p < 0.001), and homogeneity (p = 0.022). The rib fracture classifier, trained with the measured values in ROI analysis, detected every rib fracture from chest radiographs used for ROI analysis, but it also classified some unbroken parts of ribs as abnormal parts (8 to 17 line sets; length of each line set, 2.998 +/- 2.652 mm; length of centerlines, 131.067 +/- 29.460 mm). CONCLUSIONS: Our measurement method, which includes a flexible measurement technique for the curved shape of ribs and the proposed shape and texture measures, could discriminate the suspicious regions of ribs for possible rib fractures in chest radiographs.
Decision Support Techniques ; Rib Fractures ; Ribs ; Thorax

The clinical decision analysis (CDA) has used to overcome complexity and uncertainty in medical problems. The CDA is a tool allowing decision-makers to apply evidence-based medicine to make objective clinical decisions when faced with complex situations. The usefulness and limitation including six steps in conducting CDA were reviewed. The application of CDA results should be done under shared decision with patients' value.
Decision Support Techniques* ; Decision Trees* ; Evidence-Based Medicine ; Uncertainty

OBJECTIVES: The aim of this study was to develop a decision support model for the prediction of occlusal force from the size and color of articulating paper markings in bruxism patients. METHODS: We used the information from the datasets of 30 bruxism patients in which digital measurements of the size and color of articulating paper markings (12-µm Hanel; Coltene/Whaledent GmbH, Langenau, Germany) on canine protected hard stabilization splints were measured in pixels (P) and in red (R), green (G), and blue (B) values using Adobe Photoshop software (Adobe Systems, San Jose, CA, USA). The occlusal force (F) was measured using T-Scan III (Tekscan Inc., South Boston, MA, USA). The multiple regression equation was applied to predict F from the P and RGB. Model evaluation was performed using the datasets from 10 new patients. The patient's occlusal force measured by T-Scan III was used as a ‘gold standard’ to compare with the occlusal force predicted by the multiple regression model. RESULTS: The results demonstrate that the correlation between the occlusal force and the pixels and RGB of the articulating paper markings was positive (F = 1.62×P + 0.07×R –0.08×G + 0.08×B + 4.74; R 2 = 0.34). There was a high degree of agreement between the occlusal force of the patient measured using T-Scan III and the occlusal force predicted by the model (kappa value = 0.82). CONCLUSIONS: The results obtained demonstrate that the multiple regression model can predict the occlusal force using the digital values for the size and color of the articulating paper markings in bruxism patients.
Bite Force* ; Bruxism* ; Dataset ; Decision Making ; Decision Support Systems, Clinical* ; Decision Support Techniques ; Humans ; Logistic Models ; Occlusal Splints ; Splints

The combination of the analytical hierarchy process (AHP) and Delphi method can overcome the strong subjectivity and poor authority in the simple use of AHP, get rid of the shackles of established thinking and take fully advantages of the experiences of experts' knowledge. By a set of quantitative calculation method, we can determine the relative importance of each factor or the relative weight of the order value, thus providing the support for clinical decision making. In this article, on the basis of the combination of AHP and Delphi method, the authors explore the Chinese medicine etiology of coronary heart disease.
Biomedical Research ; Decision Support Techniques ; Medicine, Chinese Traditional ; methods ; Software

With the current overdiagnosis of thyroid cancer resulting from routine screening in Korea, it is necessary to educate the public that not all cancers are malignant. The exposure to patient decision aids (PtDAs) compared to usual care reduced the number of people choosing to undergo prostate-specific antigen screening. This article introduces the definition, usefulness, and developmental processes of PtDAs and suggests the urgent need for a Korean PtDA related to thyroid cancer screening.
Decision Support Systems, Clinical ; Decision Support Techniques* ; Early Detection of Cancer ; Humans ; Korea ; Mass Screening ; Prostate-Specific Antigen ; Thyroid Neoplasms

This paper deals with the problem of improving the capability of the medical decision support system (MDSS) for diagnosing nasal allergy by integrating the previously developed expert system with the neural network approach. Three knowledge acquisition methods were used to develop the expert system: statistical, rule-based, and the combined approach. Among the three, a combined approach showed the best prediction rate based on discriminant analysis. Using the results of a combined approach as input values, the neural network was developed using back-propagation method. Unlike the expert system, the neural network system provides the resulting allergy status in probabilistic terms. Managerial as well as legal issues were also discussed in this paper.
*Decision Support Techniques ; Hay Fever/*diagnosis ; Human ; Rhinitis, Allergic, Perennial/*diagnosis ; Support, Non-U.S. Gov't

Septic arthritis and gout are major diseases that should be suspected in patients with acute monoarthritis. These two diseases are clinically similar and often indistinguishable without the help of synovial fluid analysis. Recently, a novel diagnostic rule for gout without synovial fluid analysis was developed and showed relevant performances. This study aimed to determine whether this diagnostic rule could perform well in distinguishing gout from septic arthritis. The diagnostic rule comprises 7 clinical and laboratory variables, each of which is given a specified score. The probability of gout is classified into 3 groups according to the sum of the scores: high (> or = 8), intermediate (> 4 to < 8) and low probability (< or = 4). In this retrospective study, we applied this diagnostic rule to 136 patients who presented as acute monoarthritis and were subsequently diagnosed as acute gout (n = 82) and septic arthritis (n = 54) based on synovial fluid analysis. The mean sum of scores of acute gout patients was significantly higher than that of those with septic arthritis (8.6 +/- 0.2 vs. 3.6 +/- 0.32, P < 0.001). Patients with acute gout had significantly more 'high', and less 'low' probabilities compared to those with septic arthritis (Eta[eta]: 0.776). The prevalence of acute gouty arthritis, as confirmed by the presence of monosodium crystal, was 95.5% (61/64), 57.5% (19/33), and 5.1% (2/39) in high, intermediate and low probability group, respectively. The recently introduced diagnostic rule properly discriminates acute gout from septic arthritis. It may help physicians diagnose gout in cases difficult to be differentiated from septic arthritis.
Acute Disease ; *Algorithms ; Arthritis, Gouty/*diagnosis ; Arthritis, Infectious/*diagnosis ; *Data Interpretation, Statistical ; *Decision Support Techniques ; Diagnosis, Computer-Assisted/*methods ; Diagnosis, Differential ; Female ; Humans ; Male ; Middle Aged ; Reproducibility of Results ; Sensitivity and Specificity

As advance care planning is taking center stage in the field of end-of-life care, various tools have been developed to aid in the often emotional and difficult decision-making process. Video decision support tools are one of the most promising means of assistance, of which the modus operandi is to provide more comprehensive and precise information of medical procedures to patients and their families, allowing them to make better informed decisions. Despite such value, some are concerned about its potential negative impact. For example, video footages of some procedures may be shocking and unpalatable to non-medical professionals, and patients and families may refuse the procedures. One approach to soften the sometimes unpleasant visual of medical procedures is to show less aggressive or more relaxing scenes. Yet another potential issue is that the objectivity of video decision support tools might be vulnerable to the very stakeholders who were involved in the development. Some might argue that having multiple stakeholders may function as checks and balances and provide collective wisdom, but we should provide more systematic guarantee on the objectivity of the visual decision aids. Because the decision of the modality of an individual's death is the last and most significant choice in one's life, no party should exert their influence on such a delicate decision. With carefully designed video decision support tools, our patients will live the last moments of their lives with dignity, as they deserve.
Advance Care Planning* ; Decision Making ; Decision Support Systems, Clinical ; Decision Support Techniques ; Hope* ; Humans ; Lifting* ; Nimodipine ; Palliative Care ; Shock ; Terminal Care ; Videotape Recording

In this paper, a systematic architecture of medical data mining based on computer was provided for epidemiological analysis. Complications in diabetes mellitus were used as the cases under discussions on redundancy elimination, normalized storage, knowledge induction and visual expression of medical data. 3022 pieces of census records from Tianjin General Hospital were researched to find the solution of quantitative mining from qualitative data and knowledge discovery. From the qualitative data mining of 43 kinds of complications in diabetes mellitus, we found 18 knowledge rules with significant statistical meaning on concurrency relation, e. g. hyperlipoidemia, coronary disease, hypertension and cerebrovascular disease. And knowledge tree was noted to be an effective visual expression method for showing the rules generated from the above system. Medical analysis system based on data mining and knowledge discovery could generate effective knowledge rules from medical record database, which was found to be especially useful for epidemiological analysis and national health survey. So how to cooperate with community medical care and hospital information system in the near future is practically significant.
Automatic Data Processing ; China ; Decision Support Systems, Clinical ; Decision Support Techniques ; Decision Trees ; Diabetes Complications ; epidemiology ; Humans ; Medical Records Systems, Computerized