• Asian Pacific Journal of Cancer Prevention
• /
• v.13 no.7
• /
• pp.3031-3035
• /
• 2012

Background: Though open surgical biopsy is the procedure of choice for the diagnosis of bone tumors, many disadvantages are associated with this approach. The present study was undertaken to evaluate the role of fine needle aspiration cytology (FNAC) as a diagnostic tool in cases of bony tumors and tumor-like lesions which may be conducted in centers where facilities for surgical biopsies are inadequate. Methods: The study population consisted of 51 cases presenting with a skeletal mass. After clinical evaluation, radiological correlation was done to assess the nature and extent of each lesion. Fine needle aspiration was performed aseptically and smears were prepared. Patients subsequently underwent open surgical biopsy and tissue samples were obtained for histopathological examination. Standard statistical methods were applied for analysis of data. Results: Adequate material was not obtained even after repeated aspiration in seven cases, six of which were benign. Among the remaining 44 cases, diagnosis of malignancy was correctly provided in 28 (93.3%) out of 30 cases and categorical diagnosis in 20 (66.67%). Interpretation of cytology was more difficult in cases of benign and tumor-like lesions, with a categorical opinion only possible in seven (50%) cases. Statistical analysis showed FNAC with malignant tumors to have high sensitivity (93.3%), specificity (92.9%) and positive predictive value of 96.6%, whereas the negative predictive value was 86.7%. Conclusion: FNAC should be included in the diagnostic workup of a skeletal tumor because of its simplicity and reliability. However, a definitive pathologic diagnosis heavily depends on compatible clinical and radiologic features which can only be accomplished by teamwork. The cytological technique applied in this study could detect many bone tumors and tumor-like conditions and appears particularly suitable as a diagnostic technique for rural regions of India as other developing countries.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.18 no.2
• /
• pp.323-338
• /
• 1994

This paper outlines a framework for performing intelligent sensor validation for a diagnostic expert system while reasoning under uncertainty. The emphasis is on the algorithmic preprocess technique. A companion paper focusses on heuristic post-processing. Sensor validation plays a vital role in the ability of the overall system to correctly detemine the state of a plant monitored by imperfect sensors. Especially, several theoretical developments were made in understanding uncertain sensory data in statistical aspect. Uncertain information in sensory values is represented through probability assignments on three discrete states, "high", "normal", and "low", and additional sensor confidence measures in Algorithmic Sv.Upper and lower warning limits are generated from the historical learning sets, which represents the borderlines for heat rate degradation generated in the Algorithmic SV initiates a historic data base for better reference in future use. All the information generated in the Algorithmic SV initiate a session to differentiate the sensor fault from the process fault and to make an inference on the system performance. This framework for a diagnostic expert system with sensor validation and reasonig under uncertainty applies in HEATXPRT$^{TM}$, a data-driven on-line expert system for diagnosing heat rate degradation problems in fossil power plants.

• Journal of Physiology & Pathology in Korean Medicine
• /
• v.25 no.1
• /
• pp.138-143
• /
• 2011

In spite of abundant clinical resources of stroke patients, the objective and logical data analyses or diagnostic systems were not established in oriental medicine. As a part of researches for standardization and objectification of differentiation of syndromes for stroke, in this present study, we tried to develop the statistical diagnostic tool discriminating the 4 subtypes of syndrome differentiation using the essential indices considering the sex. Discriminant analysis was carried out using clinical data collected from 1,448 stroke patients who was identically diagnosed for the syndrome differentiation subtypes diagnosed by two clinical experts with more than 3 year experiences. Empirical discriminant model(V) for different sex was constructed using 61 significant symptoms and sign indices selected by stepwise selection. We comparison. We make comparison a between discriminant model(V) and discriminant model(IV) using 33 significant symptoms and sign indices selected by stepwise selection. Development of statistical diagnostic tool discriminating 4 subtypes by sex : The discriminant model with the 24 significant indices in women and the 19 significant indices in men was developed for discriminating the 4 subtypes of syndrome differentiation including phlegm-dampness, qi-deficiency, yin-deficiency and fire-heat. Diagnostic accuracy and prediction rate of syndrome differentiation by sex : The overall diagnostic accuracy and prediction rate of 4 syndrome differentiation subtypes using 24 symptom and sign indices was 74.63%(403/540) and 68.46%(89/130) in women, 19 symptom and sign indices was 72.05%(446/619) and 70.44%(112/159) in men. These results are almost same as those of that the overall diagnostic accuracy(73.68%) and prediction rate(70.59%) are analyzed by the discriminant model(IV) using 33 symptom and sign indices selected by stepwise selection. Considering sex, the statistical discriminant model(V) with significant 24 symptom and sign indices in women and 19 symptom and sign indices in men, instead of 33 indices would be used in the field of oriental medicine contributing to the objectification of syndrome differentiation with parsimony rule.

• The Journal of Korean Medicine
• /
• v.29 no.1
• /
• pp.7-14
• /
• 2008

Purpose : The purpose of study was to develop generalized validity evaluation methods and terms for Sasang type diagnostic tests. Methods : A generalized statistical evaluation model for Sasang typology was suggested and generalized validity evaluation indices were proposed with this model. Results : The usefulness of validity evaluations, such as sensitivity and specificity values, were confirmed by the systematic review of the data from previously reported studies. Conclusion :Major obstacles in the multi-facet analysis and systematic review for Sasang type diagnostic tests were discussed with this test validity study.

• Tribology and Lubricants
• /
• v.17 no.2
• /
• pp.153-161
• /
• 2001

The development of techniques in diagnosing the state of the system is one of the essential tools in establishing the automation and unmanned manufacturing system for the realization of CIM/FMS in the fields. In this paper, we developed various diagnostic schemes for the journal bearing supported rotor system. Up to now, vibration of the shaft, measurement of the displacement and the temperature have been used for diagnostic tools, however, the statistical features only could not differentiate the state from states. Thus, we identified the sensor data f3r the steady state in the signal processing and then applied the fuzzy c-mean technology to cope with the nonlinear characteristics of the system. This will, in return, establish a possible diagnostic system for the rotor system in the fields.

• The Korean Journal of Applied Statistics
• /
• v.35 no.3
• /
• pp.435-443
• /
• 2022

In this paper, using Covid-19 diagnostic data provided by the Korea Disease Control and Prevention Agency (KDCA), we examine the probability of confirmed cases and the probability of actually being confirmed when the rapid test is negative according to the sensitivity and specificity of the rapid diagnostic kit. When we know the conditional probability of confirmation given a positive test, we induce the relationship between sensitivity and specificity, and compute the actual sensitivity of the rapid diagnosis kit based on the data of KDCA.

• Communications for Statistical Applications and Methods
• /
• v.9 no.2
• /
• pp.521-531
• /
• 2002

Various diagnostic techniques for identifying influential observations are mostly based on the deletion of a single observation. While such techniques can satisfactorily identify influential observations in many cases, they will not always be successful because of some mask effect. It is necessary, therefore, to develop techniques that examine the potentially influential effects of a subset of observations. The partial regression plots can be used to examine an influential observation for a single parameter in multiple linear regression. However, it is often desirable to detect influential observations for a subset of regression parameters when interest centers on a selected subset of independent variables. Thus, we propose a diagnostic measure which deals with detecting influential observations on a subset of regression parameters. In this paper, we propose a measure M, which can be effectively used for the detection of influential observations on a subset of regression parameters in multiple linear regression. An illustrated example is given to show how we can use the new measure M to identify influential observations on a subset of regression parameters.

• Communications for Statistical Applications and Methods
• /
• v.20 no.3
• /
• pp.217-223
• /
• 2013

Two influence diagnostic methods for the common mean model are proposed. First, an investigation of the influence of observations according to minor perturbations of the common mean model is made by adapting the local influence method which is based on the likelihood displacement. It is well known that the maximum likelihood estimates are in general sensitive to influential observations. Case-deletions can be a candidate for detecting influential observations. However, the maximum likelihood estimators are iteratively computed and therefore case-deletions involve an enormous amount of computations. An approximation by Newton's method to the maximum likelihood estimator obtained after a single observation was deleted can reduce much of computational burden, which will be treated in this work. A numerical example is given for illustration and it shows that the proposed diagnostic methods can be useful tools.

• Communications for Statistical Applications and Methods
• /
• v.23 no.3
• /
• pp.231-239
• /
• 2016

A graphical diagnostic method based on multiple case deletions in a regression context is introduced by using the sampling distribution of the difference between two least squares estimators with and without multiple cases. Principal components analysis plays a key role in deriving this diagnostic method. Multiple case deletions of test statistic are also considered when a new observation is fitted to a given regression model. The result is useful for detecting influential observations in econometric data analysis, for example in checking whether the consumption pattern at a later time is the same as the one found before or not, as well as for investigating the influence of cases in the usual regression model. An illustrative example is given.

• Journal of Korean Medical Science
• /
• v.33 no.52
• /
• pp.338.1-338.5
• /
• 2018

The purpose of this study was to develop and evaluate psychometrically the Korean version of the Primary Care Posttraumatic Stress Disorder Screen for the Diagnostic and Statistical Manual-fifth edition (K-PC-PTSD-5). In total, 252 participants were interviewed with the Structured Clinical Interview for Diagnostic and Statistical Manual-fifth edition-research version (SCID-5-RV). The K-PC-PTSD-5 showed good internal consistency (${\alpha}=0.872$), test-retest reliability (r = 0.89), and concurrent validity (r = 0.81). A score of 3 was identified as the threshold for clinically significant posttraumatic stress disorder (PTSD) symptoms. Overall, the results indicate that the K-PC-PTSD-5 is a useful, timesaving instrument for screening PTSD symptoms.