Synthesis of hydrophilic and amphiphilic spin probes.
Explore the source record for details and available documents.
Cyclic N-Oxides↗
Biomedical subjects
A Babic
Publications and source records attributed to A Babic.
Cytological grading of breast cancer in Giemsa-stained fine needle aspiration smears.
A potential cytological nuclear grading based on a semi-quantitative evaluation of three basic nuclear features, size of cell nuclei, anisonucleosis and the proportion of nucleoli-containing-nuclei, was tested on 74 Giemsa-stained fine needle aspiration of breast smears for its reliability in establishing the malignant potential of breast cancer. The prognostic impact of DNA-ploidy and S-phase fraction was also assessed. A good correlation between the three basic nuclear features, DNA-ploidy, S-phase fraction, cytological nuclear grade and histological grade, was shown. Using the cytological nuclear grade proposed, correct classification of cases between low histological grade (HG I) and high histological grade (HG II + HG III) was achieved in 79.73%. A statistically significant difference in 5-year survival rate was also observed between low malignancy grade and high malignancy grade breast cancer patients, regardless of the grading method used. DNA-ploidy and S-phase fraction were not statistically significant in establishing the malignant potential of breast cancer.
Azure Stains↗
Liver guide for monitoring of chronic hepatitis C.
The severity of chronic hepatitis C infection in the individual patient is monitored using blood laboratory findings and liver biopsy. If blood test results could be shown to provide sufficient information concerning the disease, the invasive procedure of liver biopsy could perhaps be avoided in some instances. This study assessed the clinical relevance of blood laboratory tests for detecting disease-related changes in the liver. Histopathological classification was used to assign class membership of the patients and data mining operations were performed in an elaborate way on 19 different data sets. Disease activity could be detected by a small set of blood tests. Extended sets could identify more severe changes, but failed to distinguish them. The extracted rules are implemented as a part of the knowledge base of a corresponding decision support system aimed at specialists and general practitioners.
Analysis of Variance↗
Knowledge representation forms for data mining methodologies as applied in thoracic surgery.
Typical ways of disseminating and using results of clinical research are scientific journals and reports. Presentation forms are condensed and comprehensible mainly to the experts following the specific topics. A vast amount of information remains unutilized due to the complex form of presenting the knowledge. Subject of this research is to explore possibilities of representation and also visualization of the results obtained using data mining methodologies. The intention is to formulate more than scientific ways to communicate facts that are of interest for the clinicians, medical students and even patients. Internet technologies as already widely established media support knowledge representation forms such as hypertext documents and structured knowledge components. The "Assist Me" decision support system for surgical treatment of cardiac patients integrates several forms of data mining and representation methodologies. We are showing a feasibility study in which scientific outcomes were forwarded to a broad group of potential users.
Artificial Intelligence↗
Aspects of certainty in patient classification using a health-related quality-of-life instrument in inflammatory bowel disease.
The study has focused on deriving a certainty measure for the classification of disease activity in patients suffering from inflammatory bowel disease (IBD). The aim is to build an Internet-based health-related quality-of-life (HRQoL) questionnaire to continuously monitor a patient's condition. Data from 109 patients was collected four times in intervals of three months, using a standardized disease-specific quality-of-life questionnaire, the Rating Form of IBD Patient Concern (RFIPC), extended with 11 additional questions. Correlation analysis showed that the RFIPC items along with "general wellbeing" were highly correlated (significance < 0.001). Factor analysis confirmed this high correlation and only one factor was identified among those variables. Multivariate discriminant analysis was successful to 78.1% in classifying between cases of remission and relapse. Implementation of a smooth threshold function decreased the classification error. However, discrimination regarding change in disease activity over time has to be further improved.
Discriminant Analysis↗
Knowledge discovery for advanced clinical data management and analysis.
Knowledge discovery is a broad research field in which methods are developed to support discovery of novel and potentially useful knowledge from clinical databases and registers in systems for patient care. However, the techniques available are not readily applicable in medical domains, due to, among other reasons, low user friendliness and lack of proper methodological background. Data mining approaches to be explored and improved are predictive modelling, segmentation, dependency modelling, summarization, and change and deviation detection/modelling (in data or knowledge). Another and original contribution of the research is to build up efficient feedback loops. Human experts and available domain expert systems could provide suggestions as how to improve all major steps in the knowledge discovery process such as evaluation of knowledge, choice of data mining methods and data input. A long tradition of collecting and maintaining clinical and administrative data could be found in fields of oncology, cardiology, coronary surgery, social and primary health care medicine. All these areas, that gather data over long periods of time, could benefit from knowledge discovery.
Artificial Intelligence↗
Quantitative data analysis for exploring outcomes in cardiac surgery.
The article focuses on possibilities of statistical knowledge exploration to predict outcomes of surgical treatments. The outcomes were defined in relation to the measured peri- and intraoperative data, as well as follow-up patient questionnaire. Clinical consequences are expected in terms of a smaller data set with a better ability to predict the surgery outcomes and a better cost performance. The important questions that could discriminate quality of life (QoL) were: Relief from surgery?, Has cardiac surgery effected earlier symptoms? Work capacity? Consultations after the surgery? The performed data analysis proved to be efficient in the complex data set that was collected. Pain relief was identified to be significant, while relations between measured blood laboratory profile and later QoL were weak.
Aged↗
Quantitative collagen as a golden standard in differential diagnosing of fibrotic changes in liver tissue.
Determining a presence and degree of liver fibrosis provides means for diagnosing disease related processes. We have used two data mining methods, discriminant and regression analyses, to acquire knowledge from the data of 211 patients. We have shown and discussed that quantitative collagen has a distinguished discriminating power and can serve as a golden standard. We have additionally succeeded to obtain a formula consisting of standardised blood tests that can replace quantitative collagen. Practical implications of this is a non-invasive and cost efficient patient examination. All the results are now left for clinical evaluation and so is the current way of histopathological classifications.
Biopsy↗
Take care: guidelines for patients with chronic hepatitis C.
Alcohol consumption has significant impact on the condition of the liver, by itself, and even more in conjunction with other liver diseases such as chronic hepatitis C. Drinking habits might be delicate issues to address and could harm otherwise satisfying communication. Therefore, we intended to outline guidelines for advising hepatitis C patients concerning alcohol consumption. Analysis of a relatively limited knowledge base revealed the complexity of the disease rather than statistically significant findings regarding consumption. Thus, we instead chose to suggest a set of patient educational guidelines, which could be implemented on the Internet, hypothesizing that a better informed patient will be more able to comply with restrictions concerning alcohol consumption. A brief ad hoc evaluation pointed out Internet as a favourable media to present the information. We also suggest a tentative algorithm for further development of clinical decision support systems addressing monitoring of chronic hepatitis C patients.
Adolescent↗
Assessing an AI knowledge-base for asymptomatic liver diseases.
Discovering not yet seen knowledge from clinical data is of importance in the field of asymptomatic liver diseases. Avoidance of liver biopsy which is used as the ultimate confirmation of diagnosis by making the decision based on relevant laboratory findings only, would be considered an essential support. The system based on Quinlan's ID3 algorithm was simple and efficient in extracting the sought knowledge. Basic principles of applying the AI systems are therefore described and complemented with medical evaluation. Some of the diagnostic rules were found to be useful as decision algorithms i.e. they could be directly applied in clinical work and made a part of the knowledge-base of the Liver Guide, an automated decision support system.
Algorithms↗
Diagnostic systems for assessing alveolar bone loss.
Radiological diagnostics serves as a basic monitoring technique for alveolar bone loss which is a severe consequence of periodontal disease. To evaluate efficacy of Conventional Visual Radiography (CVR), and to assess a complete clinical status, we had used two more diagnostic systems. These are Digital Subtraction Radiography (DSR) and Probing Pocket Depth (PPD). Experimental Periodontitis was studied in 20 beagle dogs based on the measurements taken in the beginning (baseline), and before (11th month) and after the medical treatment (12th month). Data analyses pointed out the same clinical trend, i.e. a significant bone loss prior to medical treatment and its recovery to the initial state. Differences in metrics and measurement errors could be identified as causes for discrepancies between the systems, but a relationship between the CVR and PPD is worth of further research, as these systems do not appear to be entirely compatible, but rather complementary to each other.
Alveolar Bone Loss↗
Machine learning for the quality of life in inflammatory bowel disease.
Presence of a chronic disease influences patients' lives and reinforces demands to accept and then cope with the illness. In the case of inflammatory bowel disease, quality of life greatly differs through phases of remissions and relapses. Could the quality of life questionnaire tell the difference? In this study we are disclosing possibilities of assessing patients' perspectives by analysing analogue scale statements regarding concerns and worries related to ulcerative colitis. Some two hundred Swedish patients, 3/4 in remission and 1/4 in relapse, filled out a booklet containing 36 statements. To characterise the disease activity, we have used multivariate discrimination. To structure and describe in details paths distinguishing the remission from relapse, we have used an artificial intelligence procedure. Applications of the CART (Classification And Regression Trees) algorithm resulted in a set of classifiers which are, based on the similar subsets of significant variables, i.e. statements. Best reached classification accuracy did not exceed 80% in any case. Other classifiers namely, K-nearest-neighbour (KNN), Learning Vector Quantization (LVQ) and Back Propagation Neural Network (BPNN) confirmed that outcome. An expectation that the disease activity should clearly speak throughout the questionnaire held for a certain number of the observations such as pain and suffering, loss of bowel control, dying early, feeling alone, ability to have children, being treated as different and concerns regarding the medication. To highlight the difference of incorrect 20%, K-means clustering was performed. The results settled a basis for a hypothesis that the studied quality of life instrument captures more than the disease activity.
Algorithms↗
Hemopump treatment in patients with postcardiotomy heart failure.
BACKGROUND: This study examined the use of the Hemopump to treat low cardiac output syndrome after cardiopulmonary bypass. METHODS: We used the Hemopump temporary cardiac assist system in 29 patients with severe left ventricular dysfunction after open heart operations from September 1991 to November 1994. RESULTS: Five patients were excluded from the study due to initial patient/device-related problems. Ten patients died in the operating room or early during the stay in the intensive care unit due to progressive biventricular failure. Fourteen patients (58.3%) were weaned from the device, and all of them were later discharged. In a subgroup of patients (54%) in whom we had a more aggressive approach for early insertion of the pump, the survival rate was 85%. Preoperative Higging risk score was significantly related to survival. CONCLUSIONS: The Hemopump can effectively unload a failing left ventricle with preservation of multiorgan perfusion. A minor decrease in kidney function was observed in most patients, but none of the surviving patients needed hemodialysis. One patient required a short period of peritoneal dialysis to get rid of fluid overload. Hemolysis or platelet dysfunction was not a clinical problem.
Aged↗
Machine learning to support diagnostics in the domain of asymptomatic liver disease.
Machine learning procedures, in unsupervised and supervised manner, can enable their users to achieve knowledge hardly comprehensible by even the best experts. This is true also if the clinical knowledge has been carefully assembled in a prospective way. A data set including 165 patients with elevated routine laboratory tests was extensively studied according to clinical history, laboratory profile and liver biopsy. Unsupervised learning by Kohonen feature map disclosed 4 groups of patients: the largest one with no or slight histopathological changes (116) and three smaller, more homogenous, with more diseased patients. Standardized histopathological scorings of the liver specimens defined patients into two groups. Fifty-eight of them were, according to the analysis, recommended for a liver biopsy, due to more severe degrees of inflammation and fibrosis. One-hundred and seven of the patients, in whom liver biopsy was retrospectively considered unnecessary, had only minor degrees of inflammation, fibrosis and/or steatosis. Supervised learning, using the inductive systems based on Quinlan's ID3 and CART algorithms, extracted knowledge in the form of decision trees. This approach could define a need for biopsy either with a very few significant findings or by pathways containing quotients and multiplications of the different basic items. These procedures were analyzed and compared for their theoretical and applicative performances. The cluster and Fischerian discriminant analyses were performed in order to compare the classification performance. The medical appropriateness of the obtained results is satisfying, therefore decision support systems, outlined in this study, should be evaluated in wider clinical practice. To achieve this goal, an example of a Medical Logical Module (MLM), based on the Arden Syntax, is given.
Adult↗
Methods for knowledge extraction from a clinical database on liver diseases.
We performed exploratory data analysis (EDA) to examine the hidden structure in liver disease data. The purpose was to demonstrate the potential of statistical techniques for extracting knowledge from an active HIS (hospital information system) database with decision support. The goal is to give strong support to the creation of new rules or "tuning" of old rules in the knowledge base. This would facilitate utilization of large patient databases, now commonly available, to help build/update decision support systems for improved patient care. Several statistical techniques were investigated. Stepwise discriminant analysis was found to be a good method in discriminating among different disease classes. Results showed that classification strength of a few (3) variables was similar to all the available (19) variables. Other important issues in the work are treatment of missing values as well as atypical values in medical databases. In estimating missing values we utilized both statistical methods and artificial intelligence approaches. Both these approaches were promising in the estimation of missing values. The study showed that several statistical approaches are possible for knowledge extraction from clinical data collected retrospectively.
Artificial Intelligence↗
Integrated approach for designing medical decision support systems with knowledge extracted from clinical databases by statistical methods.
In clinical research data is often studied by a particular method without previous analysis of quality or semantic contents which could link clinical database and data analytical (e.g. statistical) procedures. In order to avoid bias caused by this situation, we propose that the analysis of medical data should be divided into two main steps. In the first one we concentrate on conducting the quality, semantic and structure analyses. In the second step our aim is to build an appropriate dictionary of data analysis methods for further knowledge extraction. Methods like robust statistical techniques, procedures for mixed continuous and discrete data, fuzzy linguistic approach, machine learning and neural networks can be included. The results may be evaluated both using test samples and applying other relevant data-analytical techniques to the particular problem under the study.
Artificial Intelligence↗
A statistically rule-based decision support system for the management of patients with suspected liver disease.
The paper describes how a decision support system in liver diseases, mostly oriented to prediction of the necessity for liver biopsy, has been developed. The system designed is a hybrid one and consists of two parts: logical and statistical. The logical part contains rules, formulated on the basis of current medical knowledge, which enables recognition of clear cases; diseased or non-diseased. The unclear cases are classified on the basis of rules statistically extracted from databases. These rules have been reached after a comprehensive exploratory analysis of the sample of 165 patients with slightly to moderately raised levels of routine liver tests but without signs or symptoms of liver diseases. The extracted decision diagrams which simulate traditional medical diagnosis conduct have been found to be superior to discriminant analysis and probabilistic inductive learning. They use only a limited number of laboratory tests to detect the necessity for biopsy.
Algorithms↗