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PubMed2002

Novel statistical classification model of type 2 diabetes mellitus patients for tailor-made prevention using data mining algorithm.

To estimate the usefulness of data mining algorithms for extracting risk predictors of diabetic vascular complications in proper order in the future, we tried applying the Classification and Regression Trees (CART) method to the prevalence data of 165 type 2 diabetic outpatients and already known risk factors. Among the 6 categorical and 15 continuous risk factors, age (cutoff: 65.4) was the best predictor for classifying patients into groups with and without macroangiopathy (p=0.000). Body weight (cutoff: 53.9) was the best predictor (p=0.006) in the older group (age >65.4), whereas systolic blood pressure (cutoff: 144.5) was the best predictor in the remaining group (p=0.002). Age (cutoff: 64.8) was also the best predictor for categorizing them into groups with and without microangiopathy (p=0.000). In the older group (age >64.8), BMI (cutoff: 21.5) was the best predictor (p=0.001), whereas morbidity term (cutoff: 15.5) was the best predictor in the other group (p=0.01 0). Because the orders and values of all risk factors and cutoff points mined were reasonable clinically, this method may have the potential to highlight predictors in order of importance to apply tailor-made prevention of diabetic vascular complications.

Algorithms↗

PubMed2006

Interpreter of maladies: redescription mining applied to biomedical data analysis.

Comprehensive, systematic and integrated data-centric statistical approaches to disease modeling can provide powerful frameworks for understanding disease etiology. Here, one such computational framework based on redescription mining in both its incarnations, static and dynamic, is discussed. The static framework provides bioinformatic tools applicable to multifaceted datasets, containing genetic, transcriptomic, proteomic, and clinical data for diseased patients and normal subjects. The dynamic redescription framework provides systems biology tools to model complex sets of regulatory, metabolic and signaling pathways in the initiation and progression of a disease. As an example, the case of chronic fatigue syndrome (CFS) is considered, which has so far remained intractable and unpredictable in its etiology and nosology. The redescription mining approaches can be applied to the Centers for Disease Control and Prevention's Wichita (KS, USA) dataset, integrating transcriptomic, epidemiological and clinical data, and can also be used to study how pathways in the hypothalamic-pituitary-adrenal axis affect CFS patients.

Algorithms↗

PubMed1995

Mesna/ifosfamide, mitoxantrone, etoposide, bleomycin, vincristine, prednisone (MINE-BOP) combination chemotherapy in the treatment of refractory and relapsed non-Hodgkin's lymphoma.

Twenty-one consecutive patients with refractory or relapsed non-Hodgkin's lymphomas were treated with a novel combination chemotherapy (MINE-BOP), comprising myelosuppressive (ifosfamide, mitoxantrone, etoposide) and non-myelosuppressive (bleomycin, vincristine and prednisone) drugs. Median age of the patients was 42 years and all had intermediate or high-grade lymphoma. Fifteen patients had refractory disease. All patients had previously been treated with one or two regimens, containing anthracyclines. In all cases the duration between the last chemotherapy and the MINE-BOP regimen was shorter than 12 months. Response rate was 57% with 33% complete remission (CR). Median disease-free and overall survivals were 7 and 10 months respectively. The serum LDH level was the only significant prognostic factor in this study. The toxicity of this regimen was moderate with 24% of febrile neutropenia and 9% of microscopic hematuria. Toxic death due to febrile neutropenia was observed in one patient who had bone marrow involvement. To conclude, the addition of non-myelosuppressive drugs to the chemotherapy regimen and shortening the interval between the application of cytotoxic drugs as used in the present study did not show any improvement of response and survival in this group of patients.

Adolescent↗

PubMed1999

Term domain distribution analysis: a data mining tool for text databases.

In this paper, we give a case history illustrating the real-world application of a useful technique for data mining of text databases. The technique, which we call Term Domain Distribution Analysis (TDDA), consists of keeping track of term frequencies for specific finite domains and announcing significant differences from standard frequency distributions over these domains as a hypothesis. TDDA is part of a larger framework, the Digital Filter Model, for data mining of text documents. In the case study presented, the domain of terms was the pair {right, left}, over which we expected a uniform distribution. In analyzing term frequencies in a thoracic lung cancer database, the TDDA technique led to the surprising discovery that primary thoracic lung cancer tumors appear in the right lung more often than the left lung, with a ratio of 3:2. Treating the text discovery as a hypothesis, we verified this relationship against the medical literature in which primary lung tumor sites were reported, using a standard chi 2 statistic. We subsequently developed a working theoretical model of lung cancer that may explain the discovery. This discovery and our model may change how oncologists view the mechanisms of primary lung tumor location.

Humans↗

PubMed1999

[Dilemmas in the classification of foot injuries caused by land mines].

In international classification of injuries, very often there are some specific conditions that are not clearly defined. War initiated that about specific conditions before all injuries a discussion in term of more correct presentation through the code is being made. Very often, some conditions in international classification are being led as INSUFFICIENTLY DEFINED or UNMARKED PART. When we have present large destruction due to wounding with projectiles of high kinetic energy (initial velocity), and especially stepping on land-mines, we have the situations that can not be correctly defined. The amount of these injuries which are about to follow in the future period authors will declare in few examples which have been surgically treated in Traumatological Clinic in Sarajevo. All injuries were result of stepping on land-mines and in all cases it was about foot. The stated injuries we were not able to code as amputations because the distal part of the injury was vascularized, later on we found out that there are possibilities of surgical reconstruction in meaning of functional foot, since we do not have open (explosive) fracture here. May-be, as a clinical formulation or for initial observation a more appropriate name is (term analogue in surgery of other regions beside limbs) INTERCALARY amputation of limbs. The same one could be moved from deficiency to a complete lack which surely must be linked to its causality.

Blast Injuries↗

PubMedDate not supplied

Data mining in child welfare.

Data mining is the sifting through of voluminous data to extract knowledge for decision making. This article illustrates the context, concepts, processes, techniques, and tools of data mining, using statistical and neural network analyses on a dataset concerning employee turnover. The resulting models and their predictive capability, advantages and disadvantages, and implications for decision support are highlighted.

Child↗

PubMed2000

Mining association rules from a pediatric primary care decision support system.

The purpose of this study was to apply an unsupervised data mining algorithm to a database containing data collected at the point of care for clinical decision support. The data set was taken from the Child Health Improvement Program (CHIP), a preventive services tracking and reminder system in use at the University of North Carolina. The database contains over 30,000 visits. We used a previously described pattern discovery algorithm to extract 2nd and 3rd order association rules from the data and reviewed the literature two see if the associations had been described before. The algorithm discovered 16 2nd order associations and 103 3rd order associations. The 3rd order associations contained no new information. The 2nd order associations demonstrated a covariance among a range of health risk behaviors. Additionally, the algorithm discovered that both tobacco smoke exposure and chronic cardiopulmonary disease are associated with failure on developmental screens. These relationships have been described before and have been attributed to underlying poverty. The work demonstrates the ability of unsupervised data mining by rule association on sparse clinical data to discover clinically important associations. However, many associations may be previously known or explained by confounding variables.

Algorithms↗

PubMed2000

Using data mining and OLAP to discover patterns in a database of patients with Y-chromosome deletions.

The paper presents a database of published Y chromosome deletions and the results of analyzing the database with data mining techniques. The database describes 382 patients for which 177 different markers were tested: 364 of the 382 patients had deletions. Two data mining techniques, clustering and decision tree induction were used. Clustering was used to group patients according to the overall presence/absence of deletions at the tested markers. Decision trees and On-Line-Analytical-Processing (OLAP) were used to inspect the resulting clustering and look for correlations between deletion patterns, populations and the clinical picture of infertility. The results of the analysis indicate that there are correlations between deletion patterns and patient populations, as well as clinical phenotype severity.

Chromosome Deletion↗

PubMed2000

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↗

PubMed2000

A data mining approach to the development of a diagnostic test for male infertility.

The paper presents a database of published Y chromosome deletions and the results of analyzing the database with data mining and other heuristic techniques with the goal of developing a diagnostic test for male infertility. The database describes 382 patients for which 177 markers were tested. Two data mining techniques, clustering and decision tree induction were used, as well as a heuristic set cover algorithm. Clustering was used to group markers according to their appearance across patients, while a heuristic set covering algorithm was used to select as small a set of markers that cover as many patients with deletions as possible. This algorithm created a diagnostic set of 13 markers that cover more than 90% of the patients with deletions. Finally, decision tree induction was used to relate deletion patterns to the severity of the clinical phenotype. A decision tree induced from the data uses 5 markers, all of which are also in the diagnostic set of 13 markers, to show relations between the severity of the clinical phenotype and deletion patterns which have not been known previously.

Algorithms↗

PubMed2000

A data mining system for infection control surveillance.

Nosocomial infections and antimicrobial resistance are problems of enormous magnitude that impact the morbidity and mortality of hospitalized patients as well as their cost of care. The Data Mining Surveillance System (DMSS) uses novel data mining techniques to discover unsuspected, useful patterns of nosocomial infections and antimicrobial resistance from the analysis of hospital laboratory data. This report details a mature version of DMSS as well as an experiment in which DMSS was used to analyze all inpatient culture data, collected over 15 months at the University of Alabama at Birmingham Hospital.

Alabama↗

PubMed2001

Case study: how to apply data mining techniques in a healthcare data warehouse.

Healthcare provider organizations are faced with a rising number of financial pressures. Both administrators and physicians need help analyzing large numbers of clinical and financial data when making decisions. To assist them, Rush-Presbyterian-St. Luke's Medical Center and Hitachi America, Ltd. (HAL), Inc., have partnered to build an enterprise data warehouse and perform a series of case study analyses. This article focuses on one analysis, which was performed by a team of physicians and computer science researchers, using a commercially available on-line analytical processing (OLAP) tool in conjunction with proprietary data mining techniques developed by HAL researchers. The initial objective of the analysis was to discover how to use data mining techniques to make business decisions that can influence cost, revenue, and operational efficiency while maintaining a high level of care. Another objective was to understand how to apply these techniques appropriately and to find a repeatable method for analyzing data and finding business insights. The process used to identify opportunities and effect changes is described.

Chicago↗

PubMed2000

[Blood plasma level of endothelin in miners of a deep coal mine].

In 60 miners working in a deep coal mine the blood plasma level of endoteline-1 (E-1) was measured by the immunoenzyme technique immediately after working shift. Those in the mining where the working conditions are especially harsh were found to have the highest level of E-1 exceeding the control values. In studying the age-related content of E-1 in blood plasma of coal miners the highest levels of E-1 were recordable in workers 20 to 30 years old, declining and differing in age groups 20-30 and 41-50 years old. The level of E-1 was at its greatest in those workers with minimum length of service in the underground conditions, declining with the service more than 10 years in duration.

Adult↗

PubMed2001

From data collection to knowledge data discovery: a medical application of data mining.

Prison inmates are exposed to a variety of major risk factors (psychiatric disorders, suicide attempts, illicit drug use). From 1986 to 1996, the USA prison population more than doubled while in France, it increased from 35655 in 1980 to 51623 in 1995. In spite of these findings, very little information concerning the inmates population is available. At the present time, there is a desire to adopt a policy based on the prevention of recidivism, on adequate release planning and referrals to community-based services. The aim of the RAPPEL project was to build an information system for assessing the social and health status of prison inmates. The pilot project was set up at the prison of Loos and allowed the collection and analysis of nearly 15000 records. The aim of this paper is to present the extension of the project consisting in developing a regional network grouping 11 jails. Information locally available will serve as the basis for the information system of regional jails. Data mining techniques will provide solutions for the extraction of new information. Three data mining tools were experimented : association rules, classification trees and clustering. Further extension consists in a distributed approach allowing direct access to the information system by WEB tools.

Classification↗

PubMed2001

[Effects of copper mine tailings on growth and development and physiological function of wheat].

Through pot culture experiments, the effects of Tonling copper mine tailings on growth and development and physiological function of wheat were investigated in this paper. The results showed that the seeds of wheat could germinate in copper mine tailings, but seed germination percentage and germination rate were lower than that in control. High ratio tailings decreased seedling height. Tailings decreased chlorophyll contents, but didn't effect on chlorophyll a/b. Compared with control, tailings significantly inhibited root growth and decreased root number. Tailings increased root and shoot biomass and significantly increased root/shoot ratio of wheat. The permeability of cell membrane and the free proline content in leaves of wheat increased with the increase of the ratio of tailings, they were relative tolerance index of wheat to copper tailings stress.

Biomass↗

PubMed2001

MeSHmap: a text mining tool for MEDLINE.

Our research goal is to explore text mining from the metadata included in MEDLINE documents. We present MeSHmap our prototype text mining system that exploits the MeSH indexing accompanying MEDLINE records. MeSHmap supports searches via PubMed followed by user driven exploration of the MeSH terms and subheadings in the retrieved set. The potential of the system goes beyond text retrieval. It may also be used to compare entities of the same type such as pairs of drugs or pairs of procedures etc. In addition there is the potential to generate maps of entities (drugs or diseases etc.) such that the strength of the link between two entities in the map represents their similarity as expressed in the MeSH metadata of the MEDLINE documents. Higher level operators have been proposed to support these comparison and mapping functions. This paper motivates and describes MeSHmap. Future work will include user evaluations of the system.

Abstracting and Indexing↗

PubMed2002

[Data mining in diagnostic knowledge acquisition from patients with brain glioma].

In order to correctly predict the malignant degree of brain glioma, three data mining algorithms: multi-layer perceptron network(MLP), decision tree, and rule induction are adopted to acquire diagnostic knowledge from patients with brain glioma cases. Totally 280 cases are collected, and some of them contain missing values. Preprocessing is taken to make them applicable to all three algorithms. Performance comparisons are carried out with a 10-fold cross validation test. Although the result of MLP is hard to be understood and cannot be applied directly, its reliability and accuracy are the highest when only a few hidden nodes are involved. Unlike MLP, both decision tree and rule induction use attribute-value pairs to represent diagnostic knowledge derived from treated cases. These could improve both the understandability and applicability of their results. When compared with rule induction, the inherent restriction in structure makes decision tree more efficient in decision-making but meanwhile hurts its simplicity, accuracy, and reliability. For testing samples, results of all these algorithms can achieve accuracy rate over 80%, which satisfies the basic requirement of neuroradiologists. If diagnostic accuracy rate is the main factor to be considered, MLP with only a few hidden nodes is the best. If the result is expected to be further checked or evaluated, rule induction will be the best algorithm. This work proves that data mining techniques can be used to obtain valid diagnostic knowledge from brain glioma cases and make computer aided diagnosis system in this field feasible.

Algorithms↗

PubMed2003

Treatment of lower extremity injuries due to antipersonnel mines: blast resuscitation and victim assistance team experiences in Cambodia.

The two teams comprising the fiscal year 2001 Blast Resuscitation and Victim Assistance mission had the opportunity to learn from and practice mine injury treatment principles with experienced local and international war surgeons in Cambodia. Treatment principles were modifications of International Committee of the Red Cross recommendations. A total of 14 acute lower extremity mine injuries were treated. Surgery generally consisted of an open amputation or thorough irrigation and debridement using equipment readily available in any U.S. military field hospital. The surgical techniques will be described in detail. Delayed primary closure occurred 5 days later followed by prosthesis fitting (for amputees) in an International Committee of the Red Cross facility 12 weeks later. Other options and techniques will be discussed with an emphasis on applicability to U.S. military field surgery.

Amputation, Surgical↗