Biomedical subjects

Hans Gill

Publications and source records attributed to Hans Gill.

8 recordsLinked to original sources

PubMed2006

An approach for generating fuzzy rules from decision trees.

Identifying high-risk breast cancer patients is vital both for clinicians and for patients. Some variables for identifying these patients such as tumor size are good candidates for fuzzification. In this study, Decision Tree Induction (DTI) has been applied to 3949 female breast cancer patients and crisp If-Then rules has been acquired from the resulting tree. After assigning membership functions for each variable in the crisp rules, they were converted into fuzzy rules and a mathematical model was constructed. One hundred randomly selected cases were examined by this model and compared with crisp rules predictions. The outcomes were examined by the area under the ROC curve (AUC). No significant difference was noticed between these two approaches for prediction of recurrence of breast cancer. By soft discretization of variables according to resulting rules from DTI, a predictive model, which is both more robust to noise and more comprehensible for clinicians, can be built.

Breast Neoplasms↗

PubMed2005

Exploring cancer register data to find risk factors for recurrence of breast cancer--application of Canonical Correlation Analysis.

BACKGROUND: A common approach in exploring register data is to find relationships between outcomes and predictors by using multiple regression analysis (MRA). If there is more than one outcome variable, the analysis must then be repeated, and the results combined in some arbitrary fashion. In contrast, Canonical Correlation Analysis (CCA) has the ability to analyze multiple outcomes at the same time. One essential outcome after breast cancer treatment is recurrence of the disease. It is important to understand the relationship between different predictors and recurrence, including the time interval until recurrence. This study describes the application of CCA to find important predictors for two different outcomes for breast cancer patients, loco-regional recurrence and occurrence of distant metastasis and to decrease the number of variables in the sets of predictors and outcomes without decreasing the predictive strength of the model. METHODS: Data for 637 malignant breast cancer patients admitted in the south-east region of Sweden were analyzed. By using CCA and looking at the structure coefficients (loadings), relationships between tumor specifications and the two outcomes during different time intervals were analyzed and a correlation model was built. RESULTS: The analysis successfully detected known predictors for breast cancer recurrence during the first two years and distant metastasis 2-4 years after diagnosis. Nottingham Histologic Grading (NHG) was the most important predictor, while age of the patient at the time of diagnosis was not an important predictor. CONCLUSION: In cancer registers with high dimensionality, CCA can be used for identifying the importance of risk factors for breast cancer recurrence. This technique can result in a model ready for further processing by data mining methods through reducing the number of variables to important ones.

Adult↗

PubMed2005

Canonical correlation analysis of risk factors and clinical outcomes in cardiac surgery.

Assessment of the association between risk factors and outcomes in cardiac surgery is a complex problem. The aim of this study was to explore the relationship between possible risk factors and several clinical outcomes in cardiac surgery by using canonical correlation analysis (CCA). This retrospective study of 2605 consecutive adult patients who underwent cardiac surgery, evaluated 74 potential risk factors and up to 12 outcomes by canonical correlation analysis. For three serious outcomes, sternal wound complications/mediastinitis, cerebral complications, and perioperative myocardial infarctions, CCA was preceded by univariate analyses and backward stepwise multivariate logistic regression analyses. The CCA suggests that the major risk factors for complications in these models are intraoperative and postoperative risk factors. The power of risk prediction models developed with multivariate regression analysis can be enhanced by application of canonical correlation analysis, thereby offering new ways of analyzing and interpreting sets of potential risk factors in relation to sets of clinical outcomes.

Aged↗

PubMed2005

Canonical correlation analysis for data reduction in data mining applied to predictive models for breast cancer recurrence.

Data mining methods can be used for extracting specific medical knowledge such as important predictors for recurrence of breast cancer in pertinent data material. However, when there is a huge quantity of variables in the data material it is first necessary to identify and select important variables. In this study we present a preprocessing method for selecting important variables in a dataset prior to building a predictive model.In the dataset, data from 5787 female patients were analysed. To cover more predictors and obtain a better assessment of the outcomes, data were retrieved from three different registers: the regional breast cancer, tumour markers, and cause of death registers. After retrieving information about selected predictors and outcomes from the different registers, the raw data were cleaned by running different logical rules. Thereafter, domain experts selected predictors assumed to be important regarding recurrence of breast cancer. After that, Canonical Correlation Analysis (CCA) was applied as a dimension reduction technique to preserve the character of the original data.Artificial Neural Network (ANN) was applied to the resulting dataset for two different analyses with the same settings. Performance of the predictive models was confirmed by ten-fold cross validation. The results showed an increase in the accuracy of the prediction and reduction of the mean absolute error.

Breast Neoplasms↗

PubMed2004

High antibiotic susceptibility among bacterial pathogens in Swedish ICUs. Report from a nation-wide surveillance program using TA90 as a novel index of susceptibility.

Local infection control measures, antibiotic consumption and patient demographics from 1999-2000 together with bacteriological analyses were investigated in 29 ICUs participating in the ICU-STRAMA programme. The median antibiotic consumption per ICU was 1147 (range 605-2143) daily doses per 1000 occupied bed d (DDD1000). Antibiotics to which > 90% of isolates of an organism were susceptible were defined as treatment alternatives (TA90). The mean number of TA90 was low (1-2 per organism) for Enterococcus faecium (vancomycin:VAN), coagulase negative staphylococci (VAN), Pseudomonas aeruginosa (ceftazidime:CTZ, netilmicin: NET) and Stenotrophomonas maltophilia (CTZ, trimethoprim-sulfamethoxazole: TSU), but higher (3-7) for Acinetobacter spp. (imipenem:IMI, NET, TSU), Enterococcus faecalis (ampicillin:AMP, IMI, VAN), Serratia spp. (ciprofloxacin:CIP, IMI, NET), Enterobacter spp. (CIP, IMI, NET, TSU), E. coli (cefuroxime:CXM, cefotaxime/eftazidime:CTX/CTZ, CIP, IMI, NET, piperacillin-tazobactam:PTZ, TSU), Klebsiella spp. (CTX/CTZ CIP, IMI, NET, PTZ, TSU) and Staphylococcus aureus (clindamycin, fusidic acid, NET, oxacillin, rifampicin, VAN). Of S. aureus isolates 2% were MRSA. Facilities for alcohol hand disinfection at each bed were available in 96% of the ICUs. The numbers of TA90 available were apparently higher than in ICUs in southern Europe and the US, despite a relatively high antibiotic consumption. This may be due to a moderate ecological impact of the used agents and the infection control routines in Swedish ICUs.

Adult↗

PubMed2003

Comparison of the Glasgow Coma Scale and the Reaction Level Scale for assessment of cerebral responsiveness in the critically ill.

OBJECTIVE: The Glasgow Coma Scale (GCS) is a well-known source of error in outcome prediction models. We compared assessment of cerebral responsiveness with the GCS and the Reaction Level Scale (RLS) in two otherwise similar outcome prediction models. DESIGN AND SETTING: Prospective, observational study in a general intensive care unit. PATIENTS AND PARTICIPANTS: All admissions of patients with or at risk of developing impaired brain function between 1997 and 1998 ( n=534). MEASUREMENTS AND RESULTS: Admissions were scored by RLS and APACHE II (includes scoring with the GCS). The RLS scores were transformed to APACHE II central nervous system scores according to a predetermined protocol. APACHE II estimated probability of death was calculated conventionally with the GCS and the RLS. Vital status 90 days after admission was secured from a national database. Bias and precision was 0.5% and 16.6%, respectively. The area under receiver operating characteristic curves was slightly but significantly greater with the RLS-based APACHE II model than with the GCS-based model (0.92 vs. 0.90). Discrimination was improved primarily in admissions with low and intermediate probability of death. CONCLUSIONS: Scoring of cerebral responsiveness with the RLS instead of the GCS was associated with minimal bias of the APACHE II probability of death estimate. Assessment of consciousness in critically ill with the RLS deserves further evaluation

APACHE↗

PubMed2002

Risk factor analysis of early and delayed cerebral complications after cardiac surgery.

OBJECTIVE: To report the incidence, severity, and possible risk factors for early and delayed cerebral complications. DESIGN: Retrospective study. SETTING: Linköping University Hospital, Sweden. PARTICIPANTS: Consecutive patients who underwent cardiac surgery in the period July 1996 through June 2000 (n = 3,282). INTERVENTIONS: A standard cardiopulmonary bypass (CPB) technique was used for most patients. Postoperative anticoagulant treatment included heparin or anti-Xa dalteparin. Patients undergoing coronary artery bypass graft surgery received acetylsalicylic acid, and patients undergoing valve surgery received warfarin. MEASUREMENTS AND MAIN RESULTS: Cerebral complications occurred in 107 patients (3.3%). Of these, 60 (1.8%) were early, and 33 (1.0%) were delayed, and in 14 (0.4%) patients the onset was unknown. There were 37 variables in univariate analysis (p < 0.15) and 14 variables in multivariate analysis (p < 0.05) associated with cerebral complications. Predictors of early cerebral complications were older age, preoperative hypertension, aortic aneurysm surgery, prolonged CPB time, hypotension at CPB completion and soon after CPB, and postoperative arrhythmia and supraventricular tachyarrhythmia. Predictors of delayed cerebral complications were female gender, diabetes, previous cerebrovascular disease, combined valve surgery and coronary artery bypass graft surgery, postoperative supraventricular tachyarrhythmia, and prolonged ventilator support. Early cerebral complications seem to be more serious, with more permanent deficits and a higher overall mortality (35.0% v 18.2%). CONCLUSION: Most cerebral complications had an early onset. The results of this study suggest that aggressive antiarrhythmic treatment and blood pressure control may imfurther prove the cerebral outcome after cardiac surgery.

Aged↗

PubMedDate not supplied

A variance-based measure of inter-rater agreement in medical databases.

The increasing use of encoded medical data requires flexible tools for data quality assessment. Existing methods are not always adequate, and this paper proposes a new metric for inter-rater agreement of aggregated diagnostic data. The metric, which is applicable in prospective as well as retrospective coding studies, quantifies the variability in the coding scheme, and the variation can be differentiated in categories and in coders. Five alternative definitions were compared in a set of simulated coding situations and in the context of mortality statistics. Two of them were more effective, and the choice between them must be made according to the situation. The metric is more powerful for larger numbers of coded cases, and Type I errors are frequent when coding situations include different numbers of cases. We also show that it is difficult to interpret the meaning of variation when the structures of the compared coding schemes differ.

Computer Simulation↗