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Hans Ahlfeldt

Publications and source records attributed to Hans Ahlfeldt.

10 recordsLinked to original sources
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

Using the MEDLINE database to study the concept of urinary tract infections in different domains of medicine.

As a way of exploring differences between medical domains regarding management of urinary tract infections, we investigated the MEDLINE database for differences in indexing patterns. Further, our intention was to assess the MEDLINE database as a source for studying medical domains. We examined the use of main headings, subheadings and the level of main headings in six medical domains that manage urinary tract infections. Many intuitive but also some counterintuitive results were found indicating that the MEDLINE database is difficult to use for studying medical domains mainly due to unclear semantics both in the headings and the indexing process, which results in variability in indexing. This variability probably hides significant results. We also conclude that the differences found indicate that in addition to differences between domains, there are also large variations within domains.

Abstracting and Indexing↗
PubMed2003

Textual content, health problems and diagnostic codes in electronic patient records in general practice.

OBJECTIVE: To investigate textual content, health problems and diagnostic codes in everyday electronic patient records. DESIGN: Retrospective and observational database study. SETTING: Primary health care in Stockholm. SUBJECTS: Twenty randomly selected general practitioners with 20 records each. MAIN OUTCOME MEASURES: The frequency of use of problem-oriented medical records. The number of words, problems and diagnostic codes. The completeness and correctness of the diagnostic codes. RESULTS: About 14.5% of 400 studied records were problem-oriented. The mean number of words per record was 99.4, and the mean number of problems managed per record was 1.2. On average, there were 1.1 diagnostic codes per record and this differed widely among GPs and also among the electronic patient record systems. The mean number of codes per problem was 0.9, and the proportion of correct codes was 97.4%. CONCLUSIONS: The electronic patient records in general practice in Stockholm have an extensive textual content. A vast majority of the problems are coded and the completeness and correctness of diagnostic codes are high. It seems that problem-oriented electronic patient record systems enforce coding activities. It is feasible to establish a database of diagnostic data for research and health care planning based on electronic patient records.

Disease↗
PubMed2002

Requirements and prototyping of a home health care application based on emerging JAVA technology.

IT support for home health care is an expanding area within health care IT development. Home health care differs from other in- or outpatient care delivery forms in a number of ways, and thus, the introduction of home health care applications must be based on a rigorous analysis of necessary requirements to secure safe and reliable health care. This article reports early experiences from the development of a home health care application based on emerging JAVA technologies. A prototype application for the follow-up of diabetes patients is presented and discussed in relation to a list of general requirements on home health care applications.

Blood Glucose Self-Monitoring↗
PubMed2002

Computerisation, coding, data retrieval and related attitudes among Swedish general practitioners-a survey of necessary conditions for a database of diseases and health problems.

OBJECTIVE: To investigate necessary conditions for the establishment of a database of diseases and health problems for research and health care planning, based on electronic patient records in everyday clinical use among general practitioners (GPs). DESIGN: Postal questionnaire study. SETTING: Primary health care in Sweden. SUBJECTS: Three hundred randomly selected GPs. MAIN OUTCOME MEASURES: Degree of computerisation of patient records. User frequency and characteristics of diagnosis classification systems and coding tools. Frequency of coding activities and retrieval of codes, and related attitudes. Opinions on a primary health care version of ICD-10. RESULTS: A total of 184 GPs (61% of the 300 GPs) were included in the study. About 92% used an electronic record system, some type of diagnostic classification was used by 93%, and ICD based classifications by 88%. The classification in use was computerised for 74%. Mainly simple tools were used to retrieve diagnostic codes. About 76% of GPs reported classifying at least one symptom or disease per encounter. The codes were retrieved 'once a month' or more by 19%. Classification of diseases was considered important for follow-up by 83%, and for the care of the patient by 75% of the GPs. The primary health care version of ICD-10 with a total of 972 codes was considered too limited in size by 31%. CONCLUSION: Electronic patient records in everyday clinical use in Swedish general practice provide several fundamentals for a database of diagnostic data. However, there are several barriers to the establishment of such a database that is both valid and reliable.

Analysis of Variance↗
PubMed2002

Clinical process analysis and activity-based costing at a heart center.

Cost studies, productivity, efficiency, and quality of care measures, the links between resources and patient outcomes, are fundamental issues for hospital management today. This paper describes the implementation of a model for process analysis and activity-based costing (ABC)/management at a Heart Center in Sweden as a tool for administrative cost information, strategic decision-making, quality improvement, and cost reduction. A commercial software package (QPR) containing two interrelated parts, "ProcessGuide and CostControl," was used. All processes at the Heart Center were mapped and graphically outlined. Processes and activities such as health care procedures, research, and education were identified together with their causal relationship to costs and products/services. The construction of the ABC model in CostControl was time-consuming. However, after the ABC/management system was created, it opened the way for new possibilities including process and activity analysis, simulation, and price calculations. Cost analysis showed large variations in the cost obtained for individual patients undergoing coronary artery bypass grafting (CABG) surgery. We conclude that a process-based costing system is applicable and has the potential to be useful in hospital management.

Cardiac Care Facilities↗
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↗