Biomedical subjects

Stephan Dreiseitl

Publications and source records attributed to Stephan Dreiseitl.

4 recordsLinked to original sources

PubMed2006

Approximation properties of haplotype tagging.

BACKGROUND: Single nucleotide polymorphisms (SNPs) are locations at which the genomic sequences of population members differ. Since these differences are known to follow patterns, disease association studies are facilitated by identifying SNPs that allow the unique identification of such patterns. This process, known as haplotype tagging, is formulated as a combinatorial optimization problem and analyzed in terms of complexity and approximation properties. RESULTS: It is shown that the tagging problem is NP-hard but approximable within 1 + ln((n2 - n)/2) for n haplotypes but not approximable within (1-epsilon) ln(n/2) for any epsilon > 0 unless NP subset DTIME(n(log log n)). A simple, very easily implementable algorithm that exhibits the above upper bound on solution quality is presented. This algorithm has running time O(np/2(2m-p+1)) < or = O(m(n2-n)/2) where p < or = min(n, m) for n haplotypes of size m. As we show that the approximation bound is asymptotically tight, the algorithm presented is optimal with respect to this asymptotic bound. CONCLUSION: The haplotype tagging problem is hard, but approachable with a fast, practical, and surprisingly simple algorithm that cannot be significantly improved upon on a single processor machine. Hence, significant improvement in computational efforts expended can only be expected if the computational effort is distributed and done in parallel.

Algorithms↗

PubMed2005

Nomographic representation of logistic regression models: a case study using patient self-assessment data.

Logistic regression models are widely used in medicine, but difficult to apply without the aid of electronic devices. In this paper, we present a novel approach to represent logistic regression models as nomograms that can be evaluated by simple line drawings. As a case study, we show how data obtained from a questionnaire-based patient self-assessment study on the risks of developing melanoma can be used to first identify a subset of significant covariates, build a logistic regression model, and finally transform the model to a graphical format. The advantage of the nomogram is that it can easily be mass-produced, distributed and evaluated, while providing the same information as the logistic regression model it represents.

Algorithms↗

PubMed2005

Do physicians value decision support? A look at the effect of decision support systems on physician opinion.

OBJECTIVE: Clinical decision support systems are on the verge of becoming routine software tools in clinical settings. We investigate the question of how physicians react when faced with decision support suggestions that contradict their own diagnoses. METHODOLOGY: We used a study design involving 52 volunteer dermatologists who each rated the malignancy of 25 lesion images on an ordinal scale and gave a dichotomous excise/no excise recommendation for each lesion image. After seeing the system's rating and excise suggestions, the physicians could revise their initial recommendations. RESULTS: We observed that in 24% of the cases in which the physicians' diagnoses did not match those of the decision support system, the physicians changed their diagnoses. There was a slight but significant negative correlation between susceptibility to change and experience level of the physicians. Physicians were significantly less likely to follow the decision system's recommendations when they were confident of their initial diagnoses. No differences between the physicians' inclinations to following excise versus no excise recommendations could be observed. CONCLUSION: These results indicate that physicians are quite susceptible to accepting the recommendations of decision support systems, and that quality assurance and validation of such systems is therefore of paramount importance.

Austria↗

PubMedDate not supplied

Logistic regression and artificial neural network classification models: a methodology review.

Logistic regression and artificial neural networks are the models of choice in many medical data classification tasks. In this review, we summarize the differences and similarities of these models from a technical point of view, and compare them with other machine learning algorithms. We provide considerations useful for critically assessing the quality of the models and the results based on these models. Finally, we summarize our findings on how quality criteria for logistic regression and artificial neural network models are met in a sample of papers from the medical literature.

Classification↗