Untitled

Zorman, Milan; Štiglic, Milojka Molan; Kokol, Peter; Malčić, Ivan

doi:10.1023/a:1022876330390

Cited by 29 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Advantages and disadvantages of decision trees in medicine have been widely investigated [172, 173]. The advantage of decision trees over many of the other methods is that they are not black-box models but can easily be expressed as rules.…”

Section: Computer-aided Diagnosis Systemmentioning

confidence: 99%

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Masood

Al-Jumaily

2013

International Journal of Biomedical Imaging

279

161

View full text Add to dashboard Cite

Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided.

show abstract

Section: Computer-aided Diagnosis Systemmentioning

confidence: 99%

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Masood

Al-Jumaily

2013

International Journal of Biomedical Imaging

279

161

View full text Add to dashboard Cite

show abstract

“…It is a well-known problem of this class of algorithms. Other problems linked to this method were also highlighted in previous studies [22].…”

Section: Discussionmentioning

confidence: 75%

A Panel Comprising Serum Amyloid A, White Blood Cells and Nihss for the Triage of Patients at Low Risk of Post-Stroke Infection

et al. 2021

View full text Add to dashboard Cite

Accurate and early prediction of poststroke infections is important to improve antibiotic therapy guidance and/or to avoid unnecessary antibiotic treatment. We hypothesized that the combination of blood biomarkers with clinical parameters could help to optimize risk stratification during hospitalization. In this prospective observational study, blood samples of 283 ischemic stroke patients were collected at hospital admission within 72 h from symptom onset. Among the 283 included patients, 60 developed an infection during the first five days of hospitalization. Performance predictions of blood biomarkers (Serum Amyloid-A (SAA), C-reactive protein, procalcitonin (CRP), white blood cells (WBC), creatinine) and clinical parameters (National Institutes of Health Stroke Scale (NIHSS), age, temperature) for the detection of poststroke infection were evaluated individually using receiver operating characteristics curves. Three machine learning techniques were used for creating panels: Associative Rules Mining, Decision Trees and an internal iterative-threshold based method called PanelomiX. The PanelomiX algorithm showed stable performance when applied to two representative subgroups obtained as splits of the main subgroup. The panel including SAA, WBC and NIHSS had a sensitivity of 97% and a specificity of 45% to identify patients who did not develop an infection. Therefore, it could be used at hospital admission to avoid unnecessary antibiotic (AB) treatment in around half of the patients, and consequently, to reduce AB resistance.

show abstract

Dealing with missing values in proteomics data

et al. 2022

View full text Add to dashboard Cite

Proteomics data are often plagued with missingness issues. These missing values (MVs) threaten the integrity of subsequent statistical analyses by reduction of statistical power, introduction of bias, and failure to represent the true sample. Over the years, several categories of missing value imputation (MVI) methods have been developed and adapted for proteomics data. These MVI methods perform their tasks based on different prior assumptions (e.g., data is normally or independently distributed) and operating principles (e.g., the algorithm is built to address random missingness only), resulting in varying levels of performance even when dealing with the same dataset. Thus, to achieve a satisfactory outcome, a suitable MVI method must be selected. To guide decision making on suitable MVI method, we provide a decision chart which facilitates strategic considerations on datasets presenting different characteristics. We also bring attention to other issues that can impact proper MVI such as the presence of confounders (e.g., batch effects) which can influence MVI performance. Thus, these too, should be considered during or before MVI.

show abstract

Untitled

Cited by 29 publications

References 3 publications

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

A Panel Comprising Serum Amyloid A, White Blood Cells and Nihss for the Triage of Patients at Low Risk of Post-Stroke Infection

Dealing with missing values in proteomics data

Contact Info

Product

Resources

About