scholarly journals NCMHap: a novel method for haplotype reconstruction based on Neutrosophic c-means clustering

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Fatemeh Zamani ◽  
Mohammad Hossein Olyaee ◽  
Alireza Khanteymoori
Keyword(s):  
Clustering Algorithm ◽  
Clinical Decision Making ◽  
Genetic Diseases ◽  
Clinical Decision ◽  
Single Individual ◽  
Haplotype Reconstruction ◽  
Huge Amount ◽  
Noisy Input ◽  
Benchmark Datasets ◽  
Novel Method

Abstract Background Single individual haplotype problem refers to reconstructing haplotypes of an individual based on several input fragments sequenced from a specified chromosome. Solving this problem is an important task in computational biology and has many applications in the pharmaceutical industry, clinical decision-making, and genetic diseases. It is known that solving the problem is NP-hard. Although several methods have been proposed to solve the problem, it is found that most of them have low performances in dealing with noisy input fragments. Therefore, proposing a method which is accurate and scalable, is a challenging task. Results In this paper, we introduced a method, named NCMHap, which utilizes the Neutrosophic c-means (NCM) clustering algorithm. The NCM algorithm can effectively detect the noise and outliers in the input data. In addition, it can reduce their effects in the clustering process. The proposed method has been evaluated by several benchmark datasets. Comparing with existing methods indicates when NCM is tuned by suitable parameters, the results are encouraging. In particular, when the amount of noise increases, it outperforms the comparing methods. Conclusion The proposed method is validated using simulated and real datasets. The achieved results recommend the application of NCMHap on the datasets which involve the fragments with a huge amount of gaps and noise.

scholarly journals NCMHap: A novel method for haplotype reconstruction based on Neutrosophic c-means clustering

2020 ◽  
Author(s):  
Fatemeh Zamani ◽  
mohammad Olyaee ◽  
Alireza Khanteymoori
Keyword(s):  
Clustering Algorithm ◽  
Clinical Decision Making ◽  
Genetic Diseases ◽  
Clinical Decision ◽  
Single Individual ◽  
Haplotype Reconstruction ◽  
Huge Amount ◽  
Noisy Input ◽  
Benchmark Datasets ◽  
Novel Method

Abstract Background: Single individual haplotype (SIH) problem refers to reconstructing haplotypes of an individual based on several input fragments sequenced from a specified chromosome. Solving this problem is an important task in computational biology and has many applications in the pharmaceutical industry, clinical decision-making, and genetic diseases. It is known that solving the problem is NP-hard. Although several methods have been proposed to solve the problem, it is found that most of them have low performances in dealing with noisy input fragments. Therefore, proposing a method which is accurate and scalable, is a challenging task. Results: In this paper, we introduced a method, named NCMHap, which utilizes the Neutrosophic c-means (NCM) clustering algorithm. The NCM algorithm can effectively detect the noise and outliers in the input data. In addition, it can reduce their effects in the clustering process. The proposed method has been evaluated by several benchmark datasets. Comparing with existing methods indicates when NCM is tuned by suitable parameters, the results are encouraging. In particular, when the amount of noise increases, it outperforms the comparing methods. Conclusion: The proposed method is validated using simulated and real datasets. The achieved results recommend the application of NCMHap on the datasets which involve the fragments with a huge amount of gaps and noise.

scholarly journals NCMHap: A novel method for haplotype reconstruction based on Neutrosophic c-means clustering

2020 ◽  
Author(s):  
Fatemeh Zamani ◽  
Mohammad Olyaee ◽  
Alireza Khanteymoori
Keyword(s):  
Clustering Algorithm ◽  
Clinical Decision Making ◽  
Genetic Diseases ◽  
Clinical Decision ◽  
Single Individual ◽  
Haplotype Reconstruction ◽  
Huge Amount ◽  
Noisy Input ◽  
Benchmark Datasets ◽  
Novel Method

Abstract Background: Single individual haplotype (SIH) problem refers to reconstructing haplotypes of an individual based on several input fragments sequenced from a specified chromosome. Solving this problem is an important task in computational biology and has many applications in the pharmaceutical industry, clinical decision-making and genetic diseases. It is known that solving the problem is NP-hard. Although several methods have been proposed to solve the problem, but it is found that most of them have low performances in dealing with noisy input fragments. Therefore, proposing a method which be accurate and scalable, is a challenging task.Results: In this paper, we introduced a method, named NCMHap, which utilizes the Neutrosophic c-means (NCM) clustering algorithm. The NCM algorithm can effectively detect the noise and outliers in the input data. In addition, it can reduce their effects in the clustering process. The proposed method has been evaluated by several benchmark datasets. Comparing with existing methods indicates that NCMHap is significantly superior in the most cases, particularly when the amount of noise increases, it outperforms the comparing methods.Conclusion: The experimental results recommend the application of the proposed method on the datasets which involve the fragments with huge amount of gaps and noise.

Clinical Conundrums When Integrating the QbTest into a Standard ADHD Assessment of Children and Young People

2021 ◽  
Author(s):  
Carsten Vogt
Keyword(s):  
Clinical Practice ◽  
Clinical Decision Making ◽  
Ecological Validity ◽  
Neuropsychological Test ◽  
Real Life ◽  
Clinical Decision ◽  
Hyperactivity Disorder ◽  
Standard Clinical Practice ◽  
Novel Method ◽  
The Impact

AbstractThe uptake of the QbTest in clinical practice is increasing and has recently been supported by research evidence proposing its effectiveness in relation to clinical decision-making. However, the exact underlying process leading to this clinical benefit is currently not well established and requires further clarification. For the clinician, certain challenges arise when adding the QbTest as a novel method to standard clinical practice, such as having the skills required to interpret neuropsychological test information and assess for diagnostically relevant neurocognitive domains that are related to attention-deficit hyperactivity disorder (ADHD), or how neurocognitive domains express themselves within the behavioral classifications of ADHD and how the quantitative measurement of activity in a laboratory setting compares with real-life (ecological validity) situations as well as the impact of comorbidity on test results. This article aims to address these clinical conundrums in aid of developing a consistent approach and future guidelines in clinical practice.

scholarly journals Multispectral Imaging Algorithm Predicts Breslow Thickness of Melanoma

2021 ◽  
Vol 11 (1) ◽  
pp. 189
Author(s):  
Szabolcs Bozsányi ◽  
Noémi Nóra Varga ◽  
Klára Farkas ◽  
András Bánvölgyi ◽  
Kende Lőrincz ◽  
...  
Keyword(s):  
Multispectral Imaging ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Breslow Thickness ◽  
Substantial Agreement ◽  
Quantitative Parameter ◽  
Clinical Images ◽  
Melanoma Diagnosis ◽  
Safety Margins ◽  
Novel Method

Breslow thickness is a major prognostic factor for melanoma. It is based on histopathological evaluation, and thus it is not available to aid clinical decision making at the time of the initial melanoma diagnosis. In this work, we assessed the efficacy of multispectral imaging (MSI) to predict Breslow thickness and developed a classification algorithm to determine optimal safety margins of the melanoma excision. First, we excluded nevi from the analysis with a novel quantitative parameter. Parameter s’ could differentiate nevi from melanomas with a sensitivity of 89.60% and specificity of 88.11%. Following this step, we have categorized melanomas into three different subgroups based on Breslow thickness (≤1 mm, 1–2 mm and >2 mm) with a sensitivity of 78.00% and specificity of 89.00% and a substantial agreement (κ = 0.67; 95% CI, 0.58–0.76). We compared our results to the performance of dermatologists and dermatology residents who assessed dermoscopic and clinical images of these melanomas, and reached a sensitivity of 60.38% and specificity of 80.86% with a moderate agreement (κ = 0.41; 95% CI, 0.39–0.43). Based on our findings, this novel method may help predict the appropriate safety margins for curative melanoma excision.

scholarly journals Pan-cancer identification of clinically relevant genomic subtypes using outcome-weighted integrative clustering

2020 ◽  
Author(s):  
Arshi Arora ◽  
Adam B. Olshen ◽  
Venkatraman E. Seshan ◽  
Ronglai Shen
Keyword(s):  
Cancer Patient ◽  
Clustering Algorithm ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Unsupervised Clustering ◽  
Patient Stratification ◽  
Cell Of Origin ◽  
Weighted Clustering ◽  
Molecular Phenotypes ◽  
Major Factors

ABSTRACTMolecular phenotypes of cancer are complex and influenced by a multitude of factors. Conventional unsupervised clustering of heterogeneous cancer patient populations is inevitably driven by the dominant variation from major factors such as cell-of-origin or histology. Drawing from ideas in supervised text classification, we developed survClust, an outcome-weighted clustering algorithm for integrative patient stratification. We show survClust outperforms unsupervised clustering in identifying cancer patient subpopulations characterized by specific genomic phenotypes with more aggressive clinical behavior. The algorithm and tools we developed have direct utility toward clinically relevant patient stratification based on tumor genomics to inform clinical decision-making.

Preliminary Findings on the Use of Session Questionnaires to Improve Clinician-Client Alliance With Special Consideration to Clinical Education

2015 ◽  
Vol 25 (1) ◽  
pp. 50-60
Author(s):  
Anu Subramanian
Keyword(s):  
Graduate Students ◽  
Clinical Education ◽  
Clinical Decision Making ◽  
Rating Scale ◽  
Treatment Effectiveness ◽  
Clinical Decision ◽  
Added Value ◽  
Therapeutic Outcomes ◽  
Specific Feedback ◽  
Stakeholder Perspective

ASHA's focus on evidence-based practice (EBP) includes the family/stakeholder perspective as an important tenet in clinical decision making. The common factors model for treatment effectiveness postulates that clinician-client alliance positively impacts therapeutic outcomes and may be the most important factor for success. One strategy to improve alliance between a client and clinician is the use of outcome questionnaires. In the current study, eight parents of toddlers who attended therapy sessions at a university clinic responded to a session outcome questionnaire that included both rating scale and descriptive questions. Six graduate students completed a survey that included a question about the utility of the questionnaire. Results indicated that the descriptive questions added value and information compared to using only the rating scale. The students were varied in their responses regarding the effectiveness of the questionnaire to increase their comfort with parents. Information gathered from the questionnaire allowed for specific feedback to graduate students to change behaviors and created opportunities for general discussions regarding effective therapy techniques. In addition, the responses generated conversations between the client and clinician focused on clients' concerns. Involving the stakeholder in identifying both effective and ineffective aspects of therapy has advantages for clinical practice and education.

Clinical Decision-Making for Stroke and Aphasia in the Older Adult

2009 ◽  
Vol 14 (1) ◽  
pp. 4-11 ◽  
Author(s):  
Jacqueline Hinckley
Keyword(s):  
Executive Function ◽  
Cerebrovascular Disease ◽  
Cognitive Abilities ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Cognitive Profile ◽  
Focal Lesion ◽  
Cognitive Profiles ◽  
Supportive Communication ◽  
Spaced Retrieval

Abstract A patient with aphasia that is uncomplicated by other cognitive abilities will usually show a primary impairment of language. The frequency of additional cognitive impairments associated with cerebrovascular disease, multiple (silent or diagnosed) infarcts, or dementia increases with age and can complicate a single focal lesion that produces aphasia. The typical cognitive profiles of vascular dementia or dementia due to cerebrovascular disease may differ from the cognitive profile of patients with Alzheimer's dementia. In order to complete effective treatment selection, clinicians must know the cognitive profile of the patient and choose treatments accordingly. When attention, memory, and executive function are relatively preserved, strategy-based and conversation-based interventions provide the best choices to target personally relevant communication abilities. Examples of treatments in this category include PACE and Response Elaboration Training. When patients with aphasia have co-occurring episodic memory or executive function impairments, treatments that rely less on these abilities should be selected. Examples of treatments that fit these selection criteria include spaced retrieval and errorless learning. Finally, training caregivers in the use of supportive communication strategies is helpful to patients with aphasia, with or without additional cognitive complications.

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