Computer-assisted judgment: Defining strengths and liabilities.

Snyder, Douglas K.

doi:10.1037/1040-3590.12.1.52

Cited by 34 publications

(23 citation statements)

References 49 publications

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“…But for multi-parametric studies, the amount of data can be overwhelming even for expert readers, and conventional qualitative image interpretation may lead to inconsistent diagnostic precision. In recent years artificial intelligence and machine learning methods have been proven useful for diagnostic decision making problems in high dimensional feature space (23, 24). Support vector machine (SVM) learning is based on structural risk minimization (SRM) rather than the traditional empirical minimization (ERM) principle.…”

Section: Introductionmentioning

confidence: 99%

Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma

Wong

Young

et al. 2011

Magnetic Resonance Imaging

134

111

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Purpose To automatically differentiate radiation necrosis from recurrent tumor at high spatial resolution using multi-parametric MRI features. Materials and Methods MRI data retrieved from 31 patients (15 recurrent tumor and 16 radiation necrosis) who underwent chemoradiation therapy after surgical resection included post-Gd T1, T2, FLAIR, PD, ADC and PWI derived rCBV, rCBF and MTT maps. After alignment to post contrast T1WI, an 8-dimensional feature vector was constructed. An OC-SVMs classifier was trained using a radiation necrosis training set. Classifier parameters were optimized based on the area under ROC curve. The classifier was then tested on the full dataset. Results The sensitivity and specificity of optimized classifier for pseudoprogression was 89.91% and 93.72% respectively. The area under ROC curve was 0.9439. The distribution of voxels classified as radiation necrosis was supported by the clinical interpretation of followup scans for both non-progressing and progressing test cases. The ADC map derived from DWI and rCBV, rCBF derived from PWI were found to make a greater contribution to the discrimination than the conventional images. Conclusion Machine learning using multi-parametric MRI features may be a promising approach to identify the distribution of radiation necrosis tissue in resected GBM patients undergoing chemoradiation.

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Section: Introductionmentioning

confidence: 99%

Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma

Wong

Young

et al. 2011

Magnetic Resonance Imaging

134

111

View full text Add to dashboard Cite

show abstract

“…While they were positive about the results reached with the system, few indicated that they would use it in practice [79]. Criticisms of these systems fall into general categories of requiring too much data entry, limited in the decision areas (designed to only address a single clinical issue), inconvenient to access or viewed as a challenge to professional capability [72,73,79].…”

Section: Inference (Knowledge) Enginementioning

confidence: 98%

Development of a hand-held real-time decision support aid for critical care nursing

Fortier

Jagannathan

Michel

et al. 2003

36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of The

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In the current health care environment, nurse clinicians must work "faster and smarter" making complex decisions on almost a continual basis. Evidencebased knowledge and standardized guides, such as clinical algorithms, can support clinical nursing decisions, however; effective real-time access is limited. This paper outlines research addressing this problem. In this research, current clinical knowledge is delivered to the clinician via an off-the-shelf handheld computer using wireless access to a central server and data repository. Innovative minimal-set database, data mining and knowledge discovery algorithms using a combination of case based and rule based learning with added confidence measures permitting bi-directional (forward and backwards) inferencing based on individual client data are developed and presented for the hand held device. The technology provides real-time decision support for the multiple cases and sequential decisions characterizing present critical care nursing practice. Nurses will be able to consider a full range of alternative explanations, determine additional data needs, find, isolate and examine patient case outliers for additional diagnostic data or verify the appropriateness of a selected strategy. Once fully developed the system will have the capacity to maintain a history of a series of decisions and outcomes thereby over time improving the case base and rule bases used for decision support. Outcomes of the real time decision support aid include more timely health care, less biased decisions, and improved patient outcomes.

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“…Instead, test norms and the validity and reliability should be considered for each version of a test, whether it is paper-and-pencil or computer-based. Both Garb (2000) and Snyder (2000) noted that validity studies of computer and Internet-based assessment are rare. Even if a computer-based measure has satisfactory psychometric properties, the test scores should always be interpreted in association with other data, such as supplementary test scores and an individual interview, if possible.…”

Section: Ethical Issues In Computer and Internet-based Career Assessmentmentioning

confidence: 99%

International Handbook of Career Guidance

2008

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Computer-assisted judgment: Defining strengths and liabilities.

Cited by 34 publications

References 49 publications

Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma

Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma

Development of a hand-held real-time decision support aid for critical care nursing

International Handbook of Career Guidance

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