Clinical Decision Analysis using Decision Tree

Bae, Jong-Myon

doi:10.4178/epih/e2014025

Cited by 62 publications

(37 citation statements)

References 107 publications

(102 reference statements)

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“…Therefore, the decision trees should be viewed as descriptive explorative analysis explaining the data, but they are not confirming predictors. 35,36 In summary, the inclusion of the MCP in the automatized morphometric analysis corroborates previous neuropathological studies that showed that most MSA patients have a mixed SND and OPCA pathology and that brain atrophy patterns do not necessarily correspond to the clinical impression of predominant motor deficits. The combination of automated subcortical and supra-and infratentorial segmentation that included the volumetric measure of the MCP further improved the existing volumetric MRI decision support strategies on the diagnostic accuracy in separating MSA from PD.…”

Section: Discussionsupporting

confidence: 84%

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Morphometric MRI profiles of multiple system atrophy variants and implications for differential diagnosis

et al. 2019

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Background Manual width measurements of the middle cerebellar peduncle on MRI were shown to improve the accuracy of an imaging‐guided diagnosis of multiple system atrophy (MSA). Recently, automated volume segmentation algorithms were able to reliably differentiate patients with Parkinson's disease (PD) and the parkinsonian variant of MSA. The objective of the current study was to integrate probabilistic information of the middle cerebellar peduncle into an existing MRI atlas for automated subcortical segmentation and to evaluate the diagnostic properties of the novel atlas for the differential diagnosis of MSA (parkinsonian and cerebellar variant) versus PD. Methods Three Tesla MRI scans of 48 healthy individuals were used to establish an automated whole‐brain segmentation procedure that includes the volumes of the putamen, cerebellar gray and white matter, and the middle cerebellar peduncles. Classification accuracy of segmented volumes were tested in early‐stage MSA patients (18 MSA‐parkinsonism, 13 MSA‐cerebellar) and 19 PD patients using a C4.5 classifier. Results Putaminal and infratentorial atrophy were present in 77.8% and 61.1% of MSA‐parkinsonian patients, respectively. Four of 18 MSA‐parkinsonian patients (22.2%) had infratentorial atrophy without evidence of putaminal atrophy. Infratentorial atrophy was present in all MSA‐cerebellar patients, with concomitant putaminal atrophy in 46.2% of these cases. The diagnostic algorithm using putaminal and infratentorial volumetric information correctly classified all PD patients and 96.8% of MSA patients. Conclusions The middle cerebellar peduncle was successfully integrated into a subcortical segmentation atlas, and its excellent diagnostic accuracy outperformed existing volumetric MRI processing strategies in differentiating MSA patients with variable atrophy patterns from PD patients. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

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Section: Discussionsupporting

confidence: 84%

“…Readers should also be aware that the decision trees, like any other statistical model, may change in structure if important variables are added or removed. Therefore, the decision trees should be viewed as descriptive explorative analysis explaining the data, but they are not confirming predictors …”

Section: Discussionmentioning

confidence: 99%

Morphometric MRI profiles of multiple system atrophy variants and implications for differential diagnosis

et al. 2019

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“…In fact, decision trees are reliable and effective decision making techniques used in different areas of medical decision making [45]. Meanwhile, in evidence-based medicine, it was used for clinical decision analysis [46].…”

Section: Methodsmentioning

confidence: 99%

A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) Using Optimized Ensemble Learning

Mohebian

Marateb

Mansourian

et al. 2017

Computational and Structural Biotechnology Journal

129

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Cancer is a collection of diseases that involves growing abnormal cells with the potential to invade or spread to the body. Breast cancer is the second leading cause of cancer death among women. A method for 5-year breast cancer recurrence prediction is presented in this manuscript. Clinicopathologic characteristics of 579 breast cancer patients (recurrence prevalence of 19.3%) were analyzed and discriminative features were selected using statistical feature selection methods. They were further refined by Particle Swarm Optimization (PSO) as the inputs of the classification system with ensemble learning (Bagged Decision Tree: BDT). The proper combination of selected categorical features and also the weight (importance) of the selected interval-measurement-scale features were identified by the PSO algorithm. The performance of HPBCR (hybrid predictor of breast cancer recurrence) was assessed using the holdout and 4-fold cross-validation. Three other classifiers namely as supported vector machines, DT, and multilayer perceptron neural network were used for comparison. The selected features were diagnosis age, tumor size, lymph node involvement ratio, number of involved axillary lymph nodes, progesterone receptor expression, having hormone therapy and type of surgery. The minimum sensitivity, specificity, precision and accuracy of HPBCR were 77%, 93%, 95% and 85%, respectively in the entire cross-validation folds and the hold-out test fold. HPBCR outperformed the other tested classifiers. It showed excellent agreement with the gold standard (i.e. the oncologist opinion after blood tumor marker and imaging tests, and tissue biopsy). This algorithm is thus a promising online tool for the prediction of breast cancer recurrence.

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“…All of the decision tree calculations (ie, “folding back”) and sensitivity analyses were carried out using dedicated computer software (TreePlan™ v.2.03 and SensIt™ v.1.53. TreePlan Software Inc., San Francisco, CA, USA), following generally accepted rules …”

Section: Methodsmentioning

confidence: 99%

Thrombectomy of Ventricular Assist Device‐Originated Embolic Stroke: A Clinical Decision Model

Colletta

Bar

Liebo

et al. 2019

Journal of Neuroimaging

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BACKGROUND AND PURPOSE: The use of ventricular assist devices (VADs) for the treatment of heart failure has become increasingly common. These patients have a considerable risk of cerebral embolism. We describe such a patient and his successful treatment by thrombectomy, compare his attributes with those previously published, and describe the construct of a clinical decision model, whose results bear practical implications for patient management. METHODS: The details of our patient and his treatment are presented, followed by a literature review of all previously reported similar cases. Using this information, as well as that available from published series, we constructed a probabilistic decision tree, completed all calculations (ie, "folding back"), and, in order to assess the strength of the results, subjected them to multiple independent sensitivity analyses of each of the variables. RESULTS: The therapeutic success of our case, the 14th reported to date, when combined with previous reports, shows: (1) recanalization times of 184 minutes, (2) "successful" recanalization (ie, TICI = 2b or 3) achieved in 71% of procedures, (3) ultimate functional outcome (ie, mRS = 0-2) achieved in 57% patients, and (4) ultimate successful heart transplantations in 66% of cases. The clinical decision model showed the predicted utility of thrombectomy to be superior to conservative management (3.33 QALY vs. 2.56 QALY, respectively). The sensitivity analyses support the validity of these results. CONCLUSIONS: In conclusion, thrombectomy appears to be a safe and effective method (and often the only viable one) for urgent treatment of patients with VAD-originated cerebral embolism.

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Clinical Decision Analysis using Decision Tree

Cited by 62 publications

References 107 publications

Morphometric MRI profiles of multiple system atrophy variants and implications for differential diagnosis

Morphometric MRI profiles of multiple system atrophy variants and implications for differential diagnosis

A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) Using Optimized Ensemble Learning

Thrombectomy of Ventricular Assist Device‐Originated Embolic Stroke: A Clinical Decision Model

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