Clinical signs and early prognosis in vegetative state: A decisional tree, data-mining study

Brain Injury ◽  
2008 ◽  
Vol 22 (7-8) ◽  
pp. 617-623 ◽  
Author(s):  
G. Dolce ◽  
M. Quintieri ◽  
S. Serra ◽  
V. Lagani ◽  
L. Pignolo
Keyword(s):  
Data Mining ◽  
Vegetative State ◽  
Clinical Signs ◽  
Tree Data

Heart Rate Variability, Emotions, and Music

2010 ◽  
Vol 24 (2) ◽  
pp. 112-119 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
G. Dolce
Keyword(s):  
Data Mining ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Low Frequency ◽  
Emotional Reactions ◽  
Heart Beat ◽  
Healthy Controls ◽  
Frequency Spectra ◽  
Emotional Value

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.

Data Mining Approach to Analyze COVID-19 Clinical Dataset

2021 ◽  
Vol 15 (6) ◽  
pp. 1812-1819
Author(s):  
Azita Yazdani ◽  
Ramin Ravangard ◽  
Roxana Sharifian
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Clinical Signs ◽  
Study Data ◽  
Mining Machine ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Mining Approach

The new coronavirus has been spreading since the beginning of 2020 and many efforts have been made to develop vaccines to help patients recover. It is now clear that the world needs a rapid solution to curb the spread of COVID-19 worldwide with non-clinical approaches such as data mining, enhanced intelligence, and other artificial intelligence techniques. These approaches can be effective in reducing the burden on the health care system to provide the best possible way to diagnose and predict the COVID-19 epidemic. In this study, data mining models for early detection of Covid-19 in patients were developed using the epidemiological dataset of patients and individuals suspected of having Covid-19 in Iran. C4.5, support vector machine, Naive Bayes, logistic regression, Random Forest, and k-nearest neighbor algorithm were used directly on the dataset using Rapid miner to develop the models. By receiving clinical signs, this model diagnosis the risk of contracting the COVID-19 virus. Examination of the models in this study has shown that the support vector machine with 93.41% accuracy is more efficient in the diagnosis of patients with COVID-19 pandemic, which is the best model among other developed models. Keywords: COVID-19, Data mining, Machine Learning, Artificial Intelligence, Classification

Heart rate variability: An index of brain processing in vegetative state? An artificial intelligence, data mining study

2010 ◽  
Vol 121 (12) ◽  
pp. 2024-2034 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
D. Conforti ◽  
G. Dolce
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Brain Processing

Data-Driven Decision Tree Classification for Product Portfolio Design Optimization

2009 ◽  
Vol 9 (4) ◽  
Author(s):  
Conrad S. Tucker ◽  
Harrison M. Kim
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Engineering Design ◽  
Optimization Techniques ◽  
Product Portfolio ◽  
Performance Expectations ◽  
Data Set ◽  
Tree Data ◽  
Portfolio Design ◽  
Product Concepts

The formulation of a product portfolio requires extensive knowledge about the product market space and also the technical limitations of a company’s engineering design and manufacturing processes. A design methodology is presented that significantly enhances the product portfolio design process by eliminating the need for an exhaustive search of all possible product concepts. This is achieved through a decision tree data mining technique that generates a set of product concepts that are subsequently validated in the engineering design using multilevel optimization techniques. The final optimal product portfolio evaluates products based on the following three criteria: (1) it must satisfy customer price and performance expectations (based on the predictive model) defined here as the feasibility criterion; (2) the feasible set of products/variants validated at the engineering level must generate positive profit that we define as the optimality criterion; (3) the optimal set of products/variants should be a manageable size as defined by the enterprise decision makers and should therefore not exceed the product portfolio limit. The strength of our work is to reveal the tremendous savings in time and resources that exist when decision tree data mining techniques are incorporated into the product portfolio design and selection process. Using data mining tree generation techniques, a customer data set of 40,000 responses with 576 unique attribute combinations (entire set of possible product concepts) is narrowed down to 46 product concepts and then validated through the multilevel engineering design response of feasible products. A cell phone example is presented and an optimal product portfolio solution is achieved that maximizes company profit, without violating customer product performance expectations.

A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease

2018 ◽  
Vol 22 (3) ◽  
pp. 225-242 ◽  
Author(s):  
K. Mathan ◽  
Priyan Malarvizhi Kumar ◽  
Parthasarathy Panchatcharam ◽  
Gunasekaran Manogaran ◽  
R. Varadharajan
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Heart Disease ◽  
Decision Tree ◽  
Gini Index ◽  
Mining Method ◽  
Data Mining Method ◽  
Tree Data ◽  
Neural Network Classifiers

scholarly journals Vertebral artery and posterior inferior cerebellar artery aneurysms: Results of microsurgical treatment of eighty patients

2019 ◽  
Vol 10 ◽  
pp. 227
Author(s):  
Yuri Pilipenko ◽  
Shalva Eliava ◽  
Dmitry Okishev ◽  
Elena Okisheva ◽  
Andronikos Spyrou
Keyword(s):  
Vertebral Artery ◽  
Vegetative State ◽  
Posterior Inferior Cerebellar Artery ◽  
Clinical Signs ◽  
Surgical Approaches ◽  
Microsurgical Treatment ◽  
Suboccipital Craniotomy ◽  
Cerebellar Artery ◽  
Angiographic Data ◽  
Pica Aneurysms

Background: The choice of surgical approaches and options for the microsurgical vertebral artery (VA) and posterior inferior cerebellar artery (PICA) aneurysms repair remains controversial. Methods: A retrospective analysis of the clinical, surgical, and angiographic data of 80 patients with VA and PICA aneurysms treated from 2012 to 2018 was performed. Results: The aneurysms were saccular in 50 cases (62.5%) and fusiform in 30 cases (37.5%). The median suboccipital craniotomy was the most common approach (73.8%). Retrosigmoid craniotomy was performed in 25% of patients. There were the following types of microsurgical operations: neck clipping (61.25%), clipping with the artery lumen formation (13.75%), trapping (10%), proximal clipping (5%), and deconstruction with anastomosis (10%). Fifty-seven (71.3%) patients were discharged without worsening of the clinical signs after surgery. The most common postoperative neurological disorder was palsy of IX and X cranial nerve revealed in 14 (17.5%) patients. No fatal outcomes or patients in vegetative state were identified. The complete occlusion of PICA and VA aneurysms according angiography was in 77 (96.3%) cases. Conclusion: Microsurgical treatment is an effective method for VA and PICA aneurysms. The majority of VA and PICA aneurysms do not require complex basal approaches. A thorough preoperative planning, reconstructive clipping techniques, and anastomoses creation, as well as patient selection based on the established algorithms and consultations with endovascular surgeons, may reduce the number of complications and increase the rate of complete microsurgical occlusion in VA and PICA aneurysms.

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