Cross-Industry Standard Process for data mining is applicable to the lung cancer surgery domain, improving decision making as well as knowledge and quality management

Rivo, Eduardo; Fuente, Javier de la; Rivo, Ángel; García-Fontán, Eva; Cañizares, Miguel-Ángel; Gil, Pedro

doi:10.1007/s12094-012-0764-8

Cited by 17 publications

(7 citation statements)

References 11 publications

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“…In lung cancer, the applicability of information discovery in database methods, based on data mining techniques, has been tested previously [ 26 , 27 ]. Rivo et al [ 27 ] reported a data mining project developed on a data warehouse containing records for 501 patients operated for lung cancer with curative intention. Data mining objectives were stated so as to discover risk factors for surgical mortality.…”

Section: Discussionmentioning

confidence: 99%

A data mining based clinical decision support system for survival in lung cancer

Pontes

Núñez

Rubio

et al. 2021

Rep Pract Oncol Radiother.

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Background A clinical decision support system (CDSS ) has been designed to predict the outcome (overall survival) by extracting and analyzing information from routine clinical activity as a complement to clinical guidelines in lung cancer patients. Materials and methods Prospective multicenter data from 543 consecutive (2013–2017) lung cancer patients with 1167 variables were used for development of the CDSS. Data Mining analyses were based on the XGBoost and Generalized Linear Models algorithms. The predictions from guidelines and the CDSS proposed were compared. Results Overall, the highest (> 0.90) areas under the receiver-operating characteristics curve AUCs for predicting survival were obtained for small cell lung cancer patients. The AUCs for predicting survival using basic items included in the guidelines were mostly below 0.70 while those obtained using the CDSS were mostly above 0.70. The vast majority of comparisons between the guideline and CDSS AUCs were statistically significant (p < 0.05). For instance, using the guidelines, the AUC for predicting survival was 0.60 while the predictive power of the CDSS enhanced the AUC up to 0.84 (p = 0.0009). In terms of histology, there was only a statistically significant difference when comparing the AUCs of small cell lung cancer patients (0.96) and all lung cancer patients with longer (≥ 18 months) follow up (0.80; p < 0.001). Conclusions The CDSS successfully showed potential for enhancing prediction of survival. The CDSS could assist physicians in formulating evidence-based management advice in patients with lung cancer, guiding an individualized discussion according to prognosis.

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Section: Discussionmentioning

confidence: 99%

A data mining based clinical decision support system for survival in lung cancer

Pontes

Núñez

Rubio

et al. 2021

Rep Pract Oncol Radiother.

View full text Add to dashboard Cite

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“…Applying data mining and knowledge discovery as an emerging strategy has increased enormously over the past few years and has been used to predict the outcome of various pharmaceutical and medical treatments [27,34,35]. Individuals respond differently to similar treatments presumably since individuals have different biological and genetic determinants of CVD.…”

Section: Discussionmentioning

confidence: 99%

The effect of aged garlic extract on the atherosclerotic process – a randomized double-blind placebo-controlled trial

Wlosinska

Nilsson

Hlebowicz

et al. 2020

BMC Complement Med Ther

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Background One of the most serious secondary manifestations of Cardiovascular Disease (CVD) is coronary atherosclerosis. This study aimed to evaluate whether aged garlic extract (AGE) can influence coronary artery calcification (CAC) and to predict the individual effect of AGE using a standard process for data mining (CRISP–DM). Method This was a single-center parallel randomized controlled study in a university hospital in Europe. Patients were randomized, in a double-blind manner, through a computer-generated randomization chart. Patients with a Framingham risk score ≥ 10 after CT scan (n = 104) were randomized to an intake of placebo or AGE (2400 mg daily) for 1 year. Main outcome measures were changes in CAC score and secondary outcome measures changes in blood pressure, fasting blood glucose, blood lipids and inflammatory biomarkers. Result 104 patients were randomized and 46 in the active group and 47 in the placebo group were analyzed. There was a significant (p < 0.05) change in CAC progression (OR: 2.95 [1.05–8.27]), blood glucose (OR: 3.1 [1.09–8.85]) and IL-6 (OR 2.56 [1.00–6.53]) in favor of the active group. There was also a significant (p = 0.027) decrease in systolic blood pressure in the AGE group, from a mean of 148 (SD: 19) mmHg at 0 months, to 140 (SD: 15) mmHg after 12 months. The AGE Algorithm, at a selected probability cut-off value of 0.5, the accuracy score for CAC progression was 80%, precision score of 79% and recall score 83%. The score for blood pressure was 74% (accuracy, precision and recall). There were no side-effects in either group. Conclusions AGE inhibits CAC progression, lowers IL–6, glucose levels and blood pressure in patients at increased risk of cardiovascular events in a European cohort. An algorithm was made and was used to predict with 80% precision which patient will have a significantly reduced CAC progression using AGE. The algorithm could also predict with a 74% precision which patient will have a significant blood pressure lowering effect pressure using AGE. Trial registration Clinical trials NCT03860350, retrospectively registered (1/32019).

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“…Step 3: Implementing the selected data mining techniques In this step, to select data mining models for data analysis, various studies in this field were reviewed [12][13][14]. According to the studies and the type and quality of data, appropriate models were selected.…”

Section: Step1: Data Identificationmentioning

confidence: 99%

Predicting Mortality of COVID-19 Patients based on Data Mining Techniques

Moulaei

2021

J Biomed Phys Eng

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If Coronavirus (COVID-19) is not predicted, managed, and controlled timely, the health systems of any country and their people will face serious problems. Predictive models can be helpful in health resource management and prevent outbreak and death caused by COVID-19. The present study aimed at predicting mortality in patients with COVID-19 based on data mining techniques. To do this study, the mortality factors of COVID-19 patients were first identified based on different studies. These factors were confirmed by specialist physicians. Based on the confirmed factors, the data of COVID-19 patients were extracted from 850 medical records. Decision tree (J48), MLP, KNN, random forest, and SVM data mining models were used for prediction. The models were evaluated based on accuracy, precision, specificity, sensitivity, and the ROC curve. According to the results, the most effective factor used to predict the death of COVID-19 patients was dyspnea. Based on ROC (1.000), accuracy (99.23%), precision (99.74%), sensitivity (98.25%) and specificity (99.84%), the random forest was the best model in predicting of mortality than other models. After the random forest, KNN5, MLP, and J48 models were ranked next, respectively. Data analysis of COVID-19 patients can be a suitable and practical tool for predicting the mortality of these patients. Given the sensitivity of medical science concerning maintaining human life and lack of specialized human resources in the health system, using the proposed models can increase the chances of successful treatment, prevent early death and reduce the costs associated with long treatments for patients, hospitals and the insurance industry.

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Cross-Industry Standard Process for data mining is applicable to the lung cancer surgery domain, improving decision making as well as knowledge and quality management

Abstract: The CRISP-DM process model is very suitable for lung cancer surgery analysis, improving decision making as well as knowledge and quality management.

Cited by 17 publications

References 11 publications

A data mining based clinical decision support system for survival in lung cancer

A data mining based clinical decision support system for survival in lung cancer

The effect of aged garlic extract on the atherosclerotic process – a randomized double-blind placebo-controlled trial

Predicting Mortality of COVID-19 Patients based on Data Mining Techniques

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