A Novel Software to Improve Healthcare Base on Predictive Analytics and Mobile Services for Cloud Data Centers

Mahdi, Muhammed Abaid; Al-Janabi, Samaher

doi:10.1007/978-3-030-23672-4_23

Cited by 40 publications

(13 citation statements)

References 20 publications

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“…With the accumulation of big data and the development of techniques for data storage, machine learning methods have attracted considerable research attention [24][25][26]. Several innovative and pragmatic machine learning methods such as random forest (RF) [27], gradient boosting machine (GBM) [28] and the least absolute shrinkage and selection operator (LASSO) [29] which is a type of linear regression using shrinkage, have been proposed, and these models have good prediction performance in medicine.…”

Section: Introductionmentioning

confidence: 99%

Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU

Kong

Lin

2020

BMC Med Inform Decis Mak

101

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Background Early and accurate identification of sepsis patients with high risk of in-hospital death can help physicians in intensive care units (ICUs) make optimal clinical decisions. This study aimed to develop machine learning-based tools to predict the risk of hospital death of patients with sepsis in ICUs. Methods The source database used for model development and validation is the medical information mart for intensive care (MIMIC) III. We identified adult sepsis patients using the new sepsis definition Sepsis-3. A total of 86 predictor variables consisting of demographics, laboratory tests and comorbidities were used. We employed the least absolute shrinkage and selection operator (LASSO), random forest (RF), gradient boosting machine (GBM) and the traditional logistic regression (LR) method to develop prediction models. In addition, the prediction performance of the four developed models was evaluated and compared with that of an existent scoring tool – simplified acute physiology score (SAPS) II – using five different performance measures: the area under the receiver operating characteristic curve (AUROC), Brier score, sensitivity, specificity and calibration plot. Results The records of 16,688 sepsis patients in MIMIC III were used for model training and test. Amongst them, 2949 (17.7%) patients had in-hospital death. The average AUROCs of the LASSO, RF, GBM, LR and SAPS II models were 0.829, 0.829, 0.845, 0.833 and 0.77, respectively. The Brier scores of the LASSO, RF, GBM, LR and SAPS II models were 0.108, 0.109, 0.104, 0.107 and 0.146, respectively. The calibration plots showed that the GBM, LASSO and LR models had good calibration; the RF model underestimated high-risk patients; and SAPS II had the poorest calibration. Conclusion The machine learning-based models developed in this study had good prediction performance. Amongst them, the GBM model showed the best performance in predicting the risk of in-hospital death. It has the potential to assist physicians in the ICU to perform appropriate clinical interventions for critically ill sepsis patients and thus may help improve the prognoses of sepsis patients in the ICU.

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Section: Introductionmentioning

confidence: 99%

Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU

Kong

Lin

2020

BMC Med Inform Decis Mak

101

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“…Data mining algorithms are classified into two functional types, predictive and descriptive [5], and eight application types, classification, estimation, prediction, correlation analysis, sequence, time sequence, description, and visualization [6]. The successful application of data mining in biomedical research provides reliable support for clinical decision-making (e.g., disease diagnosis, therapy selection, and disease prognosis prediction) and management decision-making (e.g., staffing, medical insurance, and quality control) [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery

Zhao

Cui

et al. 2020

BMC Med Inform Decis Mak

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Background: Surgical resection of pheochromocytoma may lead to high risk factors for intraoperative hemodynamic instability (IHD), which can be life-threatening. This study aimed to investigate the risk factors that could predict IHD during pheochromocytoma surgery by data mining. Method: Relief-F was used to select the most important features. The accuracies of seven data mining models (CART, C4.5, C5.0, and C5.0 boosted), random forest algorithm, Naive Bayes and logistic regression were compared, the cross-validation, hold-out, and bootstrap methods were used in the validation phase. The accuracy of these models was calculated independently by dividing the training and the test sets. Receiver-Operating Characteristic curves were used to obtain the area under curve (AUC). Result: Random forest had the highest AUC and accuracy values of 0.8636 and 0.8509, respectively. Then, we improved the random forest algorithm according to the classification of imbalanced data. Improved random forest model had the highest specificity and precision among all algorithms, including relatively higher sensitivity (recall) and the highest f1-score integrating recall and precision. The important attributes were body mass index, mean age, 24 h urine vanillylmandelic acid/upper normal limit value, tumor size and enhanced computed tomography difference. Conclusions: The improved random forest algorithm may be useful in predicting IHD risk factors in pheochromocytoma surgery. Data mining technologies are being increasingly applied in clinical and medical decision-making, and provide continually expanding support for the diagnosis, treatment, and prevention of various diseases.

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“…Real-world data can also be collected in quality registries as objective outcome data, e.g. laboratory results or ndings in physical examinations (21)(22)(23)(24) There is a lack of knowledge on whether the use of knowledge bases re ects the patient outcomes data in nationwide quality registries and whether burden of care, measured in Sweden as a Care Need Index in uences the register outcomes in any way (25). The aim of this study was to explore the effect of use of an online knowledge base on patient experiences and health care quality.…”

Section: Introductionmentioning

confidence: 99%

Use of online knowledge base in primary health care and correlation to health care quality: An observational study

Gerdesköld

Toth-Pal

Wårdh

et al. 2020

Preprint

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BackgroundEvidence-based information available at the point of care improves patient care outcomes. Online knowledge bases can increase the application of evidence-based medicine and influence patient outcome data which may be captured in quality registries. The aim of this study was to explore the effect of use of an online knowledge base on patient experiences and health care quality.MethodsThe study was conducted as a retrospective, observational study of 24 primary health care centers in Sweden exploring their use of an online knowledge base. Frequency of use was compared to patient outcomes in two national quality registries. A socio-economic Care Need Index was applied to assess whether the burden of care influenced the results from those quality registries. Non-parametric statistical methods and linear regression were used.ResultsFrequency of knowledge base use showed two groups: frequent and non-frequent users, with a significant use difference between the groups (p<.001). Outcome data showed significant higher values for all seven National Primary Care Patient Survey dimensions in the frequent compared to the non-frequent knowledge base users (p<.001), whereas 10 out of 11 parameters in the National Diabetes Register showed no differences between the groups (p>.05). Adjusting for Care Need Index had almost no effect on the outcomes for the groups.ConclusionsFrequent users of a national online knowledge base received higher ratings on patient experiences, but figures on health care quality in diabetes showed near to no correlation. The findings indicate that some effects may be attributed to the use of knowledge bases and requires a controlled evaluation.

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A Novel Software to Improve Healthcare Base on Predictive Analytics and Mobile Services for Cloud Data Centers

Cited by 40 publications

References 20 publications

Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU

Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU

Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery

Use of online knowledge base in primary health care and correlation to health care quality: An observational study

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