Go Green! Reusing Brain Monitoring Data Containing Missing Values: A Feasibility Study with Traumatic Brain Injury Patients

Feng, Mengling; Loy, Liang Yu; Zhang, Feng; Zhang, Zhuo; Vellaisamy, Kuralmani; Chin, Pei Loon; Guan, Cuntai; Shen, Liang; King, Nicolas Kon Kam; Lee, Kah Keow; Ang, Beng Ti

doi:10.1007/978-3-7091-0956-4_10

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…Lazy learners are a sub-type of incremental learning. These algorithms can model complex decision spaces that have hyper polygonal shapes and are not easily describable by others ML algorithms (3,30). J48 and Random Forest are widely used ML algorithms, which is a decision tree algorithm.…”

Section: Study Predictorsmentioning

confidence: 99%

Comparing of Data Mining Techniques for Predicting In-Hospital Mortality Among Patients with COVID-19

Shanbehzadeh¹,

Orooji²,

Kazemi-Arpanahi³

2021

jbe

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Introduction: The COVID-19 epidemic is currently fronting the worldwide health care systems with many qualms and unexpected challenges in medical decision-making and the effective sharing of medical resources. Machine Learning (ML)-based prediction models can be potentially advantageous to overcome these uncertainties. Objective: This study aims to train several ML algorithms to predict the COVID-19 in-hospital mortality and compare their performance to choose the best performing algorithm. Finally, the contributing factors scored using some feature selection methods. Material and Methods: Using a single-center registry, we studied the records of 1353 confirmed COVID-19 hospitalized patients from Ayatollah Taleghani hospital, Abadan city, Iran. We applied six feature scoring techniques and nine well-known ML algorithms. To evaluate the models’ performances, the metrics derived from the confusion matrix calculated. Results: The study participants were 1353 patients, the male sex found to be higher than the women (742 vs. 611), and the median age was 57.25 (interquartile 18-100). After feature scoring, out of 54 variables, absolute neutrophil/lymphocyte count and loss of taste and smell were found the top three predictors. On the other hand, platelet count, magnesium, and headache gained the lowest importance for predicting the COVID-19 mortality. Experimental results indicated that the Bayesian network algorithm with an accuracy of 89.31% and a sensitivity of 64.2 % has been more successful in predicting mortality. Conclusion: ML provides a reasonable level of accuracy in predicting. So, using the ML-based prediction models facilitate more responsive health systems and would be beneficial for timely identification of vulnerable patients to inform appropriate judgment by the health care providers. Abbreviation: Coronavirus Disease 2019 (COVID‐19), World Health Organization (WHO), Machine Learning (ML), Artificial Intelligence (AI), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Locally Weighted Learning (LWL), Clinical Decision Support System (CDSS)

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Section: Study Predictorsmentioning

confidence: 99%

Comparing of Data Mining Techniques for Predicting In-Hospital Mortality Among Patients with COVID-19

Shanbehzadeh¹,

Orooji²,

Kazemi-Arpanahi³

2021

jbe

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“…The node leaves in the constructed regression trees are composed of multivariate linear models. This algorithm has proved its efficiency in several predicting applications, such as, the case of predicting missing values in brains suffering from a traumatic lesion [7], and the case of the recipes recommendation [8].…”

Section: Regression Trees (M5p)mentioning

confidence: 99%

Like-tasted user groups to predict ratings in recommender systems

Jaffali

Jamoussi

Smaïli

et al. 2020

Soc. Netw. Anal. Min.

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Recommendation Systems have gained the intention of many researchers due to the growth of the business of personalizing, sorting and suggesting products to customers. Most of rating prediction in recommendation systems are based on customer preferences or on the historical behavior of similar customers. The similarity between customers is generally measured by the number of times customers liked or disliked the same item. Given the huge number and the variety of items, many customers cannot be considered as similar, as they did not evaluate the same items, even if they have similar tastes. This paper presents a new method of rating prediction in recommendation systems. The proposed method starts by identifying the taste directions or the interest centers based on the users' demographic information combined with their previous evaluations. Thus, it uses the Principal Component Analysis (PCA) to retrieve the major taste orientations. According to these orientations, user groups are created. Then, for each group, it generates a prediction model, that will be used to predict unknown rates of users within the corresponding group. In order to assess the accuracy of the proposed method, we compare its

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“…There are publications also from Australia [38,[198][199][200], Austria [53,85], Brazil [58], Canada [93,118,132,135,139], China [9, 201,202], Croatia [203], the Czech Republic [204], Denmark [134]; Finland [134], France [43,77,114,205,206], Greece [207], Hungary [141], India [208], Japan [140,146,167,209], Portugal [64]; Russia [210,211], Singapore [2,4,10,16,74,212,213]; Serbia [214]; South Africa [26,28,35], South Korea [127], Sweden [90,134,215], Switzerland [11,216,217]...…”

Section: Clinical Applicationsmentioning

confidence: 99%

Systematic and Comprehensive Literature Review of Publications on Direct Cerebral Oxygenation Monitoring

Lang

Jaeger²

2013

TOCCMJ

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This review has been compiled to assess publications related to the clinical application of direct cerebral tissue oxygenation (pbtO2) monitoring published in international, peer-reviewed scientific journals, or major meeting reports published as journal supplements. Its goal was to extract relevant, i.e. positive and negative, information on indications, clinical application, safety issues and impact on clinical situations, as well as treatment strategies in neurosurgery, neurosurgical anaesthesiology, neurosurgical intensive care, neurology, and related specialties. For completeness' sake it also presents related basic science research and case reports. This review is an update of its previous edition published elsewhere in 2007. This review reflects publications from 2004 to 2012. Only relevant publications prior to 2004, which explicitly addressed or systematically examined the above issues, are included in this review and are listed in the reference section. Based on 349 citations it is the most comprehensive review available on direct cerebral oxygen monitoring to this date.Keywords: Review, cerebral oxygenation, raumedic, licox, neurotrend, cerebral partial pressure of oxygen, brain tissue oxygenation. METHODSThis review is based on a systematic "Medline" literature search using the following search terms: licox, neurotrend, neurovent, raumedic, brain tissue oxygen, brain tissue oxygenation, cerebral oxygenation, cerebral tissue oxygen, cerebral partial pressure of oxygen, btO2, ptiO2, pti(O)2, ptiO(2), pbrO2, PBr(O2), P(bt)O(2), P(br)O(2), PbrO(2), BTpO2, tP(O2), PbO2, PbtO2, Pbto, PbtO(2).Only few publications were not retrieved using this technique. They were, however, identified through personal communication with scientists in the field, meeting proceedings and reviewer assignments. This strategy provided sufficient certainty that relevant papers have not been missed. Unpublished meeting abstracts and published conference proceedings were excluded for this review expect for few which provided comparative studies between different probes. To address relevant matters, related to specific pbtO2 issues, selected non-peer reviewed papers published in supplements were also used only when no other peer-reviewed information was available. RESULTSThis review is based on a previous review [1]. It is currently based on 349 papers, 274 more papers compared to its previous version in 2007, which warrants the update.

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Go Green! Reusing Brain Monitoring Data Containing Missing Values: A Feasibility Study with Traumatic Brain Injury Patients

Cited by 5 publications

References 21 publications

Comparing of Data Mining Techniques for Predicting In-Hospital Mortality Among Patients with COVID-19

Comparing of Data Mining Techniques for Predicting In-Hospital Mortality Among Patients with COVID-19

Like-tasted user groups to predict ratings in recommender systems

Systematic and Comprehensive Literature Review of Publications on Direct Cerebral Oxygenation Monitoring

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