A Naive Bayes machine learning approach to risk prediction using censored, time‐to‐event data

Wolfson, Julian; Bandyopadhyay, Sunayan; Elidrisi, Mohamed; Vazquez‐Benitez, Gabriela; Vock, David M.; Musgrove, Donald; Adomavicius, Gediminas; Johnson, Paul E.; O’Connor, Patrick J.

doi:10.1002/sim.6526

Cited by 47 publications

(38 citation statements)

References 45 publications

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“…Astonishingly, the performance of a NBN is somewhat competitive given that this is obviously an unrealistic assumption (5,6).…”

Section: Introductionmentioning

confidence: 99%

Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review

Moghbeli¹

2016

Acta Inform Med

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articles remained. They were reviewed in full text and 15 articles were excluded. Eventually, 23 articles were selected which met our eligibility criteria and were included in this study. Result: In this article, the use of NBN in predicting diseases was described. Finally, the results were reported in terms of Accuracy, Sensitivity, Specificity and Area under ROC curve (AUC). The last column in Table 2

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“…Astonishingly, the performance of a NBN is somewhat competitive given that this is obviously an unrealistic assumption (5,6).…”

Section: Introductionmentioning

confidence: 99%

Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review

Moghbeli¹

2016

Acta Inform Med

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“…Recently, the importance of platelet function assays before, during and after CABG surgery received an increasing interest in the literature [36,37].…”

Section: Discussionmentioning

confidence: 99%

“…(Table 1) The Spearman correlation is nonparametric with the exact sampling distribution obtained without requiring knowledge of the joint probability distribution of the parameters. Correlation analysis using Spearman's correlation coefficients (Table 1) [35][36][37][38][39][40][41][42][43][44][45]. PLTEM, was calculated by subtracting FIBTEM from EXTEM to distinguish thrombocytopenia from hypofibrinogenemia [8].…”

Section: Correlationmentioning

confidence: 99%

“…In this context, a correlation between oxaliplatinum exposure and thrombocytopenia has been described with thrombocytopenia related to chemotherapeutic agents, caused by a mild bone marrow suppression (toxicity to megakaryocytic progenitors) occurring in 45-77% of the cases [32,33]. Additionally, oxaliplatin has also been related with drug-induced, immune mediated thrombocytopenia (DITP) and splenic sequestration of platelets due to portal hypertension based on sinusoidal injury [34][35][36][37].…”

Section: Maximum Clot Firmness Decreased During Surgery But Significamentioning

confidence: 99%

“…With more frequent use of hypothermia in clinical practice and concomitant use of platelet-inhibiting drugs, there is a growing need to understand the ramifications of platelet-inhibiting drugs on coagulation and platelet function [36,37].…”

Section: Fundamental Aspects Of Hypothermiamentioning

confidence: 99%

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Platelets, from sample to big data

Poucke¹

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'WE CANNOT SOLVE OUR PROBLEMS WITH THE SAME THINKING WE USED WHEN WE CREATED THEM.'Albert Einstein > E X P L O R I N G G R A N U L A R I T Y I N P L AT E L E T R E S E A R C H 4 E X P L O R I N G G R A N U L A R I T Y I N P L AT E L E T R E S E A R C H 5 TABLE OF CONTENTS CHAPTER 1Introduction 7 CHAPTER 2 Hypothermia: effects on platelet function and hemostasis 15CHAPTER 3 Response of platelet concentrates to pressure and temperature 27 changes without impairment of the in vitro function CHAPTER 4 Platelet function during hypothermia in experimental mock circulation 39CHAPTER 5 Early platelet recovery following cardiac surgery with cardiopulmonary 51 bypass CHAPTER 6 Thrombin generation and platelet activation in cytoreductive surgery 67 combined with hyperthermic intra-peritoneal chemotherapy CHAPTER 7 Normalization methods in time series of platelet function assays.91From sample to big data 117 CHAPTER 9 Discussion and valorization 145Nederlandse samenvatting 159 English summary 165Publications 171Biography 177Thanks and acknowledgements 181CHAPTER 8 E X P L O R I N G G R A N U L A R I T Y I N P L AT E L E T R E S E A R C H 6 E X P L O R I N G G R A N U L A R I T Y I N P L AT E L E T R E S E BACKGROUNDFor computationally managing the various levels of detail in biomedical data, biological granularity is indispensable for both dealing effectively with large amounts of data and for structuring the knowledge to analyze and vertically integrate, across the different levels of granularity. Managing granularities of data in the field of platelet research requires specific methodologies to evolve to new knowledge applicable for clinical practice. In this thesis, the author attempted to find inflection points for innovation. Current inflection points in biomedical research are related with the capacity for description and collecting data, the efficiency of compiling, organizing, manipulating these data and extracting true understanding of fundamental biological processes, and insights into human health and disease, from them. PLATELET BIOLOGYPlatelets (2~4 microns in diameter) are essential effectors of cellular hemostasis in humans and other mammals. Although the relevance of platelets in hemostasis and thrombosis is well established, platelets also contribute to host inflammatory and immune responses in infections or injury [1]. Suggested by comparative biology of the innate antibacterial and primitive hemostatic systems of invertebrates, the anucleate human platelet is a specialized cell fragment unique to mammals. Nonmammalian vertebrates, such as fish and birds, have nucleated platelets. Fig. 1.Invertebrates have an even more primitive blood cell, the amebocyte. The amebocyte is the single blood cell of invertebrates with a multitude of functions. The presence of platelets in fetal blood by 12 weeks of gestation suggests that platelet functions are useful to the fetus [2]. Platelets lack nuclei, but perform various vital functions of nucleated cells. Platelets can generate new cell bodies packed with respiring m...

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Comparing LASSO and random forest models for predicting neurological dysfunction among fluoroquinolone users

Ellis

Hubbard

Willis

et al. 2021

Pharmacoepidemiology and Drug

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Background Fluoroquinolones are associated with central (CNS) and peripheral (PNS) nervous system symptoms, and predicting the risk of these outcomes may have important clinical implications. Both LASSO and random forest are appealing modeling methods, yet it is not clear which method performs better for clinical risk prediction. Purpose To compare models developed using LASSO versus random forest for predicting neurological dysfunction among fluoroquinolone users. Methods We developed and validated risk prediction models using claims data from a commercially insured population. The study cohort included adults dispensed an oral fluoroquinolone, and outcomes were CNS and PNS dysfunction. Model predictors included demographic variables, comorbidities and medications known to be associated with neurological symptoms, and several healthcare utilization predictors. We assessed the accuracy and calibration of these models using measures including AUC, calibration curves, and Brier scores. Results The underlying cohort contained 16 533 (1.18%) individuals with CNS dysfunction and 46 995 (3.34%) individuals with PNS dysfunction during 120 days of follow‐up. For CNS dysfunction, LASSO had an AUC of 0.81 (95% CI: 0.80, 0.82), while random forest had an AUC of 0.80 (95% CI: 0.80, 0.81). For PNS dysfunction, LASSO had an AUC of 0.75 (95% CI: 0.74, 0.76) versus an AUC of 0.73 (95% CI: 0.73, 0.74) for random forest. Both LASSO models had better calibration, with Brier scores 0.17 (LASSO) versus 0.20 (random forest) for CNS dysfunction and 0.20 (LASSO) versus 0.25 (random forest) for PNS dysfunction. Conclusions LASSO outperformed random forest in predicting CNS and PNS dysfunction among fluoroquinolone users, and should be considered for modeling when the cohort is modest in size, when the number of model predictors is modest, and when predictors are primarily binary.

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A Naive Bayes machine learning approach to risk prediction using censored, time‐to‐event data

Cited by 47 publications

References 45 publications

Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review

Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review

Platelets, from sample to big data

Comparing LASSO and random forest models for predicting neurological dysfunction among fluoroquinolone users

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