Comparing machine learning and regression models for mortality prediction based on the Hungarian Myocardial Infarction Registry

Piros, Peter; Ferenci, Tamás; Fleiner, Rita; Andréka, Péter; Fujita, Hamido; Főző, László; Kovács, Levente; Jánosi, András

doi:10.1016/j.knosys.2019.04.027

Cited by 30 publications

(12 citation statements)

References 20 publications

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“…According to the top features shown in Table 8 , HLAMMs and the number of years on dialysis before transplant (VINTAGE) were the most important features with a relative importance of over 10%. This observation has been confirmed in other studies [ 32 , 36 ]. Donor-recipient CMV status, donor-recipient race, end-stage renal disease diagnosis (ESRDDXSIMP), and functional status of the recipient were ranked as having medium importance with a relative score between 5% and 10%.…”

Section: Resultssupporting

confidence: 91%

Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study

Naqvi¹,

Tennankore²,

Vinson³

et al. 2021

J Med Internet Res

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Background Kidney transplantation is the optimal treatment for patients with end-stage renal disease. Short- and long-term kidney graft survival is influenced by a number of donor and recipient factors. Predicting the success of kidney transplantation is important for optimizing kidney allocation. Objective The aim of this study was to predict the risk of kidney graft failure across three temporal cohorts (within 1 year, within 5 years, and after 5 years following a transplant) based on donor and recipient characteristics. We analyzed a large data set comprising over 50,000 kidney transplants covering an approximate 20-year period. Methods We applied machine learning–based classification algorithms to develop prediction models for the risk of graft failure for three different temporal cohorts. Deep learning–based autoencoders were applied for data dimensionality reduction, which improved the prediction performance. The influence of features on graft survival for each cohort was studied by investigating a new nonoverlapping patient stratification approach. Results Our models predicted graft survival with area under the curve scores of 82% within 1 year, 69% within 5 years, and 81% within 17 years. The feature importance analysis elucidated the varying influence of clinical features on graft survival across the three different temporal cohorts. Conclusions In this study, we applied machine learning to develop risk prediction models for graft failure that demonstrated a high level of prediction performance. Acknowledging that these models performed better than those reported in the literature for existing risk prediction tools, future studies will focus on how best to incorporate these prediction models into clinical care algorithms to optimize the long-term health of kidney recipients.

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Section: Resultssupporting

confidence: 91%

Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study

Naqvi¹,

Tennankore²,

Vinson³

et al. 2021

J Med Internet Res

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“…Ten (45.45%) studies compared the performances of various ML algorithms followed by LR and RF which were used as comparators in six (54.54%) and five (54.54%) studies, in that order. Nevertheless, in two studies by Khera et al 28 and Piros et al, 42 ML models compared with logistic regression did not show increased performance.…”

Section: Comparisonsmentioning

confidence: 88%

Predicting Major Adverse Cardiovascular Events in Acute Coronary Syndrome: A Scoping Review of Machine Learning Approaches

et al. 2022

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Background: Acute coronary syndrome is the topmost cause of death worldwide; therefore, it is necessary to predict major adverse cardiovascular events and cardiovascular deaths in patients with acute coronary syndrome to make correct and timely clinical decisions. Objective: The current review aimed to highlight algorithms and important predictor variables through examining those studies which used machine learning algorithms for predicting major adverse cardiovascular events in patients with acute coronary syndrome. Methods: In order to predict major adverse cardiovascular events in patients with acute coronary syndrome, the preferred reporting items for scoping reviews guidelines were used. PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore databases were searched for articles published between 2005 and 2021. The findings of the studies are presented in the form of a narrative synthesis of evidence. Results: According to the results, 14 (63.64%) studies did not perform external validation and only used registry data. The algorithms used in this study comprised, inter alia, Regression Logistic, Random Forest, Boosting Ensemble, Non-Boosting Ensemble, Decision Trees, and Naive Bayes. Multiple studies (N=20) achieved a high Area under the ROC Curve between 0.8 to 0.99 in predicting mortality and major adverse cardiovascular events. The predictor variables used in these studies were divided into demographic, clinical, and therapeutic features. However, no study reported the integration of machine learning model into clinical practice. Conclusion: Machine learning algorithms rendered acceptable results to predict major adverse cardiovascular events and mortality outcomes in patients with acute coronary syndrome. However, these approaches have never been integrated into clinical practice. Further research is required to develop feasible and effective machine learning prediction models to measure their potentially important implications for optimizing the quality of care in patients with acute coronary syndrome. Keywords: Machine learning, Mortality, Acute coronary syndrome, Myocardial infarction, Prediction, Major adverse cardiac event.

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“…In this context, a major difference between statistical analysis and ML is that the latter often sacrifices interpretability (or explainability) in favor of the model's predictive power (Song et al, 2021b), even though both ML and statistical analysis may perform equally well on some datasets. For example, area under the receiver operation characteristic (AUROC) curves have been compared for models predicting various diseases, revealing values of 0.736 (ML) vs. 0.748 (logistic regression) when predicting acute kidney disease (Song et al, 2021b), 0.837 (neural net models) vs. 0.836 (regression models) when predicting infraction mortality (Piros et al, 2019a), and 0.926 (ANN) vs. 0.869 (Cox regression) when predicting the outcome of COVID-19 (Abdulaal et al, 2020b).…”

Section: Descriptive Modelsmentioning

confidence: 99%

The use of predictive models to develop chromatography-based purification processes

Bernau

Knödler

Emonts

et al. 2022

Front. Bioeng. Biotechnol.

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Chromatography is the workhorse of biopharmaceutical downstream processing because it can selectively enrich a target product while removing impurities from complex feed streams. This is achieved by exploiting differences in molecular properties, such as size, charge and hydrophobicity (alone or in different combinations). Accordingly, many parameters must be tested during process development in order to maximize product purity and recovery, including resin and ligand types, conductivity, pH, gradient profiles, and the sequence of separation operations. The number of possible experimental conditions quickly becomes unmanageable. Although the range of suitable conditions can be narrowed based on experience, the time and cost of the work remain high even when using high-throughput laboratory automation. In contrast, chromatography modeling using inexpensive, parallelized computer hardware can provide expert knowledge, predicting conditions that achieve high purity and efficient recovery. The prediction of suitable conditions in silico reduces the number of empirical tests required and provides in-depth process understanding, which is recommended by regulatory authorities. In this article, we discuss the benefits and specific challenges of chromatography modeling. We describe the experimental characterization of chromatography devices and settings prior to modeling, such as the determination of column porosity. We also consider the challenges that must be overcome when models are set up and calibrated, including the cross-validation and verification of data-driven and hybrid (combined data-driven and mechanistic) models. This review will therefore support researchers intending to establish a chromatography modeling workflow in their laboratory.

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Comparing machine learning and regression models for mortality prediction based on the Hungarian Myocardial Infarction Registry

Cited by 30 publications

References 20 publications

Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study

Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study

Predicting Major Adverse Cardiovascular Events in Acute Coronary Syndrome: A Scoping Review of Machine Learning Approaches

The use of predictive models to develop chromatography-based purification processes

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