Prediction of Periventricular Leukomalacia in Neonates after Cardiac Surgery Using Machine Learning Algorithms

Jalali, Ali; Simpao, Allan F.; Gálvez, Jorge A.; Licht, Daniel J.; Nataraj, C.

doi:10.1007/s10916-018-1029-z

Cited by 20 publications

(29 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jalali at el. 23 also found that ML algorithms and wavelet analysis can be accurately applied to prediction models to assess the occurrence of periventricular leukomalacia in neonates postcardiac surgery. Artificial neural networks (ANNs) nonlinear technology are equally useful in identifying risk factors and predicting mortality in patients who have undergone cardiac surgery.…”

Section: Technology and Cardiac Surgerymentioning

confidence: 94%

“…Both Saeyeldin et al 21 and Ruiz‐Fernández et al 22 highlight the same message that information from AI about risks allows anticipation of treatment plans, overall significantly contributing to clinical decisions. Jalali at el 23 . also found that ML algorithms and wavelet analysis can be accurately applied to prediction models to assess the occurrence of periventricular leukomalacia in neonates postcardiac surgery.…”

Section: Technology and Cardiac Surgerymentioning

confidence: 98%

See 1 more Smart Citation

Artificial intelligence and cardiac surgery during COVID‐19 era

et al. 2021

View full text Add to dashboard Cite

The coronavirus disease 2019 (COVID‐19) pandemic has increased the burden on hospital staff world‐wide. Through the redistribution of scarce resources to these high‐priority cases, the cardiac sector has fallen behind. In efforts to reduce transmission, reduction in direct patient–physician contact has led to a backlog of cardiac cases. However, this accumulation of postponed or cancelled nonurgent cardiac care seems to be resolvable with the assistance of technology. From telemedicine to artificial intelligence (AI), technology has transformed healthcare systems nationwide. Telemedicine enables patient monitoring from a distance, while AI unveils a whole new realm of possibilities in clinical practice, examples include: traditional systems replacement with more efficient and accurate processing machines; automation of clerical process; and triage assistance through risk predictions. These possibilities are driven by deep and machine learning. The two subsets of AI are explored and limitations regarding “big data” are discussed. The aims of this review are to explore AI: the advancements in methodology; current integration in cardiac surgery or other clinical scenarios; and potential future roles, which are innately nearing as the COVID‐19 era urges alternative approaches for care.

show abstract

Section: Technology and Cardiac Surgerymentioning

confidence: 94%

Section: Technology and Cardiac Surgerymentioning

confidence: 98%

Artificial intelligence and cardiac surgery during COVID‐19 era

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Given the preoperative characteristics of a patient, one can follow the decision "path" along the branches of the tree, understanding at each step the sequential decisions taken, and also comprehend the characteristics of resulting patient cohorts with similar risk, as revealed by the tree's terminal leaves. The ability to understand the decisions of the OCT algorithm is in advantageous contrast with findings of various studies in similar settings showing the use of "black-box" deep learning solutions, [21][22][23][24][25][26][27] which, despite higher performance indicators, have architectural inner workings which are obscure not just for clinicians, but for AI experts as well. Such black-box methods cannot provide interpretable explanations of "why" a given patient is assigned a certain outcome risk.…”

Section: Commentmentioning

confidence: 99%

Adverse Outcomes Prediction for Congenital Heart Surgery: A Machine Learning Approach

Bertsimas

Zhuo

Dunn

et al. 2021

World J Pediatr Congenit Heart Surg

View full text Add to dashboard Cite

Objective: Risk assessment tools typically used in congenital heart surgery (CHS) assume that various possible risk factors interact in a linear and additive fashion, an assumption that may not reflect reality. Using artificial intelligence techniques, we sought to develop nonlinear models for predicting outcomes in CHS. Methods: We built machine learning (ML) models to predict mortality, postoperative mechanical ventilatory support time (MVST), and hospital length of stay (LOS) for patients who underwent CHS, based on data of more than 235,000 patients and 295,000 operations provided by the European Congenital Heart Surgeons Association Congenital Database. We used optimal classification trees (OCTs) methodology for its interpretability and accuracy, and compared to logistic regression and state-of-the-art ML methods (Random Forests, Gradient Boosting), reporting their area under the curve (AUC or c-statistic) for both training and testing data sets. Results: Optimal classification trees achieve outstanding performance across all three models (mortality AUC = 0.86, prolonged MVST AUC = 0.85, prolonged LOS AUC = 0.82), while being intuitively interpretable. The most significant predictors of mortality are procedure, age, and weight, followed by days since previous admission and any general preoperative patient risk factors. Conclusions: The nonlinear ML-based models of OCTs are intuitively interpretable and provide superior predictive power. The associated risk calculator allows easy, accurate, and understandable estimation of individual patient risks, in the theoretical framework of the average performance of all centers represented in the database. This methodology has the potential to facilitate decision-making and resource optimization in CHS, enabling total quality management and precise benchmarking initiatives.

show abstract

“…Table 1 summarizes and shows the characteristics of the studies. Fourteen studies evaluated the functionality of CDSSs in ICUs [51][52][53][54][55][56][57][58][59][60][61][62][63][64], three examined the applicability of databases in ICUs [65][66][67], one studied investigated the usefulness of EHRs [68], and another one considered CPOE [69]. Lastly, three analyzed the function of combinations of CDSS/CPOE and CDSS/EHR [70][71][72].…”

Section: Study Characteristicsmentioning

confidence: 99%

Applicability of Clinical Decision Support in Management among Patients Undergoing Cardiac Surgery in Intensive Care Unit: A Systematic Review

et al. 2021

View full text Add to dashboard Cite

The advances achieved in recent decades regarding cardiac surgery have led to a new risk that goes beyond surgeons' dexterity; postoperative hours are crucial for cardiac surgery patients and are usually spent in intensive care units (ICUs), where the patients need to be continuously monitored to adjust their treatment. Clinical decision support systems (CDSSs) have been developed to take this real-time information and provide clinical suggestions to physicians in order to reduce medical errors and to improve patient recovery. In this review, an initial total of 499 papers were considered after identification using PubMed, Web of Science, and CINAHL. Twenty-two studies were included after filtering, which included the deletion of duplications and the exclusion of titles or abstracts that were not of real interest. A review of these papers concluded the applicability and advances that CDSSs offer for both doctors and patients. Better prognosis and recovery rates are achieved by using this technology, which has also received high acceptance among most physicians. However, despite the evidence that well-designed CDSSs are effective, they still need to be refined to offer the best assistance possible, which may still take time, despite the promising models that have already been applied in real ICUs.

show abstract

Prediction of Periventricular Leukomalacia in Neonates after Cardiac Surgery Using Machine Learning Algorithms

Cited by 20 publications

References 27 publications

Artificial intelligence and cardiac surgery during COVID‐19 era

Artificial intelligence and cardiac surgery during COVID‐19 era

Adverse Outcomes Prediction for Congenital Heart Surgery: A Machine Learning Approach

Applicability of Clinical Decision Support in Management among Patients Undergoing Cardiac Surgery in Intensive Care Unit: A Systematic Review

Contact Info

Product

Resources

About