Machine Learning Improves Risk Stratification After Acute Coronary Syndrome

Machine learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research

The maternal cardiovascular system undergoes functional and structural adaptations during pregnancy and postpartum to support increased metabolic demands of offspring and placental growth, labor, and delivery, as well as recovery from childbirth. Thus, pregnancy imposes physiological stress upon the maternal cardiovascular system, and in the absence of an appropriate response it imparts potential risks for cardiovascular complications and adverse outcomes. The proportion of pregnancy-related maternal deaths from cardiovascular events has been steadily increasing, contributing to high rates of maternal mortality. Despite advances in cardiovascular physiology research, there is still no comprehensive understanding of maternal cardiovascular adaptations in healthy pregnancies. Furthermore, current approaches for the prognosis of cardiovascular complications during pregnancy are limited. Machine learning (ML) offers new and effective tools for investigating mechanisms involved in pregnancy-related cardiovascular complications as well as the development of potential therapies. The main goal of this review is to summarize existing research that uses ML to understand mechanisms of cardiovascular physiology during pregnancy and develop prediction models for clinical application in pregnant patients. We also provide an overview of ML platforms that can be used to comprehensively understand cardiovascular adaptations to pregnancy and discuss the interpretability of ML outcomes, the consequences of model bias, and the importance of ethical consideration in ML use.

Machine learning to identify a composite indicator to predict cardiac death in ischemic heart disease

BACKGROUND

Machine learning (ML) employs algorithms that learn from data, building models with the potential to predict events by aggregating a large number of variables and assessing their complex interactions. The aim of this study is to assess ML potential in identifying patients with ischemic heart disease (IHD) at high risk of cardiac death (CD).

METHODS

3987 (mean age 68 ± 11) hospitalized IHD patients were enrolled. We implemented and compared various ML models and their combination into ensembles. Model output constitutes a new ML indicator to be employed for stratification. Primary variable importance was assessed with ablation tests.

RESULTS

An ensemble classifier combining three ML models achieved the best performance to predict CD (AUROC of 0.830, F1-macro of 0.726). ML indicator use through Cox survival analysis outperformed the 18 variables individually, producing a better stratification compared to standard multivariate analysis (improvement of ∼20%). Patients in the low risk group defined through ML indicator had a significantly higher survival (88.8% versus 29.1%). The main variables identified were Dyslipidemia, LVEF, Previous CABG, Diabetes, Previous Myocardial Infarction, Smoke, Documented resting or exertional ischemia, with an AUROC of 0.791 and an F1-score of 0.674, lower than that of 18 variables. Both code and clinical data are freely available with this article.

CONCLUSION

ML may allow a faster, low-cost and reliable evaluation of IHD patient prognosis by inclusion of more predictors and identification of those more significant, improving outcome prediction towards the development of precision medicine in this clinical field.

ChatGPT provides inconsistent risk-stratification of patients with atraumatic chest pain

BACKGROUND

ChatGPT-4 is a large language model with promising healthcare applications. However, its ability to analyze complex clinical data and provide consistent results is poorly known. Compared to validated tools, this study evaluated ChatGPT-4's risk stratification of simulated patients with acute nontraumatic chest pain.

METHODS

Three datasets of simulated case studies were created: one based on the TIMI score variables, another on HEART score variables, and a third comprising 44 randomized variables related to non-traumatic chest pain presentations. ChatGPT-4 independently scored each dataset five times. Its risk scores were compared to calculated TIMI and HEART scores. A model trained on 44 clinical variables was evaluated for consistency.

RESULTS

ChatGPT-4 showed a high correlation with TIMI and HEART scores (r = 0.898 and 0.928, respectively), but the distribution of individual risk assessments was broad. ChatGPT-4 gave a different risk 45-48% of the time for a fixed TIMI or HEART score. On the 44-variable model, a majority of the five ChatGPT-4 models agreed on a diagnosis category only 56% of the time, and risk scores were poorly correlated (r = 0.605).

CONCLUSION

While ChatGPT-4 correlates closely with established risk stratification tools regarding mean scores, its inconsistency when presented with identical patient data on separate occasions raises concerns about its reliability. The findings suggest that while large language models like ChatGPT-4 hold promise for healthcare applications, further refinement and customization are necessary, particularly in the clinical risk assessment of atraumatic chest pain patients.

ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review

Cardiovascular diseases (CVD) are a leading cause of death globally, and result in significant morbidity and reduced quality of life. The electrocardiogram (ECG) plays a crucial role in CVD diagnosis, prognosis, and prevention; however, different challenges still remain, such as an increasing unmet demand for skilled cardiologists capable of accurately interpreting ECG. This leads to higher workload and potential diagnostic inaccuracies. Data-driven approaches, such as machine learning (ML) and deep learning (DL) have emerged to improve existing computer-assisted solutions and enhance physicians' ECG interpretation of the complex mechanisms underlying CVD. However, many ML and DL models used to detect ECG-based CVD suffer from a lack of explainability, bias, as well as ethical, legal, and societal implications (ELSI). Despite the critical importance of these Trustworthy Artificial Intelligence (AI) aspects, there is a lack of comprehensive literature reviews that examine the current trends in ECG-based solutions for CVD diagnosis or prognosis that use ML and DL models and address the Trustworthy AI requirements. This review aims to bridge this knowledge gap by providing a systematic review to undertake a holistic analysis across multiple dimensions of these data-driven models such as type of CVD addressed, dataset characteristics, data input modalities, ML and DL algorithms (with a focus on DL), and aspects of Trustworthy AI like explainability, bias and ethical considerations. Additionally, within the analyzed dimensions, various challenges are identified. To these, we provide concrete recommendations, equipping other researchers with valuable insights to understand the current state of the field comprehensively.

Data analytics approach for short- and long-term mortality prediction following acute non-ST-elevation myocardial infarction (NSTEMI) and Unstable Angina (UA) in Asians

BACKGROUND

Traditional risk assessment tools often lack accuracy when predicting the short- and long-term mortality following a non-ST-segment elevation myocardial infarction (NSTEMI) or Unstable Angina (UA) in specific population.

OBJECTIVE

To employ machine learning (ML) and stacked ensemble learning (EL) methods in predicting short- and long-term mortality in Asian patients diagnosed with NSTEMI/UA and to identify the associated features, subsequently evaluating these findings against established risk scores.

METHODS

We analyzed data from the National Cardiovascular Disease Database for Malaysia (2006-2019), representing a diverse NSTEMI/UA Asian cohort. Algorithm development utilized in-hospital records of 9,518 patients, 30-day data from 7,133 patients, and 1-year data from 7,031 patients. This study utilized 39 features, including demographic, cardiovascular risk, medication, and clinical features. In the development of the stacked EL model, four base learner algorithms were employed: eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF), with the Generalized Linear Model (GLM) serving as the meta learner. Significant features were chosen and ranked using ML feature importance with backward elimination. The predictive performance of the algorithms was assessed using the area under the curve (AUC) as a metric. Validation of the algorithms was conducted against the TIMI for NSTEMI/UA using a separate validation dataset, and the net reclassification index (NRI) was subsequently determined.

RESULTS

Using both complete and reduced features, the algorithm performance achieved an AUC ranging from 0.73 to 0.89. The top-performing ML algorithm consistently surpassed the TIMI risk score for in-hospital, 30-day, and 1-year predictions (with AUC values of 0.88, 0.88, and 0.81, respectively, all p < 0.001), while the TIMI scores registered significantly lower at 0.55, 0.54, and 0.61. This suggests the TIMI score tends to underestimate patient mortality risk. The net reclassification index (NRI) of the best ML algorithm for NSTEMI/UA patients across these periods yielded an NRI between 40-60% (p < 0.001) relative to the TIMI NSTEMI/UA risk score. Key features identified for both short- and long-term mortality included age, Killip class, heart rate, and Low-Molecular-Weight Heparin (LMWH) administration.

CONCLUSIONS

In a broad multi-ethnic population, ML approaches outperformed conventional TIMI scoring in classifying patients with NSTEMI and UA. ML allows for the precise identification of unique characteristics within individual Asian populations, improving the accuracy of mortality predictions. Continuous development, testing, and validation of these ML algorithms holds the promise of enhanced risk stratification, thereby revolutionizing future management strategies and patient outcomes.

Interpretable machine learning models for predicting in-hospital and 30 days adverse events in acute coronary syndrome patients in Kuwait

The relationships between acute coronary syndromes (ACS) adverse events and the associated risk factors are typically complicated and nonlinear, which poses significant challenges to clinicians' attempts at risk stratification. Here, we aim to explore the implementation of modern risk stratification tools to untangle how these complex factors shape the risk of adverse events in patients with ACS. We used an interpretable multi-algorithm machine learning (ML) approach and clinical features to fit predictive models to 1,976 patients with ACS in Kuwait. We demonstrated that random forest (RF) and extreme gradient boosting (XGB) algorithms, remarkably outperform traditional logistic regression model (AUCs = 0.84 & 0.79 for RF and XGB, respectively). Our in-hospital adverse events model identified left ventricular ejection fraction as the most important predictor with the highest interaction strength with other factors. However, using the 30-days adverse events model, we found that performing an urgent coronary artery bypass graft was the most important predictor, with creatinine levels having the strongest overall interaction with other related factors. Our ML models not only untangled the non-linear relationships that shape the clinical epidemiology of ACS adverse events but also elucidated their risk in individual patients based on their unique features.

Electrocardiogram Interpretation Using Artificial Intelligence: Diagnosis of Cardiac and Extracardiac Pathologic Conditions. How Far Has Machine Learning Reached?

Artificial intelligence (AI) is already widely used in different fields of medicine, making possible the integration of the paraclinical exams with the clinical findings in patients, for a more accurate and rapid diagnosis and treatment decision. The electrocardiogram remains one of the most important, fastest, cheapest, and noninvasive methods of diagnosis in cardiology, despite the rapid development and progression of the technology. Even if studied a long time ago, it still has a lot of less understood features that, with a better understanding, can give more clues to a correct and prompt diagnosis in a short time. The use of AI in the interpretation of the ECG improved the accuracy and the time to diagnosis in different cardiovascular diseases, and more than this, explaining the decision to make AI diagnosis improved the human understanding of the different features of the ECG that might be considered for a more accurate diagnosis. The purpose of this article is to provide an overview of the most recently published articles about the use of AI in ECG interpretation.

The predictive value of machine learning for mortality risk in patients with acute coronary syndromes: a systematic review and meta-analysis

BACKGROUND

Acute coronary syndromes (ACS) are the leading cause of global death. Optimizing mortality risk prediction and early identification of high-risk patients is essential for developing targeted prevention strategies. Many researchers have built machine learning (ML) models to predict the mortality risk in ACS patients. Our meta-analysis aimed to evaluate the predictive value of various ML models in predicting death in ACS patients at different times.

METHODS

PubMed, Embase, Web of Science, and Cochrane Library were searched systematically from database establishment to March 12, 2022 for studies developing or validating at least one ML predictive model for death in ACS patients. We used PROBAST to assess the risk of bias in the reported predictive models and a random-effects model to assess the pooled C-index and accuracy of these models.

RESULTS

Fifty papers were included, involving 216 ML prediction models, 119 of which were externally validated. The combined C-index of the ML models in the validation cohort predicting the in-hospital mortality, 30-day mortality, 3- or 6-month mortality, and 1 year or above mortality in ACS patients were 0.8633 (95% CI 0.8467-0.8802), 0.8296 (95% CI 0.8134-0.8462), 0.8205 (95% CI 0.7881-0.8541), and 0.8197 (95% CI 0.8042-0.8354), respectively, with the corresponding combined accuracy of 0.8569 (95% CI 0.8411-0.8715), 0.8282 (95% CI 0.7922-0.8591), 0.7303 (95% CI 0.7184-0.7418), and 0.7837 (95% CI 0.7455-0.8175), indicating that the ML models were relatively excellent in predicting ACS mortality at different times. Furthermore, common predictors of death in ML models included age, sex, systolic blood pressure, serum creatinine, Killip class, heart rate, diastolic blood pressure, blood glucose, and hemoglobin.

CONCLUSIONS

The ML models had excellent predictive power for mortality in ACS, and the methodologies may need to be addressed before they can be used in clinical practice.

Complete Percutaneous Coronary Revascularization in Acute Coronary Syndromes With Multivessel Coronary Disease: A Systematic Review

Multivessel disease (MVD) affects approximately 50% of patients with acute coronary syndromes (ACS) and is significantly burdened by poor outcomes and high mortality. It represents a clinical challenge in patient management and decision making and subtends an evolving research area related to the pathophysiology of unstable plaques and local or systemic inflammation. The benefits of complete revascularization are established in hemodynamically stable ACS patients with MVD, and guidelines provide some reference points to inform clinical practice, based on an evidence level that is solid for ST-segment elevation myocardial infarction and less robust for non-ST-segment elevation myocardial infarction and cardiogenic shock. However, several areas of uncertainty remain, such as the optimal timing for complete revascularization or the best guiding strategy for intermediate stenoses. We performed a systematic review of current evidence in the field of percutaneous revascularization in ACS and MVD, also including future perspectives from ongoing trials that will directly compare different timing strategies and investigate the role of invasive and noninvasive guidance techniques. (Complete percutaneous coronary revascularization in patients with acute myocardial infarction and multivessel disease; CRD42022383123).

Use of Artificial Intelligence in the Advancement of Breast Surgery and Implications for Breast Reconstruction: A Narrative Review

BACKGROUND

Breast reconstruction is a pivotal part of the recuperation process following a mastectomy and aims to restore both the physical aesthetic and emotional well-being of breast cancer survivors. In recent years, artificial intelligence (AI) has emerged as a revolutionary technology across numerous medical disciplines. This narrative review of the current literature and evidence analysis explores the role of AI in the domain of breast reconstruction, outlining its potential to refine surgical procedures, enhance outcomes, and streamline decision making.

METHODS

A systematic search on Medline (via PubMed), Cochrane Library, Web of Science, Google Scholar, Clinical Trials, and Embase databases from January 1901 to June 2023 was conducted.

RESULTS

By meticulously evaluating a selection of recent studies and engaging with inherent challenges and prospective trajectories, this review spotlights the promising role AI plays in advancing the techniques of breast reconstruction. However, issues concerning data quality, privacy, and ethical considerations pose hurdles to the seamless integration of AI in the medical field.

CONCLUSION

The future research agenda comprises dataset standardization, AI algorithm refinement, and the implementation of prospective clinical trials and fosters cross-disciplinary partnerships. The fusion of AI with other emergent technologies like augmented reality and 3D printing could further propel progress in breast surgery.

A bibliometric analysis of acute myocardial infarction in women from 2000 to 2022

Background

Plenty of publications had been written in the last several decades on acute myocardial infarction (AMI) in women. However, there are few bibliometric analyses in such field. In order to solve this problem, we attempted to examine the knowledge structure and development of research about AMI in women based on analysis of related publications.

Method

The Web of Science Core Collection was used to extract all publications regarding AMI in women, ranging from January 2000 to August 2022. Bibliometric analysis was performed using VOSviewer, Cite Space, and an online bibliometric analysis platform.

Results

A total of 14,853 publications related to AMI in women were identified from 2000 to 2022. Over the past 20 years, the United States had published the most articles in international research and participated in international cooperation the most frequently. The primary research institutions were Harvard University and University of Toronto. Circulation was the most cited journal and had an incontrovertible academic impact. 67,848 authors were identified, among which Harlan M Krumholz had the most significant number of articles and Thygesen K was co-cited most often. And the most common keywords included risk factors, disease, prognosis, mortality, criteria and algorithm.

Conclusion

The research hotspots and trends of AMI in women were identified and explored using bibliometric and visual methods. Researches about AMI in women are flourishing. Criteria and algorithms might be the focus of research in the near future, which deserved great attentions.

Machine learning for atrial fibrillation risk prediction in patients with sleep apnea and coronary artery disease

Background

Patients with sleep apnea (SA) and coronary artery disease (CAD) are at higher risk of atrial fibrillation (AF) than the general population. Our objectives were: to evaluate the role of CAD and SA in determining AF risk through cluster and survival analysis, and to develop a risk model for predicting AF.

Methods

Electronic medical record (EMR) database from 22,302 individuals including 10,202 individuals with AF, CAD, and SA, and 12,100 individuals without these diseases were analyzed using K-means clustering technique; k-nearest neighbor (kNN) algorithm and survival analysis. Age, sex, and diseases developed for each individual during 9 years were used for cluster and survival analysis.

Results

The risk models for AF, CAD, and SA were identified with high accuracy and sensitivity (0.98). Cluster analysis showed that CAD and high blood pressure (HBP) are the most prevalent diseases in the AF group, HBP is the most prevalent disease in CAD; and HBP and CAD are the most prevalent diseases in the SA group. Survival analysis demonstrated that individuals with HBP, CAD, and SA had a 1.5-fold increased risk of developing AF [hazard ratio (HR): 1.49, 95% CI: 1.18-1.87, p = 0.0041; HR: 1.46, 95% CI: 1.09-1.96, p = 0.01; HR: 1.54, 95% CI: 1.22-1.94, p = 0.0039, respectively] and individuals with chronic kidney disease (CKD) developed AF approximately 50% earlier than patients without these comorbidities in a period of 7 years (HR: 3.36, 95% CI: 1.46-7.73, p = 0.0023). Comorbidities that contributed to develop AF earlier in females compared to males in the group of 50-64 years were HBP (HR: 3.75 95% CI: 1.08-13, p = 0.04) CAD and SA in the group of 60-75 years were (HR: 2.4 95% CI: 1.18-4.86, p = 0.02; HR: 2.51, 95% CI: 1.14-5.52, p = 0.02, respectively).

Conclusion

Machine learning based algorithms demonstrated that CAD, SA, HBP, and CKD are significant risk factors for developing AF in a Latin-American population.

Risk Stratification of COVID-19 Using Routine Laboratory Tests: A Machine Learning Approach

The COVID-19 pandemic placed significant stress on an already overburdened health system. The diagnosis was based on detection of a positive RT-PCR test, which may be delayed when there is peak demand for testing. Rapid risk stratification of high-risk patients allows for the prioritization of resources for patient care. The study aims were to classify patients as severe or not severe based on outcomes using machine learning on routine laboratory tests. Data were extracted for all individuals who had at least one SARS-CoV-2 PCR test conducted via the NHLS between the periods of 1 March 2020 to 7 July 2020. Exclusion criteria: those 18 years, and those with indeterminate PCR tests. Results for 15437 patients (3301 positive and 12,136 negative) were used to fit six machine learning models, namely the logistic regression (LR) (the base model), decision trees (DT), random forest (RF), extreme gradient boosting (XGB), convolutional neural network (CNN) and self-normalising neural network (SNN). Model development was carried out by splitting the data into training and testing set of a ratio 70:30, together with a 10-fold cross-validation re-sampling technique. For risk stratification, admission to high care or ICU was the outcome for severe disease. Performance of the models varied: sensitivity was best for RF at 75% and accuracy of 75% for CNN. The area under the curve ranged from 57% for CNN to 75% for RF. RF and SNN were the best-performing models. Machine Learning (ML) can be incorporated into the laboratory information system and offers promise for early identification and risk stratification of COVID-19 patients, particularly in areas of resource-poor settings.

Robust prognostic prediction model developed with integrated biological markers for acute myocardial infarction

Commonly used prediction methods for acute myocardial infarction (AMI) were created before contemporary percutaneous coronary intervention was recognized as the primary therapy. Although several studies have used machine learning techniques for prognostic prediction of patients with AMI, its clinical application has not been achieved. Here, we developed an online application tool using a machine learning model to predict in-hospital mortality in patients with AMI. A total of 2,553 cases of ST-elevation AMI were assigned to 80% training subset for cross validation and 20% test subset for model performance evaluation. We implemented random forest classifier for the binary classification of in-hospital mortality. The selected best feature set consisted of ten clinical and biological markers including max creatine phosphokinase, hemoglobin, heart rate, creatinine, systolic blood pressure, blood sugar, age, Killip class, white blood cells, and c-reactive protein. Our model achieved high performance: the area under the curve of the receiver operating characteristic curve for the test subset, 0.95: sensitivity, 0.89: specificity, 0.91: precision, 0.43: accuracy, 0.91 respectively, which outperformed common scoring methods. The freely available application tool for prognostic prediction can contribute to risk triage and decision-making in patient-centered modern clinical practice for AMI.

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review

Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks.

Anemia or other comorbidities? using machine learning to reveal deeper insights into the drivers of acute coronary syndromes in hospital admitted patients

Acute coronary syndromes (ACS) are a leading cause of deaths worldwide, yet the diagnosis and treatment of this group of diseases represent a significant challenge for clinicians. The epidemiology of ACS is extremely complex and the relationship between ACS and patient risk factors is typically non-linear and highly variable across patient lifespan. Here, we aim to uncover deeper insights into the factors that shape ACS outcomes in hospitals across four Arabian Gulf countries. Further, because anemia is one of the most observed comorbidities, we explored its role in the prognosis of most prevalent ACS in-hospital outcomes (mortality, heart failure, and bleeding) in the region. We used a robust multi-algorithm interpretable machine learning (ML) pipeline, and 20 relevant risk factors to fit predictive models to 4,044 patients presenting with ACS between 2012 and 2013. We found that in-hospital heart failure followed by anemia was the most important predictor of mortality. However, anemia was the first most important predictor for both in-hospital heart failure, and bleeding. For all in-hospital outcome, anemia had remarkably non-linear relationships with both ACS outcomes and patients' baseline characteristics. With minimal statistical assumptions, our ML models had reasonable predictive performance (AUCs > 0.75) and substantially outperformed commonly used statistical and risk stratification methods. Moreover, our pipeline was able to elucidate ACS risk of individual patients based on their unique risk factors. Fully interpretable ML approaches are rarely used in clinical settings, particularly in the Middle East, but have the potential to improve clinicians' prognostic efforts and guide policymakers in reducing the health and economic burdens of ACS worldwide.

Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble

Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence (AI) approaches for health and biomedical research. In interventional cardiology, the hope is for AI to provide automated analysis and deeper interpretation of data from electrocardiography, computed tomography, magnetic resonance imaging, and electronic health records, among others. Furthermore, high-performance predictive models supporting decision-making hold the potential to improve safety, diagnostic and prognostic prediction in patients undergoing interventional cardiology procedures. These applications include robotic-assisted percutaneous coronary intervention procedures and automatic assessment of coronary stenosis during diagnostic coronary angiograms. Machine learning (ML) has been used in these innovations that have improved the field of interventional cardiology, and more recently, deep Learning (DL) has emerged as one of the most successful branches of ML in many applications. It remains to be seen if DL approaches will have a major impact on current and future practice. DL-based predictive systems also have several limitations, including lack of interpretability and lack of generalizability due to cohort heterogeneity and low sample sizes. There are also challenges for the clinical implementation of these systems, such as ethical limits and data privacy. This review is intended to bring the attention of health practitioners and interventional cardiologists to the broad and helpful applications of ML and DL algorithms to date in the field. Their implementation challenges in daily practice and future applications in the field of interventional cardiology are also discussed.

Artificial intelligence and cardiology: Current status and perspective: Artificial Intelligence and Cardiology

The development of artificial intelligence (AI) began in the mid-20th century but has been rapidly accelerating in the past decade. Reflecting the development of digital health over the past few years, this trend is also seen in medicine. The field of cardiovascular medicine uses a wide variety and a large amount of biosignals, so there are many situations where AI can contribute. The development of AI is in progress for all aspects of the healthcare system, including the prevention, screening, and treatment of diseases and the prediction of the prognosis. AI is expected to be used to provide specialist-level medical care, even in a situation where medical resources are scarce. However, like other medical devices, the concept and mechanism of AI must be fully understood when used; otherwise, it may be used inappropriately, resulting in detriment to the patient. Therefore, it is important to understand what we need to know as a cardiologist handling AI. This review introduces the basics and principles of AI, then shows how far the current development of AI has come, and finally gives a brief introduction of how to start the AI development for those who want to develop their own AI.

Accessing Artificial Intelligence for Clinical Decision-Making

Advancements in computing and data from the near universal acceptance and implementation of electronic health records has been formative for the growth of personalized, automated, and immediate patient care models that were not previously possible. Artificial intelligence (AI) and its subfields of machine learning, reinforcement learning, and deep learning are well-suited to deal with such data. The authors in this paper review current applications of AI in clinical medicine and discuss the most likely future contributions that AI will provide to the healthcare industry. For instance, in response to the need to risk stratify patients, appropriately cultivated and curated data can assist decision-makers in stratifying preoperative patients into risk categories, as well as categorizing the severity of ailments and health for non-operative patients admitted to hospitals. Previous overt, traditional vital signs and laboratory values that are used to signal alarms for an acutely decompensating patient may be replaced by continuously monitoring and updating AI tools that can pick up early imperceptible patterns predicting subtle health deterioration. Furthermore, AI may help overcome challenges with multiple outcome optimization limitations or sequential decision-making protocols that limit individualized patient care. Despite these tremendously helpful advancements, the data sets that AI models train on and develop have the potential for misapplication and thereby create concerns for application bias. Subsequently, the mechanisms governing this disruptive innovation must be understood by clinical decision-makers to prevent unnecessary harm. This need will force physicians to change their educational infrastructure to facilitate understanding AI platforms, modeling, and limitations to best acclimate practice in the age of AI. By performing a thorough narrative review, this paper examines these specific AI applications, limitations, and requisites while reviewing a few examples of major data sets that are being cultivated and curated in the US.

An Artificial Neural Network-Based Pediatric Mortality Risk Score: Development and Performance Evaluation Using Data From a Large North American Registry

Background

In the pediatric intensive care unit (PICU), quantifying illness severity can be guided by risk models to enable timely identification and appropriate intervention. Logistic regression models, including the pediatric index of mortality 2 (PIM-2) and pediatric risk of mortality III (PRISM-III), produce a mortality risk score using data that are routinely available at PICU admission. Artificial neural networks (ANNs) outperform regression models in some medical fields.

Objective

In light of this potential, we aim to examine ANN performance, compared to that of logistic regression, for mortality risk estimation in the PICU.

Methods

The analyzed data set included patients from North American PICUs whose discharge diagnostic codes indicated evidence of infection and included the data used for the PIM-2 and PRISM-III calculations and their corresponding scores. We stratified the data set into training and test sets, with approximately equal mortality rates, in an effort to replicate real-world data. Data preprocessing included imputing missing data through simple substitution and normalizing data into binary variables using PRISM-III thresholds. A 2-layer ANN model was built to predict pediatric mortality, along with a simple logistic regression model for comparison. Both models used the same features required by PIM-2 and PRISM-III. Alternative ANN models using single-layer or unnormalized data were also evaluated. Model performance was compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC) and their empirical 95% CIs.

Results

Data from 102,945 patients (including 4068 deaths) were included in the analysis. The highest performing ANN (AUROC 0.871, 95% CI 0.862-0.880; AUPRC 0.372, 95% CI 0.345-0.396) that used normalized data performed better than PIM-2 (AUROC 0.805, 95% CI 0.801-0.816; AUPRC 0.234, 95% CI 0.213-0.255) and PRISM-III (AUROC 0.844, 95% CI 0.841-0.855; AUPRC 0.348, 95% CI 0.322-0.367). The performance of this ANN was also significantly better than that of the logistic regression model (AUROC 0.862, 95% CI 0.852-0.872; AUPRC 0.329, 95% CI 0.304-0.351). The performance of the ANN that used unnormalized data (AUROC 0.865, 95% CI 0.856-0.874) was slightly inferior to our highest performing ANN; the single-layer ANN architecture performed poorly and was not investigated further.

Conclusions

A simple ANN model performed slightly better than the benchmark PIM-2 and PRISM-III scores and a traditional logistic regression model trained on the same data set. The small performance gains achieved by this two-layer ANN model may not offer clinically significant improvement; however, further research with other or more sophisticated model designs and better imputation of missing data may be warranted.

A review of risk prediction models in cardiovascular disease: conventional approach vs. artificial intelligent approach

Cardiovascular disease (CVD) is the leading cause of death worldwide and is a global health issue. Traditionally, statistical models are used commonly in the risk prediction and assessment of CVD. However, the adoption of artificial intelligent (AI) approach is rapidly taking hold in the current era of technology to evaluate patient risks and predict the outcome of CVD. In this review, we outline various conventional risk scores and prediction models and do a comparison with the AI approach. The strengths and limitations of both conventional and AI approaches are discussed. Besides that, biomarker discovery related to CVD are also elucidated as the biomarkers can be used in the risk stratification as well as early detection of the disease. Moreover, problems and challenges involved in current CVD studies are explored. Lastly, future prospects of CVD risk prediction and assessment in the multi-modality of big data integrative approaches are proposed.

Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction

Importance

Accurate prediction of adverse outcomes after acute myocardial infarction (AMI) can guide the triage of care services and shared decision-making, and novel methods hold promise for using existing data to generate additional insights.

Objective

To evaluate whether contemporary machine learning methods can facilitate risk prediction by including a larger number of variables and identifying complex relationships between predictors and outcomes.

Design, Setting, and Participants

This cohort study used the American College of Cardiology Chest Pain-MI Registry to identify all AMI hospitalizations between January 1, 2011, and December 31, 2016. Data analysis was performed from February 1, 2018, to October 22, 2020.

Main Outcomes and Measures

Three machine learning models were developed and validated to predict in-hospital mortality based on patient comorbidities, medical history, presentation characteristics, and initial laboratory values. Models were developed based on extreme gradient descent boosting (XGBoost, an interpretable model), a neural network, and a meta-classifier model. Their accuracy was compared against the current standard developed using a logistic regression model in a validation sample.

Results

A total of 755 402 patients (mean [SD] age, 65 [13] years; 495 202 [65.5%] male) were identified during the study period. In independent validation, 2 machine learning models, gradient descent boosting and meta-classifier (combination including inputs from gradient descent boosting and a neural network), marginally improved discrimination compared with logistic regression (C statistic, 0.90 for best performing machine learning model vs 0.89 for logistic regression). Nearly perfect calibration in independent validation data was found in the XGBoost (slope of predicted to observed events, 1.01; 95% CI, 0.99-1.04) and the meta-classifier model (slope of predicted-to-observed events, 1.01; 95% CI, 0.99-1.02), with more precise classification across the risk spectrum. The XGBoost model reclassified 32 393 of 121 839 individuals (27%) and the meta-classifier model reclassified 30 836 of 121 839 individuals (25%) deemed at moderate to high risk for death in logistic regression as low risk, which were more consistent with the observed event rates.

Conclusions and Relevance

In this cohort study using a large national registry, none of the tested machine learning models were associated with substantive improvement in the discrimination of in-hospital mortality after AMI, limiting their clinical utility. However, compared with logistic regression, XGBoost and meta-classifier models, but not the neural network, offered improved resolution of risk for high-risk individuals.

A new approach for interpretability and reliability in clinical risk prediction: Acute coronary syndrome scenario

INTRODUCTION

The risk prediction of the occurrence of a clinical event is often based on conventional statistical procedures, through the implementation of risk score models. Recently, approaches based on more complex machine learning (ML) methods have been developed. Despite the latter usually have a better predictive performance, they obtain little approval from the physicians, as they lack interpretability and, therefore, clinical confidence. One clinical issue where both types of models have received great attention is the mortality risk prediction after acute coronary syndromes (ACS).

OBJECTIVE

We intend to create a new risk assessment methodology that combines the best characteristics of both risk score and ML models. More specifically, we aim to develop a method that, besides having a good performance, offers a personalized model and outcome for each patient, presents high interpretability, and incorporates an estimation of the prediction reliability which is not usually available. By combining these features in the same approach we expect that it can boost the confidence of physicians to use such a tool in their daily activity.

METHODS

In order to achieve the mentioned goals, a three-step methodology was developed: several rules were created by dichotomizing risk factors; such rules were trained with a machine learning classifier to predict the acceptance degree of each rule (the probability that the rule is correct) for each patient; that information was combined and used to compute the risk of mortality and the reliability of such prediction. The methodology was applied to a dataset of 1111 patients admitted with any type of ACS (myocardial infarction and unstable angina) in two Portuguese hospitals, to assess the 30-days all-cause mortality risk, being validated through a Monte-Carlo cross-validation technique. The performance was compared with state-of-the-art approaches: logistic regression (LR), artificial neural network (ANN), and clinical risk score model (namely the Global Registry of Acute Coronary Events - GRACE).

RESULTS

For the scenario being analyzed, the performance of the proposed approach and the comparison models was assessed through discrimination and calibration. The ability to rank the patients was evaluated through the area under the ROC curve (AUC), and the ability to stratify the patients into low or high-risk groups was determined using the geometric mean (GM) of specificity and sensitivity, the negative predictive value (NPV) and the positive predictive value (PPV). The validation calibration curves were also inspected. The proposed approach (AUC = 81%, GM = 74%, PPV = 17%, NPV = 99%) achieved testing results identical to the standard LR model (AUC = 83%, GM = 73%, PPV = 16%, NPV=99%), but offers superior interpretability and personalization; it also significantly outperforms the GRACE risk model (AUC = 79%, GM = 47%, PPV = 13%, NPV = 98%) and the standard ANN model (AUC = 78%, GM = 70%, PPV = 13%, NPV = 98%). The calibration curve also suggests a very good generalization ability of the obtained model as it approaches the ideal curve (slope = 0.96). Finally, the reliability estimation of individual predictions presented a great correlation with the misclassifications rate.

CONCLUSION

We developed and described a new tool that showed great potential to guide the clinical staff in the risk assessment and decision-making process, and to obtain their wide acceptance due to its interpretability and reliability estimation properties. The methodology presented a good performance when applied to ACS events, but those properties may have a beneficial application in other clinical scenarios as well.

Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility

BACKGROUND

Machine learning (ML) is increasingly used in research for subtype definition and risk prediction, particularly in cardiovascular diseases. No existing ML models are routinely used for cardiovascular disease management, and their phase of clinical utility is unknown, partly due to a lack of clear criteria. We evaluated ML for subtype definition and risk prediction in heart failure (HF), acute coronary syndromes (ACS) and atrial fibrillation (AF).

METHODS

For ML studies of subtype definition and risk prediction, we conducted a systematic review in HF, ACS and AF, using PubMed, MEDLINE and Web of Science from January 2000 until December 2019. By adapting published criteria for diagnostic and prognostic studies, we developed a seven-domain, ML-specific checklist.

RESULTS

Of 5918 studies identified, 97 were included. Across studies for subtype definition (n = 40) and risk prediction (n = 57), there was variation in data source, population size (median 606 and median 6769), clinical setting (outpatient, inpatient, different departments), number of covariates (median 19 and median 48) and ML methods. All studies were single disease, most were North American (n = 61/97) and only 14 studies combined definition and risk prediction. Subtype definition and risk prediction studies respectively had limitations in development (e.g. 15.0% and 78.9% of studies related to patient benefit; 15.0% and 15.8% had low patient selection bias), validation (12.5% and 5.3% externally validated) and impact (32.5% and 91.2% improved outcome prediction; no effectiveness or cost-effectiveness evaluations).

CONCLUSIONS

Studies of ML in HF, ACS and AF are limited by number and type of included covariates, ML methods, population size, country, clinical setting and focus on single diseases, not overlap or multimorbidity. Clinical utility and implementation rely on improvements in development, validation and impact, facilitated by simple checklists. We provide clear steps prior to safe implementation of machine learning in clinical practice for cardiovascular diseases and other disease areas.

Machine Learning Compared With Conventional Statistical Models for Predicting Myocardial Infarction Readmission and Mortality: A Systematic Review

BACKGROUND

Machine learning (ML) methods are increasingly used in addition to conventional statistical modelling (CSM) for predicting readmission and mortality in patients with myocardial infarction (MI). However, the two approaches have not been systematically compared across studies of prognosis in patients with MI.

METHODS

Following PRISMA guidelines, we systematically reviewed the literature via Medline, EPub, Cochrane Central, Embase, Inspec, ACM Digital Library, and Web of Science. Eligible studies included primary research articles published from January 2000 to March 2020, comparing ML and CSM for prognostication after MI.

RESULTS

Of 7,348 articles, 112 underwent full-text review, with the final set composed of 24 articles representing 374,365 patients. ML methods included artificial neural networks (n = 12 studies), random forests (n = 11), decision trees (n = 8), support vector machines (n = 8), and Bayesian techniques (n = 7). CSM included logistic regression (n = 19 studies), existing CSM-derived risk scores (n = 12), and Cox regression (n = 2). Thirteen of 19 studies examining mortality reported higher C-indexes with the use of ML compared with CSM. One study examined readmissions at 2 different time points, with C-indexes that were higher for ML than CSM. Across all studies, a total of 29 comparisons were performed, but the majority (n = 26, 90%) found small (< 0.05) absolute differences in the C-index between ML and CSM. With the use of a modified CHARMS checklist, sources of bias were identifiable in the majority of studies, and only 2 were externally validated.

CONCLUSION

Although ML algorithms tended to have higher C-indexes than CSM for predicting death or readmission after MI, these studies exhibited threats to internal validity and were often unvalidated. Further comparisons are needed, with adherence to clinical quality standards for prognosis research. (Trial registration: PROSPERO CRD42019134896).

Treatment of individual predictors with neural network algorithms improves Global Registry of Acute Coronary Events score discrimination

Objective:

The aim of this study was to develop, train, and test different neural network (NN) algorithm-based models to improve the Global Registry of Acute Coronary Events (GRACE) score performance to predict in-hospital mortality after an acute coronary syndrome.

Methods:

We analyzed a prospective database, including 40 admission variables of 1255 patients admitted with the acute coronary syndrome in a community hospital. Individual predictors included in GRACE score were used to train and test three NN algorithm-based models (guided models), namely: one- and two-hidden layer multilayer perceptron and a radial basis function network. Three extra NNs were built using the 40 admission variables of the entire database (unguided models). Expected mortality according to GRACE score was calculated using the logistic regression equation.

Results:

In terms of receiver operating characteristic area and negative predictive value (NPV), almost all NN algorithms outperformed logistic regression. Only radial basis function models obtained a better accuracy level based on NPV improvement, at the expense of positive predictive value (PPV) reduction. The independent normalized importance of variables for the best unguided NN was: creatinine 100%, Killip class 61%, ejection fraction 52%, age 44%, maximum creatine-kinase level 41%, glycemia 40%, left bundle branch block 35%, and weight 33%, among the top 8 predictors.

Conclusions:

Treatment of individual predictors of GRACE score with NN algorithms improved accuracy and discrimination power in all models with respect to the traditional logistic regression approach; nevertheless, PPV was only marginally enhanced. Unguided variable selection would be able to achieve better results in PPV terms.

In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department

Background Classical ST-T waveform changes on standard 12-lead ECG have limited sensitivity in detecting acute coronary syndrome (ACS) in the emergency department. Numerous novel ECG features have been previously proposed to augment clinicians' decision during patient evaluation, yet their clinical utility remains unclear. Methods and Results This was an observational study of consecutive patients evaluated for suspected ACS (Cohort 1 n=745, age 59±17, 42% female, 15% ACS; Cohort 2 n=499, age 59±16, 49% female, 18% ACS). Out of 554 temporal-spatial ECG waveform features, we used domain knowledge to select a subset of 65 physiology-driven features that are mechanistically linked to myocardial ischemia and compared their performance to a subset of 229 data-driven features selected by multiple machine learning algorithms. We then used random forest to select a final subset of 73 most important ECG features that had both data- and physiology-driven basis to ACS prediction and compared their performance to clinical experts. On testing set, a regularized logistic regression classifier based on the 73 hybrid features yielded a stable model that outperformed clinical experts in predicting ACS, with 10% to 29% of cases reclassified correctly. Metrics of nondipolar electrical dispersion (ie, circumferential ischemia), ventricular activation time (ie, transmural conduction delays), QRS and T axes and angles (ie, global remodeling), and principal component analysis ratio of ECG waveforms (ie, regional heterogeneity) played an important role in the improved reclassification performance. Conclusions We identified a subset of novel ECG features predictive of ACS with a fully interpretable model highly adaptable to clinical decision support applications. Registration URL: https://www.clinicaltrials.gov; Unique Identifier: NCT04237688.

Value of a Machine Learning Approach for Predicting Clinical Outcomes in Young Patients With Hypertension

Risk stratification of young patients with hypertension remains challenging. Generally, machine learning (ML) is considered a promising alternative to traditional methods for clinical predictions because it is capable of processing large amounts of complex data. We, therefore, explored the feasibility of an ML approach for predicting outcomes in young patients with hypertension and compared its performance with that of approaches now commonly used in clinical practice. Baseline clinical data and a composite end point—comprising all-cause death, acute myocardial infarction, coronary artery revascularization, new-onset heart failure, new-onset atrial fibrillation/atrial flutter, sustained ventricular tachycardia/ventricular fibrillation, peripheral artery revascularization, new-onset stroke, end-stage renal disease—were evaluated in 508 young patients with hypertension (30.83±6.17 years) who had been treated at a tertiary hospital. Construction of the ML model, which consisted of recursive feature elimination, extreme gradient boosting, and 10-fold cross-validation, was performed at the 33-month follow-up evaluation, and the model’s performance was compared with that of the Cox regression and recalibrated Framingham Risk Score models. An 11-variable combination was considered most valuable for predicting outcomes using the ML approach. The C statistic for identifying patients with composite end points was 0.757 (95% CI, 0.660–0.854) for the ML model, whereas for Cox regression model and the recalibrated Framingham Risk Score model it was 0.723 (95% CI, 0.636–0.810) and 0.529 (95% CI, 0.403–0.655). The ML approach was comparable with Cox regression for determining the clinical prognosis of young patients with hypertension and was better than that of the recalibrated Framingham Risk Score model.

Artificial Intelligence in Cardiology: Present and Future

Artificial intelligence (AI) is a nontechnical, popular term that refers to machine learning of various types but most often to deep neural networks. Cardiology is at the forefront of AI in medicine. For this review, we searched PubMed and MEDLINE databases with no date restriction using search terms related to AI and cardiology. Articles were selected for inclusion on the basis of relevance. We highlight the major achievements in recent years in nearly all areas of cardiology and underscore the mounting evidence suggesting how AI will take center stage in the field. Artificial intelligence requires a close collaboration among computer scientists, clinical investigators, clinicians, and other users in order to identify the most relevant problems to be solved. Best practices in the generation and implementation of AI include the selection of ideal data sources, taking into account common challenges during the interpretation, validation, and generalizability of findings, and addressing safety and ethical concerns before final implementation. The future of AI in cardiology and in medicine in general is bright as the collaboration between investigators and clinicians continues to excel.

Predicting severe clinical events by learning about life-saving actions and outcomes using distant supervision

Medical error is a leading cause of patient death in the United States. Among the different types of medical errors, harm to patients caused by doctors missing early signs of deterioration is especially challenging to address due to the heterogeneity of patients' physiological patterns. In this study, we implemented risk prediction models using the gradient boosted tree method to derive risk estimates for acute onset diseases in the near future. The prediction model uses physiological variables as input signals and the time of the administration of outcome-related interventions and discharge diagnoses as labels. We examine four categories of acute onset illness: acute heart failure (AHF), acute lung injury (ALI), acute kidney injury (AKI), and acute liver failure (ALF). To develop and test the model, we consider data from two sources: 23,578 admissions to the Intensive Care Unit (ICU) from the MIMIC-3 dataset (Beth-Israel Hospital) and 16,612 ICU admissions on hospitals affiliated with our institution (University of Washington Medical Center and Harborview Medical Center, the UW-CDR dataset). We systematically identify outcome-related interventions for each acute organ failure, then use them, along with discharge diagnoses, to label proxy events to train gradient boosted trees. The trained models achieve the highest F1 score with a value of 0.6018 when predicting the need for life-saving interventions for ALI within the next 24 h in the MIMIC-3 dataset while showing a median F1 score of 0.3850 from all acute organ failures in both datasets. The approach also achieves the highest F1 score of 0.6301 when classifying a patient's ALI status at the time of discharge from the MIMIC-3 dataset, with a median F1 score of 0.4307 in both datasets. This study shows the potential for using the time of outcome-related intervention administrations and discharge diagnoses as labels to train supervised machine learning models that predict the risk of acute onset illnesses.

A machine learning approach to predict early outcomes after pituitary adenoma surgery

OBJECTIVEPituitary adenomas occur in a heterogeneous patient population with diverse perioperative risk factors, endocrinopathies, and other tumor-related comorbidities. This heterogeneity makes predicting postoperative outcomes challenging when using traditional scoring systems. Modern machine learning algorithms can automatically identify the most predictive risk factors and learn complex risk-factor interactions using training data to build a robust predictive model that can generalize to new patient cohorts. The authors sought to build a predictive model using supervised machine learning to accurately predict early outcomes of pituitary adenoma surgery.METHODSA retrospective cohort of 400 consecutive pituitary adenoma patients was used. Patient variables/predictive features were limited to common patient characteristics to improve model implementation. Univariate and multivariate odds ratio analysis was performed to identify individual risk factors for common postoperative complications and to compare risk factors with model predictors. The study population was split into 300 training/validation patients and 100 testing patients to train and evaluate four machine learning models using binary classification accuracy for predicting early outcomes.RESULTSThe study included a total of 400 patients. The mean ± SD patient age was 53.9 ± 16.3 years, 59.8% of patients had nonfunctioning adenomas and 84.7% had macroadenomas, and the mean body mass index (BMI) was 32.6 ± 7.8 (58.0% obesity rate). Multivariate odds ratio analysis demonstrated that age < 40 years was associated with a 2.86 greater odds of postoperative diabetes insipidus and that nonobese patients (BMI < 30) were 2.2 times more likely to develop postoperative hyponatremia. Using broad criteria for a poor early postoperative outcome-major medical and early surgical complications, extended length of stay, emergency department admission, inpatient readmission, and death-31.0% of patients met criteria for a poor early outcome. After model training, a logistic regression model with elastic net (LR-EN) regularization best predicted early postoperative outcomes of pituitary adenoma surgery on the 100-patient testing set-sensitivity 68.0%, specificity 93.3%, overall accuracy 87.0%. The receiver operating characteristic and precision-recall curves for the LR-EN model had areas under the curve of 82.7 and 69.5, respectively. The most important predictive variables were lowest perioperative sodium, age, BMI, highest perioperative sodium, and Cushing's disease.CONCLUSIONSEarly postoperative outcomes of pituitary adenoma surgery can be predicted with 87% accuracy using a machine learning approach. These results provide insight into how predictive modeling using machine learning can be used to improve the perioperative management of pituitary adenoma patients.

Machine learning for comprehensive forecasting of Alzheimer's Disease progression

Most approaches to machine learning from electronic health data can only predict a single endpoint. The ability to simultaneously simulate dozens of patient characteristics is a crucial step towards personalized medicine for Alzheimer's Disease. Here, we use an unsupervised machine learning model called a Conditional Restricted Boltzmann Machine (CRBM) to simulate detailed patient trajectories. We use data comprising 18-month trajectories of 44 clinical variables from 1909 patients with Mild Cognitive Impairment or Alzheimer's Disease to train a model for personalized forecasting of disease progression. We simulate synthetic patient data including the evolution of each sub-component of cognitive exams, laboratory tests, and their associations with baseline clinical characteristics. Synthetic patient data generated by the CRBM accurately reflect the means, standard deviations, and correlations of each variable over time to the extent that synthetic data cannot be distinguished from actual data by a logistic regression. Moreover, our unsupervised model predicts changes in total ADAS-Cog scores with the same accuracy as specifically trained supervised models, additionally capturing the correlation structure in the components of ADAS-Cog, and identifies sub-components associated with word recall as predictive of progression.

A supervised machine learning approach to characterize spinal network function

Spontaneous activity is a common feature of immature neuronal networks throughout the central nervous system and plays an important role in network development and consolidation. In postnatal rodents, spontaneous activity in the spinal cord exhibits complex, stochastic patterns that have historically proven challenging to characterize. We developed a software tool for quickly and automatically characterizing and classifying episodes of spontaneous activity generated from developing spinal networks. We recorded spontaneous activity from in vitro lumbar ventral roots of 16 neonatal [postnatal day (P)0-P3] mice. Recordings were DC coupled and detrended, and episodes were separated for analysis. Amplitude-, duration-, and frequency-related features were extracted from each episode and organized into five classes. Paired classes and features were used to train and test supervised machine learning algorithms. Multilayer perceptrons were used to classify episodes as rhythmic or multiburst. We increased network excitability with potassium chloride and tested the utility of the tool to detect changes in features and episode class. We also demonstrate usability by having a novel experimenter use the program to classify episodes collected at a later time point (P5). Supervised machine learning-based classification of episodes accounted for changes that traditional approaches cannot detect. Our tool, named SpontaneousClassification, advances the detail in which we can study not only developing spinal networks, but also spontaneous networks in other areas of the nervous system. NEW & NOTEWORTHY Spontaneous activity is important for nervous system network development and consolidation. Our software uses machine learning to automatically and quickly characterize and classify episodes of spontaneous activity in the spinal cord of newborn mice. It detected changes in network activity following KCl-enhanced excitation. Using our software to classify spontaneous activity throughout development, in pathological models, or with neuromodulation, may offer insight into the development and organization of spinal circuits.

Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients

Background

Patient with acute coronary syndrome benefits from early revascularization. However, methods for the selection of patients who require urgent revascularization from a variety of patients visiting the emergency room with chest symptoms is not fully established. Electrocardiogram is an easy and rapid procedure, but may contain crucial information not recognized even by well-trained physicians.

Objective

To make a prediction model for the needs for urgent revascularization from 12-lead electrocardiogram recorded in the emergency room.

Method

We developed an artificial intelligence model enabling the detection of hidden information from a 12-lead electrocardiogram recorded in the emergency room. Electrocardiograms obtained from consecutive patients visiting the emergency room at Keio University Hospital from January 2012 to April 2018 with chest discomfort was collected. These data were splitted into validation and derivation dataset with no duplication in each dataset. The artificial intelligence model was constructed to select patients who require urgent revascularization within 48 hours. The model was trained with the derivation dataset and tested using the validation dataset.

Results

Of the consecutive 39,619 patients visiting the emergency room with chest discomfort, 362 underwent urgent revascularization. Of them, 249 were included in the derivation dataset and the remaining 113 were included in validation dataset. For the control, 300 were randomly selected as derivation dataset and another 130 patients were randomly selected for validation dataset from the 39,317 who did not undergo urgent revascularization. On validation, our artificial intelligence model had predictive value of the c-statistics 0.88 (95% CI 0.84–0.93) for detecting patients who required urgent revascularization.

Conclusions

Our artificial intelligence model provides information to select patients who need urgent revascularization from only 12-leads electrocardiogram in those visiting the emergency room with chest discomfort.