scholarly journals Investigating performance of deep learning and machine learning risk stratification of Asian in-hospital patients after ST-elevation myocardial infarction

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
S Kasim ◽  
S Malek ◽  
K S Ibrahim ◽  
P N F Amir ◽  
M F Aziz
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Feature Selection ◽  
Deep Learning ◽  
Asian Population ◽  
Mortality Prediction ◽  
Expert Recommendation ◽  
Timi Score ◽  
St Elevation ◽  
Validation Set

Abstract Background Machine learning (ML) algorithm support vector machine (SVM) performed better than Thrombolysis in Myocardial Infarction (TIMI) score for ASIAN STEMI patients. However, Deep Learning (DL) effectiveness in the multiethnic ASIAN population has yet to be determined. DL has automatic learning of the feature from a given dataset without the need to conduct feature selection. However, the selected features by the algorithm is black box. Identifying features associated with mortality is essential to recognize characteristics of patients with high risk for better patient management. Purpose To develop a DL algorithm for in-hospital mortality in multiethnic STEMI patients using predictors identified from the SVM algorithm. To investigate DL performance constructed using predictors from SVM feature extraction and expert-recommended predictors. Methods We constructed four algorithms; a) DL and SVM algorithms with predictors identified from the SVM variable importance b) DL and SVM using predictors based on expert recommendation. We used registry data from the National Cardiovascular Disease Database of 11397 patient's. Fifty parameters including demographics, cardiovascular risk, medications and clinical variables were considered. The Area under the curve (AUC) is the performance evaluation metric. Algorithms were validated against the TIMI and tested using the same validation data. SVM variable importance with backward elimination was used to select and rank important variables. Results DL algorithms outperform SVM and TIMI on the validation dataset; i) DL with SVM selected predictors (15 predictors, AUC = 0.97), ii) DL with expert-recommended predictors (16 predictors, AUC = 0.96), iii) SVM with selected predictors (15 predictors, AUC = 0.92), iv) SVM with expert-recommended predictors (AUC = 0.89) and TIMI (AUC = 0.82). Common predictors across SVM feature selection, expert-recommendation and TIMI are: age, heart rate, Killip class, fasting blood glucose, systolic blood pressure, comorbid diseases and ST-elevation. SVM feature selection also identified diuretics, PCI and pharmacotherapy drugs as predictors that improve mortality prediction in STEMI patients. Our findings suggest that the TIMI score underestimates patients risk of mortality. DL algorithm using selected predictors classified 35% of nonsurvival patients as high risk (risk probabilities >50%) compared to only 12.7% nonsurvival patients by TIMI (score >5) (Figure below). Conclusions In the ASIAN population, patients with STEMI can be better classified using the DL algorithm compared to the ML and TIMI score. Combining ML feature selection with DL allows the identification of distinct factors in a unique ASIAN population for better mortality prediction than relying solely on an expert recommendation as it is a very subjective approach. Continuous validation on population-specific algorithms using DL and ML is needed before implementing in a real clinical setting. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Technology Development Fund 1 TIMI performance on validation set  DL performance on validation set

Risk stratification of Asian patients after ST-elevation myocardial infarction using machine learning methods

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S.S Kasim ◽  
S Malek ◽  
K.K.S Ibrahim ◽  
M.F Aziz
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Risk Stratification ◽  
Mortality Prediction ◽  
Public Institution ◽  
St Elevation Myocardial Infarction ◽  
Funding Source ◽  
Timi Score ◽  
Testing Data ◽  
St Elevation

Abstract Background Risk stratification in ST-elevation myocardial infarction (STEMI) that is population-specific is essential. Conventional risk stratification methods such Thrombolysis in Myocardial Infarction (TIMI) score is used to evaluate the risk associated with the acute coronary syndrome (ACS) which are derived from Western Caucasian cohort with a limited participant from the Asian region. In Malaysia, multi-ethnic developing country, patients presenting with STEMI are younger, have a much higher prevalence of diabetes, hypertension and renal failure, and present later to medical care than their western counterparts. Purpose We aim to investigate the predictors, predict mortality and develop a risk stratification tool for short and long term mortality in multi-ethnic STEMI patients using machine learning (ML) method. Methods We created three separate mortality prediction models using support vector machine (SVM) to identify predictors and predict mortality for in-hospital, 30-days and 1-year for STEMI patients. We used registry data from the National Cardiovascular Disease Database of 6299 patient's data for in-hospital, 3130 for 30-days and 2939 for 1-year for ML model development. Fifty parameters including demographics, cardiovascular risk, medications and clinical variables were utilised for training the models. The Area under the curve (AUC) was used as the primary performance evaluation metric. All models were validated against conventional method TIMI and tested using testing data. SVM variable importance method were used to select and rank important variables. We converted the final algorithm into an online tool with a database for continuous algorithm validation. We implemented the online calculator in selected hospitals for further testing using prospective patients data. Results The calculator is available at http://myheartstemi.uitm.edu.my. The calculator outperforms TIMI on testing data for in-hospital (15 predictors) (AUC=0.88 vs 0.81), 30 days (12 predictors) (AUC=0.90 vs 0.80) and 1-year (13 predictors) (AUC=0.84 vs 0.76). Common predictors for in-hospital, 30 days and 1-year mortality model identified in this study are; age, heart rate, Killip class, fasting blood glucose and diuretics. Invasive and less invasive treatments such as PCI pharmacotherapy drugs are also selected as important variables that improve mortality prediction. Our results also suggest that TIMI score underestimates patients risk of mortality. 90% of non-survival patients are classified as high risk (>30%) by the calculator compared 10–30% non-survival patients by TIMI. Conclusions In the multi-ethnicity population, patients with STEMI are better classified using ML method compared to the TIMI score. ML allows identification of distinct factors in unique ASIAN population for better mortality prediction. Availability of population-specific calculator and continuous testing and validation allows better risk stratification. Machine learning and TIMI performance Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): University of Malaya Grant

scholarly journals Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0254894
Author(s):  
Firdaus Aziz ◽  
Sorayya Malek ◽  
Khairul Shafiq Ibrahim ◽  
Raja Ezman Raja Shariff ◽  
Wan Azman Wan Ahmad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Risk Score ◽  
Fasting Blood Glucose ◽  
Mortality Prediction ◽  
Machine Learning Algorithms ◽  
Timi Score ◽  
Short And Long Term ◽  
Long Term Mortality

Background Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific. Objective Apply machine learning for the prediction and identification of factors associated with short and long-term mortality in Asian STEMI patients and compare with a conventional risk score. Methods The National Cardiovascular Disease Database for Malaysia registry, of a multi-ethnic, heterogeneous Asian population was used for in-hospital (6299 patients), 30-days (3130 patients), and 1-year (2939 patients) model development. 50 variables were considered. Mortality prediction was analysed using feature selection methods with machine learning algorithms and compared to Thrombolysis in Myocardial Infarction (TIMI) score. Invasive management of varying degrees was selected as important variables that improved mortality prediction. Results Model performance using a complete and reduced variable produced an area under the receiver operating characteristic curve (AUC) from 0.73 to 0.90. The best machine learning model for in-hospital, 30 days, and 1-year outperformed TIMI risk score (AUC = 0.88, 95% CI: 0.846–0.910; vs AUC = 0.81, 95% CI:0.772–0.845, AUC = 0.90, 95% CI: 0.870–0.935; vs AUC = 0.80, 95% CI: 0.746–0.838, AUC = 0.84, 95% CI: 0.798–0.872; vs AUC = 0.76, 95% CI: 0.715–0.802, p < 0.0001 for all). TIMI score underestimates patients’ risk of mortality. 90% of non-survival patients are classified as high risk (>50%) by machine learning algorithm compared to 10–30% non-survival patients by TIMI. Common predictors identified for short- and long-term mortality were age, heart rate, Killip class, fasting blood glucose, prior primary PCI or pharmaco-invasive therapy and diuretics. The final algorithm was converted into an online tool with a database for continuous data archiving for algorithm validation. Conclusions In a multi-ethnic population, patients with STEMI were better classified using the machine learning method compared to TIMI scoring. Machine learning allows for the identification of distinct factors in individual Asian populations for better mortality prediction. Ongoing continuous testing and validation will allow for better risk stratification and potentially alter management and outcomes in the future.

Abstract 15728: Machine Learning Helps Predict Short and Intermediate-term Risk for All Cause Mortality in Patients Undergoing Percutaneous Coronary Intervention

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Pradyumna Agasthi ◽  
Hasan Ashraf ◽  
Chieh-Ju Chao ◽  
Panwen Wang ◽  
Mohamed Allam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Percutaneous Coronary Intervention ◽  
Coronary Intervention ◽  
Machine Learning Algorithms ◽  
St Elevation Myocardial Infarction ◽  
Percutaneous Coronary ◽  
All Cause Mortality ◽  
St Elevation ◽  
Clinical Records

Background: Identifying patients at a high risk of mortality post percutaneous coronary intervention (PCI) is of vital clinical importance. We investigated the utility of machine learning algorithms to predict short and intermediate-term risk of all-cause mortality in patients undergoing PCI. Methods: Patient-level demographics, clinical, electrocardiographic ,echocardiographic and angiographic data from January 2006 to December 2017 were extracted from the Mayo Clinic CathPCI registry and clinical records. For patients with multiple PCI events, data collected at the time of the index PCI was used for analysis. Patients who underwent bailout coronary artery bypass graft surgery (CABG) prior to discharge were excluded. 306 variables were incorporated into random forest machine learning model (RF) to predict all-cause mortality at 6 months and 1 year after PCI. Ten-fold cross-validation repeated five times was used to optimize the hyperparameters and estimate its external performance. The National Cardiovascular Data Registry (NCDR) based logistic regression model was used for comparison. The area under receiver operator characteristic curves (AUC) was calculated to assess the ability of the models to predict all-cause mortality. Results: A total of 17356 unique patients were included for the final analysis after excluding 165 patients who underwent CABG surgery during the index hospitalization. The mean age was 66.9 ± 12.5 years;71% were male. Indications for PCI were ST-elevation myocardial infarction (9.4%), non-ST elevation myocardial infarction (12.9%), unstable angina (17.7%), and stable angina (52.8%) in the cohort. In-hospital, 6-month & 1 year mortality rates were 1.9%,4.2% & 5.8% respectively. The RF model was superior to the NCDR model in predicting inhospital, 6-month, 1 year mortality (p<0.0001) ( Figure 1 ). Conclusion: Machine learning is superior to NCDR model in predicting short and intermediate risk of all-cause mortality post PCI.

scholarly journals Comparative Analysis on Machine Learning and Deep Learning to Predict Post-Induction Hypotension

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4575 ◽  
Author(s):  
Jihyun Lee ◽  
Jiyoung Woo ◽  
Ah Reum Kang ◽  
Young-Seob Jeong ◽  
Woohyun Jung ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feature Selection ◽  
Deep Learning ◽  
Random Forest ◽  
Tracheal Intubation ◽  
Feature Engineering ◽  
Learning Models ◽  
Raw Data ◽  
Vital Records

Hypotensive events in the initial stage of anesthesia can cause serious complications in the patients after surgery, which could be fatal. In this study, we intended to predict hypotension after tracheal intubation using machine learning and deep learning techniques after intubation one minute in advance. Meta learning models, such as random forest, extreme gradient boosting (Xgboost), and deep learning models, especially the convolutional neural network (CNN) model and the deep neural network (DNN), were trained to predict hypotension occurring between tracheal intubation and incision, using data from four minutes to one minute before tracheal intubation. Vital records and electronic health records (EHR) for 282 of 319 patients who underwent laparoscopic cholecystectomy from October 2018 to July 2019 were collected. Among the 282 patients, 151 developed post-induction hypotension. Our experiments had two scenarios: using raw vital records and feature engineering on vital records. The experiments on raw data showed that CNN had the best accuracy of 72.63%, followed by random forest (70.32%) and Xgboost (64.6%). The experiments on feature engineering showed that random forest combined with feature selection had the best accuracy of 74.89%, while CNN had a lower accuracy of 68.95% than that of the experiment on raw data. Our study is an extension of previous studies to detect hypotension before intubation with a one-minute advance. To improve accuracy, we built a model using state-of-art algorithms. We found that CNN had a good performance, but that random forest had a better performance when combined with feature selection. In addition, we found that the examination period (data period) is also important.

P5476Usefulness of haemoglobin level combined with CAMI-STEMI score for predicting MACCE in patients with acute ST-elevation myocardial infarction after PCI

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
E Luo ◽  
D Wang ◽  
C Tang ◽  
G Yan ◽  
J Hou ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Haemoglobin Level ◽  
Independent Predictor ◽  
Survival Curve ◽  
Coronary Intervention ◽  
Multivariate Regression Analysis ◽  
St Elevation Myocardial Infarction ◽  
Timi Score ◽  
St Elevation ◽  
Group 2

Abstract Background Early risk stratification was strongly recommended to guide therapeutic management and to improve outcome for ST elevation myocardial infarction (STEMI) patients. Anaemia and high haemoglobin levels are common in STEMI patients, but the effect of the haemoglobin level on the prognosis of STEMI patients remains in dispute. The China Acute Myocardial Infarction registry-ST Elevation Myocardial Infarction (CAMI-STEMI) score can predict in-hospital mortality among Chinese STEMI patients, with similar performance to the well-established Thrombolysis in Myocardial Infarction (TIMI) score, while relying solely on simple and practical variables. This study aimed to evaluate the prognostic value of the haemoglobin level combined with the CAMI-STEMI score in STEMI patients after percutaneous coronary intervention (PCI). Methods We included 360 STEMI patients who underwent PCI. The patients were divided into 3 groups according to the first haemoglobin value after PCI, group 1 (male: Hb<120 g/L, female: Hb<110 g/L; 42 cases), group 2 (male: 120 g/L ≤ Hb<160 g/L, female: 110 g/L≤Hb<150 g/L; 278 cases), and group 3 (male: Hb ≥160 g/L, female: Hb ≥150 g/L; 40 cases). Clinical characteristics, and the incidence of major adverse cardiovascular and cerebral events (MACCE) during the follow-up period were recorded. Results The incidence of MACCE in the 3 groups increased with a decrease in the haemoglobin level. Multivariate regression analysis showed that the CAMI-STEMI score was an independent predictor of MACCE incidence at 30 days after PCI and that anaemia was an independent predictor of MACCE incidence at 6 months and 1 year after PCI. A high haemoglobin level was an independent predictor of MACCE incidence at 1 year after PCI. The area under receiver operating characteristic curves (AUCs) of the haemoglobin level, CAMI-STEMI score and haemoglobin level combined with CAMI-STEMI score predicting the occurrence of MACCE in STEMI patients within 30 days after PCI were 0.604, 0.614, and 0.639, respectively. Figure 1. MACCE-free survival curve Conclusion The CAMI-STEMI score was an independent predictor of MACCE incidence at 30 days after PCI. The haemoglobin level combined with the CAMI-STEMI score improved the predictive value of MACCE in STEMI patients within 30 days after PCI. Acknowledgement/Funding This study was supported by grants to Chengchun Tang from the National Natural Science Foundation of China (Research Grant #81670237)

scholarly journals The NMT Scalp EEG Dataset: An Open-Source Annotated Dataset of Healthy and Pathological EEG Recordings for Predictive Modeling

2022 ◽  
Vol 15 ◽  
Author(s):  
Hassan Aqeel Khan ◽  
Rahat Ul Ain ◽  
Awais Mehmood Kamboh ◽  
Hammad Tanveer Butt ◽  
Saima Shafait ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Open Source ◽  
Asian Population ◽  
Machine Learning Algorithms ◽  
University Hospital ◽  
South Asian Population ◽  
Diagnostic Screening ◽  
Scalp Eeg ◽  
Eeg Recordings

Electroencephalogram (EEG) is widely used for the diagnosis of neurological conditions like epilepsy, neurodegenerative illnesses and sleep related disorders. Proper interpretation of EEG recordings requires the expertise of trained neurologists, a resource which is scarce in the developing world. Neurologists spend a significant portion of their time sifting through EEG recordings looking for abnormalities. Most recordings turn out to be completely normal, owing to the low yield of EEG tests. To minimize such wastage of time and effort, automatic algorithms could be used to provide pre-diagnostic screening to separate normal from abnormal EEG. Data driven machine learning offers a way forward however, design and verification of modern machine learning algorithms require properly curated labeled datasets. To avoid bias, deep learning based methods must be trained on large datasets from diverse sources. This work presents a new open-source dataset, named the NMT Scalp EEG Dataset, consisting of 2,417 recordings from unique participants spanning almost 625 h. Each recording is labeled as normal or abnormal by a team of qualified neurologists. Demographic information such as gender and age of the patient are also included. Our dataset focuses on the South Asian population. Several existing state-of-the-art deep learning architectures developed for pre-diagnostic screening of EEG are implemented and evaluated on the NMT, and referenced against baseline performance on the well-known Temple University Hospital EEG Abnormal Corpus. Generalization of deep learning based architectures across the NMT and the reference datasets is also investigated. The NMT dataset is being released to increase the diversity of EEG datasets and to overcome the scarcity of accurately annotated publicly available datasets for EEG research.

scholarly journals Complications Associated to High TIMI Score in Patients with Acute ST Elevation Myocardial Infarction

2021 ◽  
Vol 15 (5) ◽  
pp. 1526-1528
Author(s):  
M. U. Rehman ◽  
F. Faisal ◽  
A. Abrar ◽  
A. A. Shah ◽  
M. Shoaib ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Hospital Stay ◽  
Risk Score ◽  
St Elevation Myocardial Infarction ◽  
Cross Sectional ◽  
Timi Score ◽  
Detailed Medical History ◽  
St Elevation ◽  
Timi Risk Score

Objective: To determine the complications associated to High TIMI risk score among patients presented with acute ST elevation myocardial infarction. Study Design: Cross sectional Place & Duration: Study was conducted at Cardiac Centre of Pakistan Institute of Medical Sciences (PIMS), Islamabadfor duration of 6 months from January to June, 2020. Methods: Total 290 patients of both genders with ages 35 to 80 years presented with acute myocardial infarction were included in this study. Patients detailed medical history including age, sex and residence were recorded. Thrombolysis in Myocardial Infarction (TIMI) risk score was calculated for each patient. Follow up was taken during the hospital stay and after discharge. Complications were recorded on follow-up. Data was analyzed by SPSS 21.0. Results: From all the patients high TIMI score was found in 34.48% patients. Out of 100 patients 70% were male and 30% were females with mean age 54.25+12.65 years. According to the high TIMI score 100 (34.48%) patients had score above 8 and 190 (65.52%) had score less than 8. Complications were recorded ad Ventricular fibrillation, VT, AF, Heart block, cardiogenic shock and pulmonary edema in 17%, 13%, 2%, 7%, 24% and 24% patients respectively.15% patients were died during hospital stay. 28% patients had post infarct angina, 9% patients had stroke and 28% patients treated revascularization. Conclusion: We concluded from this study that frequency of high TIMI score is high in our setting and we patients with increase score had high risk of complications and mortality. Keywords: High Thrombolysis in Myocardial Infarction, Acute ST Elevation Myocardial Infarction, Frequency, Complications, Mortality.

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