Evaluating User and Machine Learning in Short- and Long-Term Pattern Recognition-Based Myoelectric Control

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.

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Reducing error propagation for long term energy forecasting using multivariate prediction

10.29007/mbb7 ◽

2020 ◽

Author(s):

Maher Selim ◽

Ryan Zhou ◽

Wenying Feng ◽

Omar Alam

Keyword(s):

Machine Learning ◽

Learning Models ◽

Short Term ◽

Energy Forecasting ◽

Opposite Behavior ◽

Multivariate Prediction ◽

Term Energy ◽

Short And Long Term ◽

Machine Learning Models

Many statistical and machine learning models for prediction make use of historical data as an input and produce single or small numbers of output values. To forecast over many timesteps, it is necessary to run the program recursively. This leads to a compounding of errors, which has adverse effects on accuracy for long forecast periods. In this paper, we show this can be mitigated through the addition of generating features which can have an “anchoring” effect on recurrent forecasts, limiting the amount of compounded error in the long term. This is studied experimentally on a benchmark energy dataset using two machine learning models LSTM and XGBoost. Prediction accuracy over differing forecast lengths is compared using the forecasting MAPE. It is found that for LSTM model the accuracy of short term energy forecasting by using a past energy consumption value as a feature is higher than the accuracy when not using past values as a feature. The opposite behavior takes place for the long term energy forecasting. For the XGBoost model, the accuracy for both short and long term energy forecasting is higher when not using past values as a feature.

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Is it time to use machine learning survival algorithms for survival and risk factors prediction instead of Cox proportional hazard regression? A comparative population-based study

10.1101/2021.11.20.21266627 ◽

2021 ◽

Author(s):

Sara Morsy ◽

Truong Hong Hieu ◽

Abdelrahman M Makram ◽

Osama Gamal Hassan ◽

Nguyen Tran Minh Duc ◽

...

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Survival Duration ◽

Brier Score ◽

Survival Prediction ◽

Proportional Hazard ◽

Cancer Data ◽

Cox Proportional Hazard ◽

Short And Long Term

Purpose Applying machine learning in medical statistics offers more accurate prediction models. In this paper, we aimed to compare the performance of the Cox Proportional Hazard model (CPH), Classification and Regression Trees (CART), and Random Survival Forest (RSF) in short-, and long-term prediction in glioblastoma patients. Methods We extracted glioblastoma cancer data from the Surveillance, Epidemiology, and End Results database (SEER). We used the CPH, CART, and RSF for the prediction of 1- to 10-year survival probabilities. The Brier Score for each duration was calculated, and the model with the least score was considered the most accurate. Results The cohort included 26473 glioblastoma patients divided into two groups: training (n = 18538) and validation set (n = 7935). The average survival duration was seven months. For the short- and long-term predictions, RSF was the best algorithm followed by CPH and CART. Conclusion For big data, RSF was found to have the highest accuracy and best performance. Using an accurate statistical model for survival prediction and prognostic factors determination will help the care of cancer patients. However, more developments of the R packages are needed to allow more illustrations of the effect of each covariate on the survival probability.

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Pattern recognition in long-term Sooty Shearwater data: applying machine learning to create a harvest index

Ecological Applications ◽

10.1890/13-2023.1 ◽

2014 ◽

Vol 24 (8) ◽

pp. 2107-2121 ◽

Cited By ~ 1

Author(s):

G. R. W. Humphries ◽

C. Bragg ◽

J. Overton ◽

P. O'B. Lyver ◽

H. Moller

Keyword(s):

Machine Learning ◽

Pattern Recognition ◽

Harvest Index

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Towards an integration of deep learning and neuroscience

10.1101/058545 ◽

2016 ◽

Cited By ~ 5

Author(s):

Adam Henry Marblestone ◽

Greg Wayne ◽

Konrad P Kording

Keyword(s):

Machine Learning ◽

Memory Storage ◽

Cost Functions ◽

Long Term Memory ◽

Recent Developments ◽

Short And Long Term ◽

The Brain ◽

And Training ◽

Learning Data

Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion and various forms of short- and long-term memory storage. Second, cost functions and training procedures have become more complex and are varied across layers and over time. Here we think about the brain in terms of these ideas. We hypothesize that (1) the brain optimizes cost functions, (2) these cost functions are diverse and differ across brain locations and over development, and (3) optimization operates within a pre-structured architecture matched to the computational problems posed by behavior. Such a heterogeneously optimized system, enabled by a series of interacting cost functions, serves to make learning data-efficient and precisely targeted to the needs of the organism. We suggest directions by which neuroscience could seek to refine and test these hypotheses.

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A machine-learning framework for robust and reliable prediction of short- and long-term treatment response in initially antipsychotic-naïve schizophrenia patients based on multimodal neuropsychiatric data

Translational Psychiatry ◽

10.1038/s41398-020-00962-8 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Karen S. Ambrosen ◽

Martin W. Skjerbæk ◽

Jonathan Foldager ◽

Martin C. Axelsen ◽

Nikolaj Bak ◽

...

Keyword(s):

Machine Learning ◽

Treatment Response ◽

Term Treatment ◽

Reliable Prediction ◽

Learning Framework ◽

Long Term Treatment ◽

Short And Long Term

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Predicting short‐ and long‐term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine‐learning algorithms

Diabetes Obesity and Metabolism ◽

10.1111/dom.13860 ◽

2019 ◽

Vol 21 (12) ◽

pp. 2704-2711 ◽

Cited By ~ 1

Author(s):

Sunil B. Nagaraj ◽

Grigory Sidorenkov ◽

Job F. M. Boven ◽

Petra Denig

Keyword(s):

Diabetes Mellitus ◽

Machine Learning ◽

Type 2 Diabetes ◽

Type 2 Diabetes Mellitus ◽

Glycated Haemoglobin ◽

Machine Learning Algorithms ◽

Insulin Initiation ◽

Short And Long Term

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S12. A MACHINE LEARNING FRAMEWORK FOR ROBUST AND RELIABLE PREDICTION OF SHORT- AND LONG-TERM CLINICAL RESPONSE IN INITIALLY ANTIPSYCHOTIC-NAïVE SCHIZOPHRENIA PATIENTS BASED ON MULTIMODAL NEUROPSYCHIATRIC DATA

Schizophrenia Bulletin ◽

10.1093/schbul/sbaa031.078 ◽

2020 ◽

Vol 46 (Supplement_1) ◽

pp. S34-S35

Author(s):

Karen S Ambrosen ◽

Martin W Skjerbæk ◽

Jonathan Foldager ◽

Martin C Axelsen ◽

Nikolaj Bak ◽

...

Keyword(s):

Machine Learning ◽

Clinical Response ◽

Treatment Response ◽

Simulated Data ◽

Real Data ◽

First Episode ◽

Methodological Framework ◽

The Real ◽

Short And Long Term

Abstract Background The treatment response of patients with schizophrenia is heterogeneous, and markers of clinical response are missing. Studies using machine learning approaches have provided encouraging results regarding prediction of outcomes, but replicability has been challenging. In the present study, we present a novel methodological framework for applying machine learning to clinical data. Herein, algorithm selection and other methodological choices were based on model performance on a simulated dataset, to minimize bias and avoid overfitting. We subsequently applied the best performing machine learning algorithm to a rich, multimodal neuropsychiatric dataset. We aimed to 1) classify patients from controls, 2) predict short- and long-term clinical response in a sample of initially antipsychotic-naïve first-episode schizophrenia patients, and 3) validate our methodological framework. Methods We included data from 138 antipsychotic-naïve, first-episode schizophrenia patients, who had undergone assessments of psychopathology, cognition, electrophysiology, structural magnetic resonance imaging (MRI). Perinatal data and long-term outcome measures were obtained from Danish registers. Baseline diagnostic classification algorithms also included data from 151 matched healthy controls. Short-term treatment response was defined as change in psychopathology after the initial antipsychotic treatment period. Long-term treatment response (4–16 years) was based on data from Danish registers. The simulated dataset was generated to resemble the real data with respect to dimensionality, multimodality, and pattern of missing data. Noise levels were tunable to enable approximation to the signal-to-noise ratio in the real data. Robustness of the results was ensured by running two parallel, fundamentally different machine learning pipelines, a ‘single algorithm approach’ and an ‘ensemble approach’. Both pipelines included nested cross-validation, missing data imputation, and late integration. Results We significantly classified patients from controls with a balanced accuracy of 64.2% (95% CI = [51.7, 76.7]) for the single algorithm approach and 63.1% (95% CI = [50.4, 75.8]) for the ensemble approach. Post hoc analyses showed that the classification primarily was driven by the cognitive data. Neither approach predicted short- and long-term clinical response. To validate our methodological framework based on simulated data, we selected the best, a medium, and the most poorly performing algorithm on the simulated data and applied them to the real data. We found that the ranking of the algorithms was kept in the real data. Discussion Our rigorous modelling framework incorporating simulated data and parallel pipelines discriminated patients from controls, but our extensive, multimodal neuropsychiatric data from antipsychotic-naïve schizophrenia patients were not predictive of the clinical outcome. Nevertheless, our novel approach holds promise as an important step to obtain reliable, unbiased results with modest sample sizes when independent replication samples are not available.

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Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction

Scientific Reports ◽

10.1038/s41598-021-92362-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Woojoo Lee ◽

Joongyub Lee ◽

Seoung-Il Woo ◽

Seong Huan Choi ◽

Jang-Whan Bae ◽

...

Keyword(s):

Machine Learning ◽

Myocardial Infarction ◽

Support Vector ◽

St Segment Elevation ◽

Segment Elevation Myocardial Infarction ◽

St Segment ◽

Extreme Gradient Boosting ◽

Short And Long Term ◽

Long Term Mortality

AbstractMachine learning (ML) has been suggested to improve the performance of prediction models. Nevertheless, research on predicting the risk in patients with acute myocardial infarction (AMI) has been limited and showed inconsistency in the performance of ML models versus traditional models (TMs). This study developed ML-based models (logistic regression with regularization, random forest, support vector machine, and extreme gradient boosting) and compared their performance in predicting the short- and long-term mortality of patients with AMI with those of TMs with comparable predictors. The endpoints were the in-hospital mortality of 14,183 participants and the three- and 12-month mortality in patients who survived at discharge. The performance of the ML models in predicting the mortality of patients with an ST-segment elevation myocardial infarction (STEMI) was comparable to the TMs. In contrast, the areas under the curves (AUC) of the ML models for non-STEMI (NSTEMI) in predicting the in-hospital, 3-month, and 12-month mortality were 0.889, 0.849, and 0.860, respectively, which were superior to the TMs, which had corresponding AUCs of 0.873, 0.795, and 0.808. Overall, the performance of the predictive model could be improved, particularly for long-term mortality in NSTEMI, from the ML algorithm rather than using more clinical predictors.

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Machine learning for suicide risk prediction in children and adolescents with electronic health records

Translational Psychiatry ◽

10.1038/s41398-020-01100-0 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Chang Su ◽

Robert Aseltine ◽

Riddhi Doshi ◽

Kun Chen ◽

Steven C. Rogers ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Children And Adolescents ◽

Suicidal Behavior ◽

Predictive Models ◽

Suicide Risk ◽

Health Records ◽

Short And Long Term ◽

Clinical Records

AbstractAccurate prediction of suicide risk among children and adolescents within an actionable time frame is an important but challenging task. Very few studies have comprehensively considered the clinical risk factors available to produce quantifiable risk scores for estimation of short- and long-term suicide risk for pediatric population. In this paper, we built machine learning models for predicting suicidal behavior among children and adolescents based on their longitudinal clinical records, and determining short- and long-term risk factors. This retrospective study used deidentified structured electronic health records (EHR) from the Connecticut Children’s Medical Center covering the period from 1 October 2011 to 30 September 2016. Clinical records of 41,721 young patients (10–18 years old) were included for analysis. Candidate predictors included demographics, diagnosis, laboratory tests, and medications. Different prediction windows ranging from 0 to 365 days were adopted. For each prediction window, candidate predictors were first screened by univariate statistical tests, and then a predictive model was built via a sequential forward feature selection procedure. We grouped the selected predictors and estimated their contributions to risk prediction at different prediction window lengths. The developed predictive models predicted suicidal behavior across all prediction windows with AUCs varying from 0.81 to 0.86. For all prediction windows, the models detected 53–62% of suicide-positive subjects with 90% specificity. The models performed better with shorter prediction windows and predictor importance varied across prediction windows, illustrating short- and long-term risks. Our findings demonstrated that routinely collected EHRs can be used to create accurate predictive models for suicide risk among children and adolescents.

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