Heart-Rate-Based Machine-Learning Algorithms for Screening Orthostatic Hypotension

Introduction: The Pulse Initiative on resuscitation identified the need to develop biosensing for detection of critical limitations of blood flow. The ability to rapidly detect the severity of hemorrhage based on heart rate has been limited. Use of heart rate variability (HRV) is problematic. We used a number of new defined ECG features based on discrete wavelet transformation (DWT) that may be used to estimate blood loss severity. The features are defined based on the energy of detail coefficients of Daubecies DWT. Methods: The performance of DWT was tested using ECG data from a human model of hemorrhage using lower body negative pressure (LBNP). LBNP consisted of a 5-minute rest period (0 mm Hg) followed by 5 minutes of chamber decompression of the lower body to −15, −30, −45, and −60 mm Hg and additional increments of −10 mm Hg every 5 minutes until the onset of cardiovascular collapse. These levels were divided into 3 classes (mild: −15 to −30 mmHg; moderate: −45 to −60 mmHg; severe: over −60 mmHg). These levels correspond to estimated blood losses of 400 –550 cc, 500 –1000 cc and greater than 1000 cc respectively. The ECG DWT features of subjects were used for classification of each ECG recording during volume loss levels. Before classification in order to eliminate redundancy among the features, principal component analysis is applied to the feature set. Machine learning algorithms (SVM, AdaBoost, C4.5) were then applied to analyze the processed features and predict the severity of blood loss. Results: A 219 sample set was used to classify groups by using machine learning algorithms with 10-fold cross validation. C4.5 outperformed other algorithms with a prediction accuracy of 74.4%. The average precision and recall (sensitivity) for the three classes were 77.4% and 76.1%, respectively. In particular, 30 out of 39 cases in the severe class were correctly classified by C4.5. These results required sampling rates of only 125 Hz. Conclusion: This is the first reported use of an ECG analysis method to classify volume loss. The DWT method described may have the ability to rapidly determine the degree of volume loss from hemorrhage providing for more rapid triage and decision making. This may be particularly helpful for remote monitoring of war fighters or for mass casualty care.

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Supplemental Material for One Model to Rule Them All? Using Machine Learning Algorithms to Determine the Number of Factors in Exploratory Factor Analysis

Psychological Methods ◽

10.1037/met0000262.supp ◽

2020 ◽

Keyword(s):

Machine Learning ◽

Factor Analysis ◽

Exploratory Factor Analysis ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Number Of Factors

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Forecasting US movies box office performances in Turkey using machine learning algorithms

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189120 ◽

2020 ◽

Vol 39 (5) ◽

pp. 6579-6590

Author(s):

Sandy Çağlıyor ◽

Başar Öztayşi ◽

Selime Sezgin

Keyword(s):

Machine Learning ◽

Global Economy ◽

Learning Algorithms ◽

Forecast Model ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

High Stakes ◽

Box Office ◽

Industry Forecast ◽

The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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Intelligent system of English composition scoring model based on improved machine learning algorithm

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189235 ◽

2020 ◽

pp. 1-11

Author(s):

Jie Liu ◽

Lin Lin ◽

Xiufang Liang

Keyword(s):

Machine Learning ◽

Evaluation System ◽

Intelligent System ◽

Learning Algorithm ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Assessment System ◽

English Composition ◽

Region Extraction ◽

Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

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The Unlearnable Checkerboard Pattern

Communications of the Blyth Institute ◽

10.33014/issn.2640-5652.1.2.holloway.1 ◽

2019 ◽

Vol 1 (2) ◽

pp. 78-80

Author(s):

Eric Holloway

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Checkerboard Pattern ◽

Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms. Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

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