A machine-learning approach to map landscape connectivity inAedes aegyptiwith genetic and environmental data

Mapping landscape connectivity is important for controlling invasive species and disease vectors. Current landscape genetics methods are often constrained by the subjectivity of creating resistance surfaces and the difficulty of working with interacting and correlated environmental variables. To overcome these constraints, we combine the advantages of a machine-learning framework and an iterative optimization process to develop a method for integrating genetic and environmental (e.g., climate, land cover, human infrastructure) data. We validate and demonstrate this method for theAedes aegyptimosquito, an invasive species and the primary vector of dengue, yellow fever, chikungunya, and Zika. We test two contrasting metrics to approximate genetic distance and find Cavalli-Sforza–Edwards distance (CSE) performs better than linearized FST. The correlation (R) between the model’s predicted genetic distance and actual distance is 0.83. We produce a map of genetic connectivity forAe. aegypti’s range in North America and discuss which environmental and anthropogenic variables are most important for predicting gene flow, especially in the context of vector control.

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Delivering Precision Medicine to Patients with Spinal Cord Disorders; Insights into Applications of Bioinformatics and Machine Learning from Studies of Degenerative Cervical Myelopathy

10.5772/intechopen.98713 ◽

2021 ◽

Author(s):

Kalum J. Ost ◽

David W. Anderson ◽

David W. Cadotte

Keyword(s):

Machine Learning ◽

Precision Medicine ◽

New Technologies ◽

Machine Learning Techniques ◽

Massive Datasets ◽

Learning Framework ◽

Learning Techniques ◽

Machine Learning Approach ◽

Spinal Cord Disorders ◽

Degenerative Cervical Myelopathy

With the common adoption of electronic health records and new technologies capable of producing an unprecedented scale of data, a shift must occur in how we practice medicine in order to utilize these resources. We are entering an era in which the capacity of even the most clever human doctor simply is insufficient. As such, realizing “personalized” or “precision” medicine requires new methods that can leverage the massive amounts of data now available. Machine learning techniques provide one important toolkit in this venture, as they are fundamentally designed to deal with (and, in fact, benefit from) massive datasets. The clinical applications for such machine learning systems are still in their infancy, however, and the field of medicine presents a unique set of design considerations. In this chapter, we will walk through how we selected and adjusted the “Progressive Learning framework” to account for these considerations in the case of Degenerative Cervical Myeolopathy. We additionally compare a model designed with these techniques to similar static models run in “perfect world” scenarios (free of the clinical issues address), and we use simulated clinical data acquisition scenarios to demonstrate the advantages of our machine learning approach in providing personalized diagnoses.

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Machine Learning for Mechanical Ventilation Control

10.1101/2021.02.26.21252524 ◽

2021 ◽

Author(s):

Daniel Suo ◽

Cyril Zhang ◽

Paula Gradu ◽

Udaya Ghai ◽

Xinyi Chen ◽

...

Keyword(s):

Machine Learning ◽

Data Driven ◽

Pid Controllers ◽

Industry Standard ◽

Pressure Controlled Ventilation ◽

Machine Learning Approach ◽

Target Pressure ◽

Pressure Controlled ◽

Neural Network Controllers ◽

Better Than

We consider the problem of controlling an invasive mechanical ventilator for pressure-controlled ventilation: a controller must let air in and out of a sedated patient's lungs according to a trajectory of airway pressures specified by a clinician. Hand-tuned PID controllers and similar variants have comprised the industry standard for decades, yet can behave poorly by over- or under-shooting their target or oscillating rapidly. We consider a data-driven machine learning approach: First, we train a simulator based on data we collect from an artificial lung. Then, we train deep neural network controllers on these simulators.We show that our controllers are able to track target pressure waveforms significantly better than PID controllers. We further show that a learned controller generalizes across lungs with varying characteristics much more readily than PID controllers do.

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Tensorflow vs R: A Comparative Study of Usability

10.5753/sbsi.2017.6093 ◽

2017 ◽

Author(s):

Luís Dias ◽

Rosalvo Neto

Keyword(s):

Neural Network ◽

Machine Learning ◽

Comparative Study ◽

Scientific Community ◽

Deep Neural Network ◽

Network Algorithms ◽

Learning Framework ◽

Usability Tests ◽

Usability Analysis ◽

Better Than

Google released on November of 2015 Tensorflow, an open source machine learning framework that can be used to implement Deep Neural Network algorithms, a class of algorithms that shows great potential in solving complex problems. Considering the importance of usability in software success, this research aims to perform a usability analysis on Tensorflow and to compare it with another widely used framework, R. The evaluation was performed through usability tests with university students. The study led do indications that Tensorflow usability is equal or better than the usability of traditional frameworks used by the scientific community.

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Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach

10.21203/rs.2.15371/v4 ◽

2020 ◽

Author(s):

Jan Wolff ◽

Alexander Gary ◽

Daniela Jung ◽

Claus Normann ◽

Klaus Kaier ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Psychiatric Hospital ◽

Hospital Care ◽

Routine Data ◽

Psychiatric Hospitals ◽

Predictive Performance ◽

Learning Approach ◽

Machine Learning Approach ◽

Better Than

Abstract Background: A common problem in machine learning applications is availability of data at the point of decision making. The aim of the present study was to use routine data readily available at admission to predict aspects relevant to the organization of psychiatric hospital care. A further aim was to compare the results of a machine learning approach with those obtained through a traditional method and those obtained through a naive baseline classifier. Methods: The study included consecutively discharged patients between 1 st of January 2017 and 31 st of December 2018 from nine psychiatric hospitals in Hesse, Germany. We compared the predictive performance achieved by stochastic gradient boosting (GBM) with multiple logistic regression and a naive baseline classifier. We tested the performance of our final models on unseen patients from another calendar year and from different hospitals. Results: The study included 45,388 inpatient episodes. The models’ performance, as measured by the area under the Receiver Operating Characteristic curve, varied strongly between the predicted outcomes, with relatively high performance in the prediction of coercive treatment (area under the curve: 0.83) and 1:1 observations (0.80) and relatively poor performance in the prediction of short length of stay (0.69) and non-response to treatment (0.65). The GBM performed slightly better than logistic regression. Both approaches were substantially better than a naive prediction based solely on basic diagnostic grouping. Conclusion: The present study has shown that administrative routine data can be used to predict aspects relevant to the organisation of psychiatric hospital care. Future research should investigate the predictive performance that is necessary to provide effective assistance in clinical practice for the benefit of both staff and patients.

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Assessing mangrove deforestation using pixel-based image: a machine learning approach

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v10i6.3199 ◽

2021 ◽

Vol 10 (6) ◽

pp. 3178-3190

Author(s):

Ahmad Yahya Dawod ◽

Mohammed Ali Sharafuddin

Keyword(s):

Machine Learning ◽

Machine Learning Algorithms ◽

Tree Cover ◽

Support Vector ◽

Mangrove Forests ◽

Object Based ◽

Machine Learning Approach ◽

Biomass Loss ◽

Carbon Dioxide Co2 ◽

Better Than

Mangrove is one of the most productive global forest ecosystems and unique in linking terrestrial and marine environment. This study aims to clarify and understand artificial intelligence (AI) adoption in remote sensing mangrove forests. The performance of machine learning algorithms such as random forest (RF), support vector machine (SVM), decision tree (DT), and object-based nearest neighbors (NN) algorithms were used in this study to automatically classify mangrove forests using orthophotography and applying an object-based approach to examine three features (tree cover loss, above-ground carbon dioxide (CO2) emissions, and above-ground biomass loss). SVM with a radial basis function was used to classify the remainder of the images, resulting in an overall accuracy of 96.83%. Precision and recall reached 93.33 and 96%, respectively. RF performed better than other algorithms where there is no orthophotography.

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A Novel Smart City-Based Framework on Perspectives for Application of Machine Learning in Combating COVID-19

BioMed Research International ◽

10.1155/2021/5546790 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Absalom E. Ezugwu ◽

Ibrahim Abaker Targio Hashem ◽

Olaide N. Oyelade ◽

Mubarak Almutari ◽

Mohammed A. Al-Garadi ◽

...

Keyword(s):

Machine Learning ◽

Smart Cities ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Learning Approach ◽

Learning Framework ◽

National Healthcare ◽

Machine Learning Approach ◽

The Internet Of Things ◽

Analyze Data

The spread of COVID-19 worldwide continues despite multidimensional efforts to curtail its spread and provide treatment. Efforts to contain the COVID-19 pandemic have triggered partial or full lockdowns across the globe. This paper presents a novel framework that intelligently combines machine learning models and the Internet of Things (IoT) technology specifically to combat COVID-19 in smart cities. The purpose of the study is to promote the interoperability of machine learning algorithms with IoT technology by interacting with a population and its environment to curtail the COVID-19 pandemic. Furthermore, the study also investigates and discusses some solution frameworks, which can generate, capture, store, and analyze data using machine learning algorithms. These algorithms can detect, prevent, and trace the spread of COVID-19 and provide a better understanding of the disease in smart cities. Similarly, the study outlined case studies on the application of machine learning to help fight against COVID-19 in hospitals worldwide. The framework proposed in the study is a comprehensive presentation on the major components needed to integrate the machine learning approach with other AI-based solutions. Finally, the machine learning framework presented in this study has the potential to help national healthcare systems in curtailing the COVID-19 pandemic in smart cities. In addition, the proposed framework is poised as a pointer for generating research interests that would yield outcomes capable of been integrated to form an improved framework.

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A Machine Learning Approach for Gearbox System Fault Diagnosis

Entropy ◽

10.3390/e23091130 ◽

2021 ◽

Vol 23 (9) ◽

pp. 1130

Author(s):

Jan Vrba ◽

Matous Cejnek ◽

Jakub Steinbach ◽

Zuzana Krbcova

Keyword(s):

Machine Learning ◽

Fault Diagnosis ◽

Prediction Error ◽

Support Vector ◽

Learning Approach ◽

Reference Methods ◽

Machine Learning Approach ◽

Public Dataset ◽

Diagnosis Approach ◽

Better Than

This study proposes a fully automated gearbox fault diagnosis approach that does not require knowledge about the specific gearbox construction and its load. The proposed approach is based on evaluating an adaptive filter’s prediction error. The obtained prediction error’s standard deviation is further processed with a support-vector machine to classify the gearbox’s condition. The proposed method was cross-validated on a public dataset, segmented into 1760 test samples, against two other reference methods. The accuracy achieved by the proposed method was better than the accuracies of the reference methods. The accuracy of the proposed method was on average 9% higher compared to both reference methods for different support vector settings.

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Triage of documents containing protein interactions affected by mutations using an NLP based machine learning approach

BMC Genomics ◽

10.1186/s12864-020-07185-7 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Jinchan Qu ◽

Albert Steppi ◽

Dongrui Zhong ◽

Jie Hao ◽

Jian Wang ◽

...

Keyword(s):

Machine Learning ◽

Language Processing ◽

Protein Interactions ◽

Learning Approach ◽

Protein Protein Interactions ◽

Final Model ◽

Protein Protein Interaction ◽

Learning Framework ◽

Machine Learning Approach ◽

Document Triage

Abstract Background Information on protein-protein interactions affected by mutations is very useful for understanding the biological effect of mutations and for developing treatments targeting the interactions. In this study, we developed a natural language processing (NLP) based machine learning approach for extracting such information from literature. Our aim is to identify journal abstracts or paragraphs in full-text articles that contain at least one occurrence of a protein-protein interaction (PPI) affected by a mutation. Results Our system makes use of latest NLP methods with a large number of engineered features including some based on pre-trained word embedding. Our final model achieved satisfactory performance in the Document Triage Task of the BioCreative VI Precision Medicine Track with highest recall and comparable F1-score. Conclusions The performance of our method indicates that it is ideally suited for being combined with manual annotations. Our machine learning framework and engineered features will also be very helpful for other researchers to further improve this and other related biological text mining tasks using either traditional machine learning or deep learning based methods.

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Novel criteria to classify ARDS severity using a machine learning approach

Critical Care ◽

10.1186/s13054-021-03566-w ◽

2021 ◽

Vol 25 (1) ◽

Author(s):

Mohammed Sayed ◽

David Riaño ◽

Jesús Villar

Keyword(s):

Machine Learning ◽

Distress Syndrome ◽

Learning Approach ◽

The Novel ◽

Arterial Oxygenation ◽

Positive End Expiratory Pressure ◽

Machine Learning Approach ◽

Mimic Iii ◽

Over Time ◽

Better Than

Abstract Background Usually, arterial oxygenation in patients with the acute respiratory distress syndrome (ARDS) improves substantially by increasing the level of positive end-expiratory pressure (PEEP). Herein, we are proposing a novel variable [PaO2/(FiO2xPEEP) or P/FPE] for PEEP ≥ 5 to address Berlin’s definition gap for ARDS severity by using machine learning (ML) approaches. Methods We examined P/FPE values delimiting the boundaries of mild, moderate, and severe ARDS. We applied ML to predict ARDS severity after onset over time by comparing current Berlin PaO2/FiO2 criteria with P/FPE under three different scenarios. We extracted clinical data from the first 3 ICU days after ARDS onset (N = 2738, 1519, and 1341 patients, respectively) from MIMIC-III database according to Berlin criteria for severity. Then, we used the multicenter database eICU (2014–2015) and extracted data from the first 3 ICU days after ARDS onset (N = 5153, 2981, and 2326 patients, respectively). Disease progression in each database was tracked along those 3 ICU days to assess ARDS severity. Three robust ML classification techniques were implemented using Python 3.7 (LightGBM, RF, and XGBoost) for predicting ARDS severity over time. Results P/FPE ratio outperformed PaO2/FiO2 ratio in all ML models for predicting ARDS severity after onset over time (MIMIC-III: AUC 0.711–0.788 and CORR 0.376–0.566; eICU: AUC 0.734–0.873 and CORR 0.511–0.745). Conclusions The novel P/FPE ratio to assess ARDS severity after onset over time is markedly better than current PaO2/FiO2 criteria. The use of P/FPE could help to manage ARDS patients with a more precise therapeutic regimen for each ARDS category of severity.

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EXPRESS: Dynamics of Musical Success: A Machine Learning Approach for Multimedia Data Fusion

Journal of Marketing Research ◽

10.1177/00222437211016495 ◽

2021 ◽

pp. 002224372110164

Author(s):

Khaled Boughanmi ◽

Asim Ansari

Keyword(s):

Machine Learning ◽

Dirichlet Process ◽

Penalized Splines ◽

Multimedia Data ◽

Acoustic Features ◽

Modeling Framework ◽

Learning Framework ◽

Proposed Model ◽

Machine Learning Approach ◽

Different Types

The success of creative products depends upon the felt experience of consumers. Capturing such consumer reactions requires the fusing of different types of experiential covariates and perceptual data in an integrated modeling framework. In this paper, the authors develop a novel multimodal machine learning framework that combines multimedia data (e.g., metadata, acoustic features and user generated textual data) in creative product settings and apply it for predicting the success of musical albums and playlists. The authors estimate the proposed model on a unique dataset which they collected using different online sources. The model integrates different types of nonparametrics to flexibly accommodate diverse types of effects. It uses penalized splines to capture the nonlinear impact of acoustic features and a supervised hierarchical Dirichlet process to represent crowd-sourced textual tags. It captures dynamics via a state-space specification. The authors show the predictive superiority of the model with respect to several benchmarks. The results illuminate the dynamics of musical success over the past five decades. The authors then use the components of the model for marketing decisions such as forecasting the success of new albums, album tuning and diagnostics, construction of playlists for different generations of music listeners, and contextual recommendations.

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