scholarly journals Machine Learning in Action: Stroke Diagnosis and Outcome Prediction

2021 ◽  
Vol 12 ◽  
Author(s):  
Shraddha Mainali ◽  
Marin E. Darsie ◽  
Keaton S. Smetana
Keyword(s):  
Machine Learning ◽  
Outcome Prediction ◽  
Clinical Decision Making ◽  
Small Sample ◽  
Machine Learning Algorithms ◽  
Patient Specific ◽  
Learning Technology ◽  
Learning Tools ◽  
Data Set ◽  
Stroke Diagnosis

The application of machine learning has rapidly evolved in medicine over the past decade. In stroke, commercially available machine learning algorithms have already been incorporated into clinical application for rapid diagnosis. The creation and advancement of deep learning techniques have greatly improved clinical utilization of machine learning tools and new algorithms continue to emerge with improved accuracy in stroke diagnosis and outcome prediction. Although imaging-based feature recognition and segmentation have significantly facilitated rapid stroke diagnosis and triaging, stroke prognostication is dependent on a multitude of patient specific as well as clinical factors and hence accurate outcome prediction remains challenging. Despite its vital role in stroke diagnosis and prognostication, it is important to recognize that machine learning output is only as good as the input data and the appropriateness of algorithm applied to any specific data set. Additionally, many studies on machine learning tend to be limited by small sample size and hence concerted efforts to collate data could improve evaluation of future machine learning tools in stroke. In the present state, machine learning technology serves as a helpful and efficient tool for rapid clinical decision making while oversight from clinical experts is still required to address specific aspects not accounted for in an automated algorithm. This article provides an overview of machine learning technology and a tabulated review of pertinent machine learning studies related to stroke diagnosis and outcome prediction.

Swarm Intelligence Optimization: An Exploration and Application of Machine Learning Technology

2021 ◽  
Vol 30 (1) ◽  
pp. 460-469
Author(s):  
Yinying Cai ◽  
Amit Sharma
Keyword(s):  
Machine Learning ◽  
Swarm Intelligence ◽  
Research Result ◽  
Machine Learning Algorithms ◽  
Learning Technology ◽  
Data Set ◽  
Rice Pests ◽  
Machine Leaning ◽  
Smart Agriculture ◽  
Swarm Intelligence Optimization

Abstract In the agriculture development and growth, the efficient machinery and equipment plays an important role. Various research studies are involved in the implementation of the research and patents to aid the smart agriculture and authors and reviewers that machine leaning technologies are providing the best support for this growth. To explore machine learning technology and machine learning algorithms, the most of the applications are studied based on the swarm intelligence optimization. An optimized V3CFOA-RF model is built through V3CFOA. The algorithm is tested in the data set collected concerning rice pests, later analyzed and compared in detail with other existing algorithms. The research result shows that the model and algorithm proposed are not only more accurate in recognition and prediction, but also solve the time lagging problem to a degree. The model and algorithm helped realize a higher accuracy in crop pest prediction, which ensures a more stable and higher output of rice. Thus they can be employed as an important decision-making instrument in the agricultural production sector.

scholarly journals Birds Sound Classification Based on Machine Learning Algorithms

2021 ◽  
pp. 1-11
Author(s):  
Aska E. Mehyadin ◽  
Adnan Mohsin Abdulazeez ◽  
Dathar Abas Hasan ◽  
Jwan N. Saeed
Keyword(s):  
Machine Learning ◽  
Noise Suppression ◽  
Bird Species ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Learning Technology ◽  
Species Classification ◽  
Data Set ◽  
Sound Classification ◽  
Mel Frequency Cepstral Coefficient

The bird classifier is a system that is equipped with an area machine learning technology and uses a machine learning method to store and classify bird calls. Bird species can be known by recording only the sound of the bird, which will make it easier for the system to manage. The system also provides species classification resources to allow automated species detection from observations that can teach a machine how to recognize whether or classify the species. Non-undesirable noises are filtered out of and sorted into data sets, where each sound is run via a noise suppression filter and a separate classification procedure so that the most useful data set can be easily processed. Mel-frequency cepstral coefficient (MFCC) is used and tested through different algorithms, namely Naïve Bayes, J4.8 and Multilayer perceptron (MLP), to classify bird species. J4.8 has the highest accuracy (78.40%) and is the best. Accuracy and elapsed time are (39.4 seconds).

scholarly journals Spoken words as biomarkers: using machine learning to gain insight into communication as a predictor of anxiety

2020 ◽  
Vol 27 (6) ◽  
pp. 929-933
Author(s):  
George Demiris ◽  
Kristin L Corey Magan ◽  
Debra Parker Oliver ◽  
Karla T Washington ◽  
Chad Chadwick ◽  
...  
Keyword(s):  
Machine Learning ◽  
Secondary Data ◽  
Health Indicators ◽  
Machine Learning Algorithms ◽  
Standardized Assessments ◽  
Learning Tools ◽  
Data Set ◽  
Problem Solving Therapy ◽  
Audio Communication ◽  
The Impact

Abstract Objective The goal of this study was to explore whether features of recorded and transcribed audio communication data extracted by machine learning algorithms can be used to train a classifier for anxiety. Materials and Methods We used a secondary data set generated by a clinical trial examining problem-solving therapy for hospice caregivers consisting of 140 transcripts of multiple, sequential conversations between an interviewer and a family caregiver along with standardized assessments of anxiety prior to each session; 98 of these transcripts (70%) served as the training set, holding the remaining 30% of the data for evaluation. Results A classifier for anxiety was developed relying on language-based features. An 86% precision, 78% recall, 81% accuracy, and 84% specificity were achieved with the use of the trained classifiers. High anxiety inflections were found among recently bereaved caregivers and were usually connected to issues related to transitioning out of the caregiving role. This analysis highlighted the impact of lowering anxiety by increasing reciprocity between interviewers and caregivers. Conclusion Verbal communication can provide a platform for machine learning tools to highlight and predict behavioral health indicators and trends.

scholarly journals 1275 Machine Learning for Outcome Prediction in Epilepsy Surgery: A Systematic Review

2021 ◽  
Vol 108 (Supplement_6) ◽  
Author(s):  
C W L Chia ◽  
S Bhatia ◽  
D Shastin ◽  
M Chamberland
Keyword(s):  
Machine Learning ◽  
Epilepsy Surgery ◽  
Prediction Accuracy ◽  
Outcome Prediction ◽  
Clinical Decision Making ◽  
Small Sample ◽  
Support Vector ◽  
Seizure Freedom ◽  
Good Prediction ◽  
Good Prediction Accuracy

Abstract Aim A third of epilepsy patients suffer from medically refractory seizures. In patients eligible for surgical treatment, seizure freedom rates remain variable. Machine learning (ML) utilises large datasets to detect patterns to make predictions. We systematically review studies employing ML models for prediction of outcome following resective epilepsy surgery to evaluate their efficacy, applicability and value in determining surgical candidacy. Method MEDLINE, Cochrane and EMBASE databases were searched for literature published between 2010 – 2020 according to PRISMA guidance. Non-refractory epilepsy, non-clinical outcome prediction, or non-human studies were excluded. Clinical and demographic data, ML features, discrimination and prediction accuracy metrics were extracted. Results 15 studies were included. Median cohort size was 49 (range 16 – 4211). Heterogeneous input data sources were utilised: MRI (n = 10) , electrophysiology (n = 4), PET (n = 2), clinical data (n = 2), and neuropsychological testing (n = 1). The most common ML model used was support vector machines (n = 7). All studies had good discrimination (AUC > 0.70, range: 0.79 [95% CI NR] - 0.94 [95% CI 0.92 – 0.96]), and good prediction accuracy (> 0.70, range: 0.76 [95% CI NR] – 0.95 [95% CI NR]). Limitations included small sample sizes, limited external validation and lack of comparison with clinician-predicted outcomes. Conclusions Machine Learning for outcome prediction could enhance clinical decision-making for surgical candidacy in epilepsy, and lead to improved precision medicine delivery. Outcome reporting remains inconsistent, and further work is required to externally validate such models to implement these to large-scale clinical populations.

Exploration and Application of Machine Learning Technology Based on Swarm Intelligence Optimization

2019 ◽  
Vol 13 ◽  
Author(s):  
Rui-rui Cai
Keyword(s):  
Machine Learning ◽  
Swarm Intelligence ◽  
Research Result ◽  
Machine Learning Algorithms ◽  
Learning Technology ◽  
Data Set ◽  
Rice Pests ◽  
Machine Leaning ◽  
Smart Agriculture ◽  
Swarm Intelligence Optimization

Background: In the agriculture development and growth, the intelligent machinery and equipment plays an important role. Various researchers are involved for implementing the research and patents to aid the smart agriculture and author reviewer that machine leaning technologies are providing the best support for this growth. Method: To explore machine learning technology and machine learning algorithms, mostly based on swarm intelligence optimization, and their applications are studied. An optimized V3CFOA-RF model is built through V3CFOA. The algorithm is tested in the data set collected concerning rice pests, later analysed and compared in detail with other existing algorithms. Results: The research result shows that the model and algorithm proposed are not only more accurate in recognition and prediction, but also solve the time lagging problem to a degree. Conclusion: The model and algorithm helped realise a higher accuracy in crop pest prediction, which ensures a more stable and higher output of rice. Thus they can be employed as an important decision-making instrument in the agricultural production sector.

scholarly journals Constructing a System for Analysis of Machine Learning Techniques for Early Detection of Thyroid

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1978-1981
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Strategies ◽  
Clinical Decision Making ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Classification Techniques ◽  
Safe Condition

Thyroid is an unending and complex infection caused by unedifying levels of TSH (Thyroid Simulation Hormone) or by thyroid organ problems themselves. Hashimoto's thyroid is the most widely recognized cause of hypothyroidism. The body makes anticorps that pulverize the thyroid organ in an auto-safe condition. It offers machine learning algorithms in the system proposed to predict thyroid disease in disease-intensive societies effectively. This is a serious concern for public health even though it is massively increasing in many countries. This shows that the problem must be predicted as urgently as possible to overcome the shortcomings of previously existing clinical decision-making tools with low precision. This paper examines numerous machine learning strategies for osteoporosis prediction. The paper examines and assesses the use of the strategy of feature selection combined with classification techniques. WEKA's classification techniques are used to measure an osteoporosis data set. The results are calculated by means of various test options, including 10-fold cross-validation, training sets and the percentage divided with and without the selection method. The results are compared with correctly classified instances, runtime, kappa and absolute mean values for experiments with and without feature selection techniques.

scholarly journals Automobile tire life prediction based on image processing and machine learning technology

2021 ◽  
Vol 13 (3) ◽  
pp. 168781402110027
Author(s):  
Jianchen Zhu ◽  
Kaixin Han ◽  
Shenlong Wang
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Life Prediction ◽  
Traffic Accidents ◽  
Confusion Matrix ◽  
Small Sample ◽  
Image Feature ◽  
Engineering Practice ◽  
Learning Technology ◽  
K Nearest Neighbor

With economic growth, automobiles have become an irreplaceable means of transportation and travel. Tires are important parts of automobiles, and their wear causes a large number of traffic accidents. Therefore, predicting tire life has become one of the key factors determining vehicle safety. This paper presents a tire life prediction method based on image processing and machine learning. We first build an original image database as the initial sample. Since there are usually only a few sample image libraries in engineering practice, we propose a new image feature extraction and expression method that shows excellent performance for a small sample database. We extract the texture features of the tire image by using the gray-gradient co-occurrence matrix (GGCM) and the Gauss-Markov random field (GMRF), and classify the extracted features by using the K-nearest neighbor (KNN) classifier. We then conduct experiments and predict the wear life of automobile tires. The experimental results are estimated by using the mean average precision (MAP) and confusion matrix as evaluation criteria. Finally, we verify the effectiveness and accuracy of the proposed method for predicting tire life. The obtained results are expected to be used for real-time prediction of tire life, thereby reducing tire-related traffic accidents.

scholarly journals Clinician checklist for assessing suitability of machine learning applications in healthcare

2021 ◽  
Vol 28 (1) ◽  
pp. e100251
Author(s):  
Ian Scott ◽  
Stacey Carter ◽  
Enrico Coiera
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Routine Care ◽  
Machine Learning Algorithms ◽  
Clinical Settings ◽  
Machine Learning Applications ◽  
Key Issues

Machine learning algorithms are being used to screen and diagnose disease, prognosticate and predict therapeutic responses. Hundreds of new algorithms are being developed, but whether they improve clinical decision making and patient outcomes remains uncertain. If clinicians are to use algorithms, they need to be reassured that key issues relating to their validity, utility, feasibility, safety and ethical use have been addressed. We propose a checklist of 10 questions that clinicians can ask of those advocating for the use of a particular algorithm, but which do not expect clinicians, as non-experts, to demonstrate mastery over what can be highly complex statistical and computational concepts. The questions are: (1) What is the purpose and context of the algorithm? (2) How good were the data used to train the algorithm? (3) Were there sufficient data to train the algorithm? (4) How well does the algorithm perform? (5) Is the algorithm transferable to new clinical settings? (6) Are the outputs of the algorithm clinically intelligible? (7) How will this algorithm fit into and complement current workflows? (8) Has use of the algorithm been shown to improve patient care and outcomes? (9) Could the algorithm cause patient harm? and (10) Does use of the algorithm raise ethical, legal or social concerns? We provide examples where an algorithm may raise concerns and apply the checklist to a recent review of diagnostic imaging applications. This checklist aims to assist clinicians in assessing algorithm readiness for routine care and identify situations where further refinement and evaluation is required prior to large-scale use.

scholarly journals Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Imogen Schofield ◽  
David C. Brodbelt ◽  
Noel Kennedy ◽  
Stijn J. M. Niessen ◽  
David B. Church ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cushing’S Syndrome ◽  
Clinical Decision Making ◽  
Predictive Performance ◽  
Clinical Decision ◽  
Cushing's Syndrome ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Clinical Records

AbstractCushing’s syndrome is an endocrine disease in dogs that negatively impacts upon the quality-of-life of affected animals. Cushing’s syndrome can be a challenging diagnosis to confirm, therefore new methods to aid diagnosis are warranted. Four machine-learning algorithms were applied to predict a future diagnosis of Cushing's syndrome, using structured clinical data from the VetCompass programme in the UK. Dogs suspected of having Cushing's syndrome were included in the analysis and classified based on their final reported diagnosis within their clinical records. Demographic and clinical features available at the point of first suspicion by the attending veterinarian were included within the models. The machine-learning methods were able to classify the recorded Cushing’s syndrome diagnoses, with good predictive performance. The LASSO penalised regression model indicated the best overall performance when applied to the test set with an AUROC = 0.85 (95% CI 0.80–0.89), sensitivity = 0.71, specificity = 0.82, PPV = 0.75 and NPV = 0.78. The findings of our study indicate that machine-learning methods could predict the future diagnosis of a practicing veterinarian. New approaches using these methods could support clinical decision-making and contribute to improved diagnosis of Cushing’s syndrome in dogs.

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