Applications of Machine Learning in Disease Pre-screening

2019 ◽  
pp. 278-320 ◽  
Author(s):  
Upendra Kumar
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Classification Accuracy ◽  
Medical Information ◽  
Disease Risk ◽  
Learning Technology ◽  
Crucial Step ◽  
Computer Aided ◽  
Applications Of Machine Learning ◽  
Screening And Diagnosis

Computers in disease prescreening are utilized to interpret medical information. This is known as computer-aided pre-screening tool (CAPST). CAPST helps in improving the accuracy of diagnosis in medicine. The medical experts usually take the outcome of the CAPST as a second opinion to make the final diagnostic decisions. Fast and accurate prediction of disease risk and diagnosis is crucial step for the successful treatment of an individual. The AI-based machine learning technology has undergone significant developments over the past few years and is successfully used in many intelligent applications covering problems of variety of domains. One of the most stimulating questions is whether these techniques can be successfully applied to medicine in disease pre-screening and diagnosis and what kind of data it requires to be trained and learned. There are so many real-time examples of the problems where machine learning methods are applied successfully, especially in medicine. Many of them showed significant improvement in classification accuracy.

Applications of Machine Learning in Disease Pre-screening

2021 ◽  
pp. 1052-1084
Author(s):  
Upendra Kumar
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Classification Accuracy ◽  
Medical Information ◽  
Disease Risk ◽  
Learning Technology ◽  
Crucial Step ◽  
Computer Aided ◽  
Applications Of Machine Learning ◽  
Screening And Diagnosis

Computers in disease prescreening are utilized to interpret medical information. This is known as computer-aided pre-screening tool (CAPST). CAPST helps in improving the accuracy of diagnosis in medicine. The medical experts usually take the outcome of the CAPST as a second opinion to make the final diagnostic decisions. Fast and accurate prediction of disease risk and diagnosis is crucial step for the successful treatment of an individual. The AI-based machine learning technology has undergone significant developments over the past few years and is successfully used in many intelligent applications covering problems of variety of domains. One of the most stimulating questions is whether these techniques can be successfully applied to medicine in disease pre-screening and diagnosis and what kind of data it requires to be trained and learned. There are so many real-time examples of the problems where machine learning methods are applied successfully, especially in medicine. Many of them showed significant improvement in classification accuracy.

Exploring the Applications of Machine Learning in Healthcare

2020 ◽  
Vol 10 (4) ◽  
pp. 458-472
Author(s):  
Tausifa Jan Saleem ◽  
Mohammad Ahsan Chishti
Keyword(s):  
Machine Learning ◽  
Disease Risk ◽  
Disease Diagnosis ◽  
Machine Intelligence ◽  
Healthcare Applications ◽  
Comprehensive Overview ◽  
Machine Learning Applications ◽  
Remote Healthcare ◽  
Healthcare Monitoring ◽  
Applications Of Machine Learning

The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk prediction, (c) health monitoring, (d) healthcare related discoveries, and (e) epidemic outbreak prediction. The objective of the research is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.

Artificially Intelligent Physiotherapy

2021 ◽  
Vol 10 (1) ◽  
pp. 77-88
Author(s):  
Sachin Pandurang Godse ◽  
Shalini Singh ◽  
Sonal Khule ◽  
Shubham Chandrakant Wakhare ◽  
Vedant Yadav
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Range Of Motion ◽  
Real Time ◽  
Severe Pain ◽  
User Performance ◽  
Computer Aided ◽  
To Come ◽  
And Performance ◽  
At Home

Physiotherapy is the trending medication for curing bone-related injuries and pain. In many cases, due to sudden jerks or accidents, the patient might suffer from severe pain. Therefore, it is the miracle medication for curing patients. The aim here is to build a framework using artificial intelligence and machine learning for providing patients with a digitalized system for physiotherapy. Even though various computer-aided assessment of physiotherapy rehabilitation exist, recent approaches for computer-aided monitoring and performance lack versatility and robustness. In the authors' approach is to come up with proposition of an application which will record patient physiotherapy exercises and also provide personalized advice based on user performance for refinement of therapy. By using OpenPose Library, the system will detect angle between the joints, and depending upon the range of motion, it will guide patients in accomplishing physiotherapy at home. It will also suggest to patients different physio-exercises. With the help of OpenPose, it is possible to render patient images or real-time video.

Leveraging on Machine Learning Solution for Pioneering Wells Augmented Stuck Pipe Indicator in Real Time Centre

2021 ◽  
Author(s):  
Mohamad Hazwan Yusoff ◽  
Meor Muhammad Hakeem Meor Hashim ◽  
Muhammad Hadi Hamzah ◽  
Muhammad Faris Arriffin ◽  
Azlan Mohamad
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Detection System ◽  
Automation System ◽  
Learning Technology ◽  
Drilling Parameters ◽  
Bottom Hole Assembly ◽  
Signature Pattern ◽  
Monitoring Software ◽  
Productive Time

Abstract Stuck pipe incidents remain as one of the major problems in the drilling industry. The incidents will lead to expensive loss time in daily spread cost, bottom hole assembly cost, sidetracking cost as well as fishing cost. The Wells Augmented Stuck Pipe (WASP) Indicator, a state-of-the-art machine learning technology that seamlessly integrates with PETRONAS existing technologies, is introduced as the stuck pipe prevention detection system for the company. Historical real-time drilling data and stuck pipe incidents reports between 2007 and 2019 are used for the development of machine learning models. The models utilize key drilling parameters such as hookload and equivalent circulating density (ECD) to predict and analyze trends to detect any signature pattern anomalies for various stuck pipe events. The prediction and alarm are displayed in real-time monitoring software to trigger the operation team for prompt intervention. The WASP solution has demonstrated proven outcomes using historical and live well with high confidence in detecting stuck pipe incidents due to differential sticking, hole cleaning, and wellbore geometry. The WASP Indicator is envisaged to provide the company with cutting edge advantages in the industry. It is expected that the system will reduce the identification period and improve the reaction time of the monitoring specialists in recognizing the stuck pipe symptoms and highlighting potential incidents. The system is also bringing value to the company via non-productive time (NPT) cost avoidance and identification of early onset of various stuck pipe events based on distinct mechanisms. With the system, the existing portfolio value can be enhanced via setting dynamic trends and models into historical experiences context. The WASP Indicator is aspired to be the forefront innovation that will leap through the norm and lead the region in a greater plan of drilling automation system.

scholarly journals Coupling machine learning and high throughput multiplex digital PCR enables accurate detection of carbapenem-resistant genes in clinical isolates

2021 ◽  
Author(s):  
Luca Miglietta ◽  
Ahmad Moniri ◽  
Ivana Pennisi ◽  
Kenny Malpartida Cardenas ◽  
Hala Abbas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
High Throughput ◽  
Classification Accuracy ◽  
Melting Curve ◽  
Digital Pcr ◽  
Curve Analysis ◽  
Melting Curve Analysis ◽  
Carbapenem Resistant ◽  
Resistant Genes

Background: The emergence and spread of carbapenemase-producing organisms (CPO) are a significant clinical and public health concern. Rapid and accurate identification of patients colonised with CPO is essential to adopt prompt prevention measures in order to reduce the risk of transmission. Recent proof-of-concept studies have demonstrated the ability to combine machine learning (ML) algorithms with real-time digital PCR (dPCR) instruments to increase classification accuracy of multiplex assays. From this, we sought to determine if this ML based methodology could accurately identify five major carbapenem-resistant genes in clinical CPO-isolates. Methods: We collected 253 clinical isolates (including 221 CPO-positive samples) and developed a novel 5-plex assay for detection of blaVIM, blaOXA-48, blaNDM, blaIMP and blaKPC. Combining the recently reported ML method "Amplification and Melting Curve Analysis" (AMCA) with the abovementioned multiplex assay, we assessed the performance of the methodology in detecting these five carbapenem-resistant genes. The classification accuracy relies on the usage of real-time data from a single fluorescent channel and benefits from the kinetic and thermodynamic information encoded in the thousands of amplification events produced by high throughput dPCR. Results: The 5-plex showed a lower limit of detection of 100 DNA copies per reaction for each primer set and no cross-reactivity with other carbapenemase genes. The AMCA classifier demonstrated excellent predictive performance with 99.6% (CI 97.8-99.9%) accuracy (only one misclassified sample out of the 253, with a total of 163,966 positive amplification events), which represents a 7.9% increase compared to the conventional ML-based melting curve analysis (MCA) method. Conclusion: This work demonstrates the utility of the AMCA method to increase the throughput and performance of state-of-the-art molecular diagnostic platforms, reducing costs without any changes to instrument hardware. Our findings suggest that, pending additional validation directly from clinical samples, advanced data-driven multiplex dPCR could potentially be integrated in routine clinical diagnostic workflows.

scholarly journals Coupling Machine Learning and High Throughput Multiplex Digital PCR Enables Accurate Detection of Carbapenem-Resistant Genes in Clinical Isolates

2021 ◽  
Vol 8 ◽  
Author(s):  
Luca Miglietta ◽  
Ahmad Moniri ◽  
Ivana Pennisi ◽  
Kenny Malpartida-Cardenas ◽  
Hala Abbas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
High Throughput ◽  
Classification Accuracy ◽  
Melting Curve ◽  
Digital Pcr ◽  
Curve Analysis ◽  
Melting Curve Analysis ◽  
Carbapenem Resistant ◽  
Resistant Genes

Rapid and accurate identification of patients colonised with carbapenemase-producing organisms (CPOs) is essential to adopt prompt prevention measures to reduce the risk of transmission. Recent studies have demonstrated the ability to combine machine learning (ML) algorithms with real-time digital PCR (dPCR) instruments to increase classification accuracy of multiplex PCR assays when using synthetic DNA templates. We sought to determine if this novel methodology could be applied to improve identification of the five major carbapenem-resistant genes in clinical CPO-isolates, which would represent a leap forward in the use of PCR-based data-driven diagnostics for clinical applications. We collected 253 clinical isolates (including 221 CPO-positive samples) and developed a novel 5-plex PCR assay for detection of blaIMP, blaKPC, blaNDM, blaOXA-48, and blaVIM. Combining the recently reported ML method “Amplification and Melting Curve Analysis” (AMCA) with the abovementioned multiplex assay, we assessed the performance of the AMCA methodology in detecting these genes. The improved classification accuracy of AMCA relies on the usage of real-time data from a single-fluorescent channel and benefits from the kinetic/thermodynamic information encoded in the thousands of amplification events produced by high throughput real-time dPCR. The 5-plex showed a lower limit of detection of 10 DNA copies per reaction for each primer set and no cross-reactivity with other carbapenemase genes. The AMCA classifier demonstrated excellent predictive performance with 99.6% (CI 97.8–99.9%) accuracy (only one misclassified sample out of the 253, with a total of 160,041 positive amplification events), which represents a 7.9% increase (p-value <0.05) compared to conventional melting curve analysis. This work demonstrates the use of the AMCA method to increase the throughput and performance of state-of-the-art molecular diagnostic platforms, without hardware modifications and additional costs, thus potentially providing substantial clinical utility on screening patients for CPO carriage.

scholarly journals Real Time Efficient Accident Predictor System using Machine Learning Techniques (kNN, RF, LR, DT)

2020 ◽  
Vol 10 (2) ◽  
pp. 108-111
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Real Time ◽  
Classification Accuracy ◽  
Nearest Neighbors ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Classification Methods ◽  
K Nearest Neighbors ◽  
Learning Techniques

Real time crash predictor system is determining frequency of crashes and also severity of crashes. Nowadays machine learning based methods are used to predict the total number of crashes. In this project, prediction accuracy of machine learning algorithms like Decision tree (DT), K-nearest neighbors (KNN), Random forest (RF), Logistic Regression (LR) are evaluated. Performance analysis of these classification methods are evaluated in terms of accuracy. Dataset included for this project is obtained from 49 states of US and 27 states of India which contains 2.25 million US accident crash records and 1.16 million crash records respectively. Results prove that classification accuracy obtained from Random Forest (RF) is96% compared to other classification methods.

Applications of Machine Learning in Real-Time Tumor Localization

2012 ◽  
pp. 419-431
Author(s):  
Ruijiang Li ◽  
Steve B. Jiang
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Tumor Localization ◽  
Linear Discriminant ◽  
Learning Techniques ◽  
Domain Specific Knowledge ◽  
Applications Of Machine Learning

Recently, machine learning has gained great popularity in many aspects of radiation therapy. In this chapter, the authors will demonstrate the applications of various machine learning techniques in the context of real-time tumor localization in lung cancer radiotherapy. These cover a wide range of well established machine learning techniques, including principal component analysis, linear discriminant analysis, artificial neural networks, and support vector machine, etc. Respiratory gating, as a special case of tumor localization, will also be discussed. The chapter will demonstrate how domain specific knowledge and prior information can be useful in achieving more accurate and robust tumor localization. Future research directions in machine learning that can further improve the accuracy for tumor localization are also discussed.

scholarly journals Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Qinghua Huang ◽  
Fan Zhang ◽  
Xuelong Li
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Performance Improvement ◽  
Ultrasound Imaging ◽  
Low Cost ◽  
Learning Technology ◽  
Diagnostic Systems ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system.

scholarly journals Classification of Liver Patient Dataset Using Machine Learning Algorithms

2018 ◽  
Vol 7 (3.34) ◽  
pp. 323
Author(s):  
S Muthuselvan ◽  
S Rajapraksh ◽  
K Somasundaram ◽  
K Karthik
Keyword(s):  
Machine Learning ◽  
Medical Information ◽  
Learning Algorithms ◽  
Medical Industry ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
Successful Implementation ◽  
Computer Aided ◽  
Aided Diagnosis

Prediction of the disease in the human being is the very long and difficult process in early days. Now a days, computer aided diagnosis is the important role in the medical industry for predicting, analyzing and storing medical information with the images. In this paper will discuss and classify the liver patients with the help of the liver patient dataset with the help of the machine learning algorithms. WEKA is the software used here for implement the some of the classification algorithms with the data selected from the liver disease dataset. After the successful implementation of the all the algorithms, the best algorithms selected from the output of the all the algorithms execution. 

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