A Cluster-Based Machine Learning Model for Large Healthcare Data Analysis

2019 ◽  
pp. 92-106
Author(s):  
Fatemeh Sharifi ◽  
Emad Mohammed ◽  
Trafford Crump ◽  
Behrouz H. Far
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Learning Model ◽  
Healthcare Data ◽  
Machine Learning Model

Real-time data analysis using a machine learning model significantly improves prediction of successful vaginal deliveries

2020 ◽  
Vol 223 (3) ◽  
pp. 437.e1-437.e15
Author(s):  
Joshua Guedalia ◽  
Michal Lipschuetz ◽  
Michal Novoselsky-Persky ◽  
Sarah M. Cohen ◽  
Amihai Rottenstreich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Real Time ◽  
Learning Model ◽  
Time Data ◽  
Machine Learning Model ◽  
Vaginal Deliveries ◽  
Real Time Data

scholarly journals Text and Data Formatting for Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 2756-2760
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Data Preprocessing ◽  
Learning Model ◽  
Poor Quality ◽  
Big Data Analysis ◽  
Model Quality ◽  
Machine Learning Model ◽  
Learning Analysis

Machine learning is a prominent tool for getting data from large amounts of information. Whereas a good amount of machine learning analysis has targeted on increasing the accuracy and potency of coaching and reasoning algorithms, there is less attention within the equally vital issues of observing the standard of information fed into the machine learning model. The standard of huge information is far away from good. Recent studies have shown that poor quality will bring serious errors to the result of big data analysis and this could have an effect on in making additional precise results from the information. Advantages of data preprocessing within the context of ML are advanced detection of errors, model-quality improves by the usage of better data, savings in engineering hours to debug issues

Optimal Fuzzy Cluster Partitioning by Crow Search Meta-Heuristic for Biomedical Data Analysis

2021 ◽  
Vol 12 (2) ◽  
pp. 49-66
Author(s):  
Janmenjoy Nayak ◽  
Bighnaraj Naik ◽  
Pandit Byomakesha Dash ◽  
Danilo Pelusi
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Data Processing ◽  
Biomedical Informatics ◽  
Learning Model ◽  
Biomedical Data ◽  
Fuzzy C Means ◽  
Biomedical Data Analysis ◽  
Machine Learning Model ◽  
Efficient Machine

Biomedical data is often more unstructured in nature, and biomedical data processing task is becoming more complex day by day. Thus, biomedical informatics requires competent data analysis and data mining techniques for designing decision support system's framework to solve clinical and heathcare-related issues. Due to increasingly large and complex data sets and demand of biomedical informatics research, researchers are attracted towards automated machine learning models. This paper is proposed to design an efficient machine learning model based on fuzzy c-means with meta-heuristic optimizations for biomedical data analysis and clustering. The main contributions of this paper are 1) projecting an efficient machine learning model based on fuzzy c-means and meta-heuristic optimization for biomedical data classification, 2) employing benchmark validation techniques and critical hypothesises testing, and 3) providing a background for biomedical data processing with a view of data processing and mining.

scholarly journals Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis

10.2196/23943 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e23943 ◽  
Author(s):  
Roschelle L Fritz ◽  
Marian Wilson ◽  
Gordana Dermody ◽  
Maureen Schmitter-Edgecombe ◽  
Diane J Cook
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Random Forest ◽  
Smart Home ◽  
Secondary Data ◽  
Learning Model ◽  
Secondary Data Analysis ◽  
Test Model ◽  
Multiple Methods ◽  
Machine Learning Model

Background Poorly managed pain can lead to substance use disorders, depression, suicide, worsening health, and increased use of health services. Most pain assessments occur in clinical settings away from patients’ natural environments. Advances in smart home technology may allow observation of pain in the home setting. Smart homes recognizing human behaviors may be useful for quantifying functional pain interference, thereby creating new ways of assessing pain and supporting people living with pain. Objective This study aimed to determine if a smart home can detect pain-related behaviors to perform automated assessment and support intervention for persons with chronic pain. Methods A multiple methods, secondary data analysis was conducted using historic ambient sensor data and weekly nursing assessment data from 11 independent older adults reporting pain across 1-2 years of smart home monitoring. A qualitative approach was used to interpret sensor-based data of 27 unique pain events to support clinician-guided training of a machine learning model. A periodogram was used to calculate circadian rhythm strength, and a random forest containing 100 trees was employed to train a machine learning model to recognize pain-related behaviors. The model extracted 550 behavioral markers for each sensor-based data segment. These were treated as both a binary classification problem (event, control) and a regression problem. Results We found 13 clinically relevant behaviors, revealing 6 pain-related behavioral qualitative themes. Quantitative results were classified using a clinician-guided random forest technique that yielded a classification accuracy of 0.70, sensitivity of 0.72, specificity of 0.69, area under the receiver operating characteristic curve of 0.756, and area under the precision-recall curve of 0.777 in comparison to using standard anomaly detection techniques without clinician guidance (0.16 accuracy achieved; P<.001). The regression formulation achieved moderate correlation, with r=0.42. Conclusions Findings of this secondary data analysis reveal that a pain-assessing smart home may recognize pain-related behaviors. Utilizing clinicians’ real-world knowledge when developing pain-assessing machine learning models improves the model’s performance. A larger study focusing on pain-related behaviors is warranted to improve and test model performance.

scholarly journals A Framework for using Machine Learning to Support Qualitative Data Coding

2021 ◽  
Author(s):  
Peter Baumgartner ◽  
Amanda Smith ◽  
Murrey Olmsted ◽  
Dawn Ohse
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Transfer Learning ◽  
Qualitative Data ◽  
Learning Model ◽  
Language Models ◽  
Qualitative Data Analysis ◽  
Data Coding ◽  
Model Following ◽  
Machine Learning Model

Open-ended survey questions provide qualitative data that are useful for a multitude of reasons. However, qualitative data analysis is labor intensive, and researchers often lack the needed time and resources resulting in underutilization of qualitative data. In attempting to address these issues, we looked to machine learning and recent advances in language models and transfer learning to assist in qualitative coding of responses. We trained a machine learning model following the BERT architecture to predict thematic codes that were then adjudicated by human coders. Results suggest this is a promising approach that can be used to support traditional coding methods and has the potential to alleviate some of the burden associated with qualitative data analysis.

scholarly journals Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis (Preprint)

2020 ◽  
Author(s):  
Roschelle L Fritz ◽  
Marian Wilson ◽  
Gordana Dermody ◽  
Maureen Schmitter-Edgecombe ◽  
Diane J Cook
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Random Forest ◽  
Smart Home ◽  
Secondary Data ◽  
Learning Model ◽  
Secondary Data Analysis ◽  
Test Model ◽  
Multiple Methods ◽  
Machine Learning Model

BACKGROUND Poorly managed pain can lead to substance use disorders, depression, suicide, worsening health, and increased use of health services. Most pain assessments occur in clinical settings away from patients’ natural environments. Advances in smart home technology may allow observation of pain in the home setting. Smart homes recognizing human behaviors may be useful for quantifying functional pain interference, thereby creating new ways of assessing pain and supporting people living with pain. OBJECTIVE This study aimed to determine if a smart home can detect pain-related behaviors to perform automated assessment and support intervention for persons with chronic pain. METHODS A multiple methods, secondary data analysis was conducted using historic ambient sensor data and weekly nursing assessment data from 11 independent older adults reporting pain across 1-2 years of smart home monitoring. A qualitative approach was used to interpret sensor-based data of 27 unique pain events to support clinician-guided training of a machine learning model. A periodogram was used to calculate circadian rhythm strength, and a random forest containing 100 trees was employed to train a machine learning model to recognize pain-related behaviors. The model extracted 550 behavioral markers for each sensor-based data segment. These were treated as both a binary classification problem (event, control) and a regression problem. RESULTS We found 13 clinically relevant behaviors, revealing 6 pain-related behavioral qualitative themes. Quantitative results were classified using a clinician-guided random forest technique that yielded a classification accuracy of 0.70, sensitivity of 0.72, specificity of 0.69, area under the receiver operating characteristic curve of 0.756, and area under the precision-recall curve of 0.777 in comparison to using standard anomaly detection techniques without clinician guidance (0.16 accuracy achieved; <i>P</i>&lt;.001). The regression formulation achieved moderate correlation, with <i>r</i>=0.42. CONCLUSIONS Findings of this secondary data analysis reveal that a pain-assessing smart home may recognize pain-related behaviors. Utilizing clinicians’ real-world knowledge when developing pain-assessing machine learning models improves the model’s performance. A larger study focusing on pain-related behaviors is warranted to improve and test model performance.

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