Machine Learning in Medical Applications

2001 ◽  
pp. 300-307 ◽  
Author(s):  
George D. Magoulas ◽  
Andriana Prentza
Keyword(s):  
Machine Learning ◽  
Medical Applications

scholarly journals Investigating Presence of Ethnoracial Bias in Clinical Data using Machine Learning

2021 ◽  
Author(s):  
Bojana Velichkovska ◽  
Hristijan Gjoreski ◽  
Daniel Denkovski ◽  
Marija Kalendar ◽  
Leo Anthnoy Celi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Data ◽  
Vital Signs ◽  
Medical Applications ◽  
Demographic Information ◽  
Complex Issue ◽  
Important Target ◽  
Learning Research ◽  
Applications Of Machine Learning ◽  
Initial Base

AbstractAn important target for machine learning research is obtaining unbiased results, which require addressing bias that might be present in the data as well as the methodology. This is of utmost importance in medical applications of machine learning, where trained models should be unbiased so as to result in systems that are widely applicable, reliable and fair. Since bias can sometimes be introduced through the data itself, in this paper we investigate the presence of ethnoracial bias in patients’ clinical data. We focus primarily on vital signs and demographic information and classify patient ethnoraces in subsets of two from the three ethnoracial groups (African Americans, Caucasians, and Hispanics). Our results show that ethnorace can be identified in two out of three patients, setting the initial base for further investigation of the complex issue of ehtnoracial bias.

Deep Learning Techniques for Biomedical Image Analysis in Healthcare

2020 ◽  
pp. 31-46
Author(s):  
Sivakami A. ◽  
Balamurugan K. S. ◽  
Bagyalakshmi Shanmugam ◽  
Sudhagar Pitchaimuthu
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Image Interpretation ◽  
Medical Applications ◽  
Biomedical Image Analysis ◽  
Future Directions ◽  
Biomedical Image ◽  
Learning Techniques ◽  
Visual Problems

Biomedical image analysis is very relevant to public health and welfare. Deep learning is quickly growing and has shown enhanced performance in medical applications. It has also been widely extended in academia and industry. The utilization of various deep learning methods on medical imaging endeavours to create systems that can help in the identification of disease and the automation of interpreting biomedical images to help treatment planning. New advancements in machine learning are primarily about deep learning employed for identifying, classifying, and quantifying patterns in images in the medical field. Deep learning, a more precise convolutional neural network has given excellent performance over machine learning in solving visual problems. This chapter summarizes a review of different deep learning techniques used and how they are applied in medical image interpretation and future directions.

scholarly journals Holotomographic imaging of eukaryotic cells

2021 ◽  
Author(s):  
YoungJu Jo ◽  
Wei Sun Park ◽  
YongKeun Park
Keyword(s):  
Machine Learning ◽  
Refractive Index ◽  
Eukaryotic Cells ◽  
Medical Applications ◽  
Recent Advances ◽  
Biological And Medical Applications ◽  
In Cells

Abstract Holotomography measures 3D refractive index (RI) distribution in cells and tissues without exogenous labeling. Here we describe a protocol for holotomographic imaging of generic eukaryotic cells using a standardized Tomocube holotomographic microscope. Combined with the recent advances in machine learning, holotomographic imaging enables a broad range of new biological and medical applications.

scholarly journals Performance Evaluation Of SVM and ANN for Cancer Classification and Designing Algorithm for Arrhythmia Prediction in Cancer Patient

2018 ◽  
Vol 7 (3.3) ◽  
pp. 99
Author(s):  
Chaithanya Lakshmi ◽  
Sachin Bojewar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Cancer Patient ◽  
Medical Applications ◽  
Early Age ◽  
Health Evaluation ◽  
Data Set ◽  
Linear Classifier ◽  
Failure Assessment ◽  
Diagnosis Criteria

Classifying the cancer based on the age and predicting the arrhythmia in cancer patient is necessary to determine the next steps in dealing with the patients. This prediction can be done by using multiple algorithms of machine learning such as SVM, Linear classifier, neural network. Machine learning, interpretability refers to understand the underlying behaviour of the prediction of a model in order to identify diagnosis criteria and/or new rules from its output. Interpretability contributes to increase the usability of the method. Also, it is relevant in decision support systems, such as in medical applications. Using multiple algorithm on big data set and predicting the arrhythmia cases from early age to old age.Apache (Acute Physiology, Age and Chronic Health Evaluation) and SOFA (Sequential Organ Failure Assessment) score are the important factor in critically ill patients. The number of ICU (intensive care unit) admission will be depending on these two scores. Analyzing Apache and SOFA scores will be helpful for intensivist.[4]  

scholarly journals Machine learning for epigenetics and future medical applications

Epigenetics ◽  
2017 ◽  
Vol 12 (7) ◽  
pp. 505-514 ◽  
Author(s):  
Lawrence B. Holder ◽  
M. Muksitul Haque ◽  
Michael K. Skinner
Keyword(s):  
Machine Learning ◽  
Medical Applications

Multi-objective differential evolution based random forest for e-health applications

2019 ◽  
Vol 33 (05) ◽  
pp. 1950022 ◽  
Author(s):  
Manjit Kaur ◽  
Hemant Kumar Gianey ◽  
Dilbag Singh ◽  
Munish Sabharwal
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Differential Evolution ◽  
Parameter Tuning ◽  
Machine Learning Techniques ◽  
Medical Applications ◽  
Efficient Manner ◽  
Multi Objective ◽  
Learning Techniques ◽  
Health Applications

Many machine learning techniques have been used in past few decades for various medical applications. However, these techniques suffer from parameter tuning issue. Therefore, an efficient tuning of these parameters has an ability to improve the performance of existing machine learning techniques. Therefore, in this work, a novel multi-objective differential evolution based random forest technique is proposed. The proposed technique is able to tune the parameters of random forest in an efficient manner. Extensive experiments are carried out by considering the proposed and the existing competitive machine learning techniques on various medical applications. It is observed that the proposed technique outperforms existing techniques in terms of accuracy, f-measure, sensitivity and specificity.

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