Combining Image and Non-image Clinical Data: An Infrastructure that Allows Machine Learning Studies in a Hospital Environment

2018 ◽  
pp. 324-331
Author(s):  
Raphael Espanha ◽  
Frank Thiele ◽  
Georgy Shakirin ◽  
Jens Roggenfelder ◽  
Sascha Zeiter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Data ◽  
Hospital Environment

scholarly journals Realizing an Effective COVID-19 Diagnosis System Based on Machine Learning and IOT in Smart Hospital Environment

2021 ◽  
pp. 1-1 ◽  
Author(s):  
Karrar Hameed Abdulkareem ◽  
Mazin Abed Mohammed ◽  
Ahmad Salim ◽  
Muhammad Arif ◽  
Oana Geman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hospital Environment ◽  
Diagnosis System ◽  
Smart Hospital

scholarly journals Big Data for Health Care Analytics using Extreme Machine Learning Based on Map Reduce

2020 ◽  
Vol 9 (3) ◽  
pp. 2758-2762
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Storage ◽  
Clinical Data ◽  
Disease Risk ◽  
Learning Algorithm ◽  
Information Storage ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Data Set

A large volume of datasets is available in various fields that are stored to be somewhere which is called big data. Big Data healthcare has clinical data set of every patient records in huge amount and they are maintained by Electronic Health Records (EHR). More than 80 % of clinical data is the unstructured format and reposit in hundreds of forms. The challenges and demand for data storage, analysis is to handling large datasets in terms of efficiency and scalability. Hadoop Map reduces framework uses big data to store and operate any kinds of data speedily. It is not solely meant for storage system however conjointly a platform for information storage moreover as processing. It is scalable and fault-tolerant to the systems. Also, the prediction of the data sets is handled by machine learning algorithm. This work focuses on the Extreme Machine Learning algorithm (ELM) that can utilize the optimized way of finding a solution to find disease risk prediction by combining ELM with Cuckoo Search optimization-based Support Vector Machine (CS-SVM). The proposed work also considers the scalability and accuracy of big data models, thus the proposed algorithm greatly achieves the computing work and got good results in performance of both veracity and efficiency.

scholarly journals Machine Learning Algorithm Using Clinical Data and Demographic Data for Preterm Birth Prediction

2022 ◽  
Vol 226 (1) ◽  
pp. S362-S363
Author(s):  
Matthew Hoffman ◽  
Wei Liu ◽  
Jade Tunguhan ◽  
Ghamar Bitar ◽  
Kaveeta Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Preterm Birth ◽  
Clinical Data ◽  
Learning Algorithm ◽  
Demographic Data ◽  
Machine Learning Algorithm

scholarly journals Integration of metabolomics, lipidomics and clinical data using a machine learning method

2016 ◽  
Vol 17 (S15) ◽  
Author(s):  
Animesh Acharjee ◽  
Zsuzsanna Ament ◽  
James A. West ◽  
Elizabeth Stanley ◽  
Julian L. Griffin
Keyword(s):  
Machine Learning ◽  
Clinical Data ◽  
Machine Learning Method ◽  
Learning Method

A machine learning approach to suspect excessive inactivity in COPD patients using non-activity-related clinical data

2021 ◽  
Author(s):  
Bernard Aguilaniu ◽  
Eric Kelkel ◽  
Anne Rigal ◽  
David Hess ◽  
Amandine Briault ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Data ◽  
Learning Approach ◽  
Copd Patients ◽  
Machine Learning Approach

scholarly journals Machine Learning in Oncology: What Should Clinicians Know?

2020 ◽  
pp. 799-810
Author(s):  
Matthew Nagy ◽  
Nathan Radakovich ◽  
Aziz Nazha
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Data ◽  
Cancer Diagnosis ◽  
Health Data ◽  
Computer Processing ◽  
Research And Practice ◽  
New Methods ◽  
Oncology Research ◽  
Processing Power

The volume and complexity of scientific and clinical data in oncology have grown markedly over recent years, including but not limited to the realms of electronic health data, radiographic and histologic data, and genomics. This growth holds promise for a deeper understanding of malignancy and, accordingly, more personalized and effective oncologic care. Such goals require, however, the development of new methods to fully make use of the wealth of available data. Improvements in computer processing power and algorithm development have positioned machine learning, a branch of artificial intelligence, to play a prominent role in oncology research and practice. This review provides an overview of the basics of machine learning and highlights current progress and challenges in applying this technology to cancer diagnosis, prognosis, and treatment recommendations, including a discussion of current takeaways for clinicians.

scholarly journals Predicting Mortality with Applied Machine Learning: Can We Get There?

2019 ◽  
Vol 8 (1) ◽  
pp. 115-119
Author(s):  
Emily S. Patterson ◽  
C.J. Hansen ◽  
Theodore T. Allen ◽  
Qiwei Yang ◽  
Susan D. Moffatt-Bruce
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Real Time ◽  
Drug Administration ◽  
Patient Population ◽  
Food And Drug Administration ◽  
Hospital Environment ◽  
Clinical Settings ◽  
Applied Machine Learning

There is growing interest in using AI-based algorithms to support clinician decision-making. An important consideration is how transparent complex algorithms can be for predictions, particularly with respect to imminent mortality in a hospital environment. Understanding the basis of predictions, the process used to generate models and recommendations, how to generalize models based on one patient population to another, and the role of oversight organizations such as the Food and Drug Administration are important topics. In this paper, we debate opposing positions regarding whether these algorithms are ‘ready yet’ for use today in clinical settings for physicians, patients and caregivers. We report voting results from participating audience members in attendance at the conference debate for each of these positions obtained real-time from a smartphone-based platform.

scholarly journals Machine learning methods in predicting chemotherapy-induced neutropenia in oncology patients using clinical data

2019 ◽  
Vol 19 (Suppl 3) ◽  
pp. 89-90
Author(s):  
Alexander Holborow ◽  
Bryony Coupe ◽  
Mark Davies ◽  
Shangming Zhou
Keyword(s):  
Machine Learning ◽  
Clinical Data ◽  
Oncology Patients ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Chemotherapy Induced Neutropenia
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