scholarly journals Machine Learning for Structured Clinical Data

2017 ◽  
pp. 35-51 ◽  
Author(s):  
Brett Beaulieu-Jones
Keyword(s):  
Machine Learning ◽  
Clinical Data

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 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

scholarly journals Recommendations for Reporting Machine Learning Analyses in Clinical Research

2020 ◽  
Vol 13 (10) ◽  
Author(s):  
Laura M. Stevens ◽  
Bobak J. Mortazavi ◽  
Rahul C. Deo ◽  
Lesley Curtis ◽  
David P. Kao
Keyword(s):  
Machine Learning ◽  
Clinical Research ◽  
Clinical Experience ◽  
Clinical Data ◽  
Critical Evaluation ◽  
Predictive Performance ◽  
Structured Reporting ◽  
Data Sets ◽  
Overwhelming Evidence ◽  
Peer Reviewers

Use of machine learning (ML) in clinical research is growing steadily given the increasing availability of complex clinical data sets. ML presents important advantages in terms of predictive performance and identifying undiscovered subpopulations of patients with specific physiology and prognoses. Despite this popularity, many clinicians and researchers are not yet familiar with evaluating and interpreting ML analyses. Consequently, readers and peer-reviewers alike may either overestimate or underestimate the validity and credibility of an ML-based model. Conversely, ML experts without clinical experience may present details of the analysis that are too granular for a clinical readership to assess. Overwhelming evidence has shown poor reproducibility and reporting of ML models in clinical research suggesting the need for ML analyses to be presented in a clear, concise, and comprehensible manner to facilitate understanding and critical evaluation. We present a recommendation for transparent and structured reporting of ML analysis results specifically directed at clinical researchers. Furthermore, we provide a list of key reporting elements with examples that can be used as a template when preparing and submitting ML-based manuscripts for the same audience.

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