Fetal health status prediction based on maternal clinical history using machine learning techniques

2018 ◽  
Vol 163 ◽  
pp. 87-100 ◽  
Author(s):  
Akhan Akbulut ◽  
Egemen Ertugrul ◽  
Varol Topcu
Keyword(s):  
Machine Learning ◽  
Health Status ◽  
Clinical History ◽  
Machine Learning Techniques ◽  
Fetal Health ◽  
Learning Techniques

scholarly journals Using Machine-Learning Techniques to Identify Responders vs. Non-responders in Randomized Clinical Trials.

2020 ◽  
Author(s):  
Vasiliki Nikolodimou ◽  
Paul Agapow
Keyword(s):  
Machine Learning ◽  
Randomized Clinical Trials ◽  
Clustering Algorithms ◽  
Human Monoclonal Antibody ◽  
Clinical History ◽  
Differential Response ◽  
Unsupervised Clustering ◽  
Machine Learning Techniques ◽  
Genetic Characteristics ◽  
Learning Techniques

Despite the expectation of heterogeneity in therapy outcomes, especially for complex diseases like cancer, analyzing differential response to experimental therapies in a randomized clinical trial (RCT) setting is typically done by dividing patients into responders and non-responders, usually based on a single endpoint. Given the existence of biological and patho-physiological differences among metastatic colorectal cancer (mCRC) patients, we hypothesized that a data-driven analysis of an RCT population outcomes can identify sub-types of patients founded on differential response to Panitumumab - a fully human monoclonal antibody directed against the epidermal growth factor receptor. Outcome and response data of the RCT population were mined with heuristic, distance-based and model-based unsupervised clustering algorithms. The population sub-groups obtained by the best performing clustering approach were then examined in terms of molecular and clinical characteristics. The utility of this characterization was compared against that of the sub-groups obtained by the conventional responders' analysis and then contrasted with aetiological evidence around mCRC heterogeneity and biological functioning. The Partition around Medoids clustering method results into the identification of seven sub-types of patients, statistically distinct from each other in survival outcomes, prognostic biomarkers and genetic characteristics. Conventional responders analysis was proven inferior in uncovering relationships between physical, clinical history, genetic attributes and differential treatment resistance mechanisms. Combined with improved characterization of the molecular subtypes of CRC, applying Machine Learning techniques, like unsupervised clustering, onto the wealth of data already collected by previous RCTs can support the design of further targeted, more efficient RCTs and better identification of patient groups who will respond to a given intervention.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 300 ◽  
Author(s):  
Feidu Akmel ◽  
Ermiyas Birihanu ◽  
Bahir Siraj
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Quality Standard ◽  
Machine Learning Techniques ◽  
Software Systems ◽  
Health Care Insurance ◽  
Software Defect ◽  
Learning Techniques ◽  
Software Product

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

Sign in / Sign up

Export Citation Format

Share Document