scholarly journals Comparison of RCF scoring system to clinical decision for the Rey complex figure using machine‐learning algorithm

2021 ◽  
Vol 17 (S6) ◽  
Author(s):  
Chanda Simfukwe ◽  
Young Chul Youn ◽  
Seong Soo An
Keyword(s):  
Machine Learning ◽  
Scoring System ◽  
Learning Algorithm ◽  
Clinical Decision ◽  
Machine Learning Algorithm ◽  
Complex Figure ◽  
Rey Complex Figure

scholarly journals Perinatal Hypoxia Diagnostic System by using Scalable Machine Learning Algorithms

2019 ◽  
Vol 8 (12) ◽  
pp. 1954-1959
Keyword(s):  
Machine Learning ◽  
Clinical Decision Making ◽  
Learning Algorithm ◽  
Diagnostic System ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Learning Approaches ◽  
Perinatal Hypoxia ◽  
Fast Computation

Collaborating big data and machine learning approaches in healthcare can help in improving clinical decision making and treatment by identifying and accumulating accurate features. Prenatal hypoxia can also be identified by cardiotocography (CTG) monitoring that helps in identifying the condition of the fetus. Imposing the data over distributed approaches can help in fast computation to rate the fetal and mother wellbeing before delivery. Our research aims to propose and implement a scalable Machine learning Algorithm based perinatal Hypoxia diagnostic system for larger datasets. This system was implemented on the CTG dataset using python and pyspark models like SVM, Random Forest, and Logistic regression. In the proposed method experiment results contributing to spark RF are more accurate than other techniques and achieved the precision of 0.97, recall of 0.99, f-1 score of 0. 98, AUC of 0.97 and gained 97% accuracy

scholarly journals An approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abha Umesh Sardesai ◽  
Ambalika Sanjeev Tanak ◽  
Subramaniam Krishnan ◽  
Deborah A. Striegel ◽  
Kevin L. Schully ◽  
...  
Keyword(s):  
Machine Learning ◽  
Immune Response ◽  
Clinical Decision Making ◽  
Learning Algorithm ◽  
Clinical Decision ◽  
Host Immune Response ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Patient Stratification ◽  
Sepsis Management

AbstractSepsis is a life-threatening condition and understanding the disease pathophysiology through the use of host immune response biomarkers is critical for patient stratification. Lack of accurate sepsis endotyping impedes clinicians from making timely decisions alongside insufficiencies in appropriate sepsis management. This work aims to demonstrate the potential feasibility of a data-driven validation model for supporting clinical decisions to predict sepsis host-immune response. Herein, we used a machine learning approach to determine the predictive potential of identifying sepsis host immune response for patient stratification by combining multiple biomarker measurements from a single plasma sample. Results were obtained using the following cytokines and chemokines IL-6, IL-8, IL-10, IP-10 and TRAIL where the test dataset was 70%. Supervised machine learning algorithm naïve Bayes and decision tree algorithm showed good accuracy of 96.64% and 94.64%. These promising findings indicate the proposed AI approach could be a valuable testing resource for promoting clinical decision making.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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