scholarly journals A SURVEY OF ADVANCED DEEP MACHINE LEARNING TECHNIQUES IN EMERGENCY HEALTH CARE USING MEDICAL HEALTH RECORDS

2021 ◽  
Vol 10 (11 (SPECIAL ISSUE)) ◽  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Machine Learning Techniques ◽  
Health Records ◽  
Emergency Health Care ◽  
Learning Techniques ◽  
Medical Health

scholarly journals Intersections of machine learning and epidemiological methods for health services research

2020 ◽  
Author(s):  
Sherri Rose
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health Services Research ◽  
Health Services ◽  
Machine Learning Techniques ◽  
Epidemiological Methods ◽  
Learning Tools ◽  
Services Research ◽  
Learning Techniques ◽  
Regression Techniques

Abstract The field of health services research is broad and seeks to answer questions about the health care system. It is inherently interdisciplinary, and epidemiologists have made crucial contributions. Parametric regression techniques remain standard practice in health services research with machine learning techniques currently having low penetrance in comparison. However, studies in several prominent areas, including health care spending, outcomes and quality, have begun deploying machine learning tools for these applications. Nevertheless, major advances in epidemiological methods are also as yet underleveraged in health services research. This article summarizes the current state of machine learning in key areas of health services research, and discusses important future directions at the intersection of machine learning and epidemiological methods for health services research.

scholarly journals Differential Diagnosis of Tuberculosis and Pneumonia using Machine Learning

2019 ◽  
Vol 8 (11) ◽  
pp. 245-250
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Differential Diagnosis ◽  
Medical Diagnosis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Care Practice ◽  
Digital Revolution ◽  
Learning Techniques ◽  
Classification Prediction

Machine learning has become one of the top most emerging technologies in this era of digital revolution. The machine learning algorithms are being used in various fields and applications such as image recognition, speech recognition, classification, prediction, medical diagnosis etc. In medical domain, machine learning techniques have been successfully implemented to improve the accuracy of medical diagnosis and also to improve the efficiency and quality of health care. In this paper, we have analyzed the existing health care practice system and have proposed how machine learning techniques can be used for differential diagnosis of Tuberculosis and Pneumonia which are often misdiagnosed due to similar symptoms at early stages.

scholarly journals Machine learning algorithms: a background artifact

2017 ◽  
Vol 7 (1.1) ◽  
pp. 143 ◽  
Author(s):  
J. Deepika ◽  
T. Senthil ◽  
C. Rajan ◽  
A. Surendar
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Knowledge Discovery ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Human Intelligence ◽  
Machine Learning Algorithm ◽  
Human History ◽  
Learning Techniques

With the greater development of technology and automation human history is predominantly updated. The technology movement shifted from large mainframes to PCs to cloud when computing the available data for a larger period. This has happened only due to the advent of many tools and practices, that elevated the next generation in computing. A large number of techniques has been developed so far to automate such computing. Research dragged towards training the computers to behave similar to human intelligence. Here the diversity of machine learning came into play for knowledge discovery. Machine Learning (ML) is applied in many areas such as medical, marketing, telecommunications, and stock, health care and so on. This paper presents reviews about machine learning algorithm foundations, its types and flavors together with R code and Python scripts possibly for each machine learning techniques.  

scholarly journals Comparison of Machine Learning Techniques for Prediction of Hospitalization in Heart Failure Patients

2019 ◽  
Vol 8 (9) ◽  
pp. 1298 ◽  
Author(s):  
Giulia Lorenzoni ◽  
Stefano Santo Sabato ◽  
Corrado Lanera ◽  
Daniele Bottigliengo ◽  
Clara Minto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Health Care ◽  
Predictive Value ◽  
Machine Learning Techniques ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Heart Failure Patients ◽  
Learning Techniques ◽  
Using Data

The present study aims to compare the performance of eight Machine Learning Techniques (MLTs) in the prediction of hospitalization among patients with heart failure, using data from the Gestione Integrata dello Scompenso Cardiaco (GISC) study. The GISC project is an ongoing study that takes place in the region of Puglia, Southern Italy. Patients with a diagnosis of heart failure are enrolled in a long-term assistance program that includes the adoption of an online platform for data sharing between general practitioners and cardiologists working in hospitals and community health districts. Logistic regression, generalized linear model net (GLMN), classification and regression tree, random forest, adaboost, logitboost, support vector machine, and neural networks were applied to evaluate the feasibility of such techniques in predicting hospitalization of 380 patients enrolled in the GISC study, using data about demographic characteristics, medical history, and clinical characteristics of each patient. The MLTs were compared both without and with missing data imputation. Overall, models trained without missing data imputation showed higher predictive performances. The GLMN showed better performance in predicting hospitalization than the other MLTs, with an average accuracy, positive predictive value and negative predictive value of 81.2%, 87.5%, and 75%, respectively. Present findings suggest that MLTs may represent a promising opportunity to predict hospital admission of heart failure patients by exploiting health care information generated by the contact of such patients with the health care system.

scholarly journals Diagnosing hospital bacteraemia in the framework of predictive, preventive and personalised medicine using electronic health records and machine learning classifiers

2021 ◽  
Author(s):  
Oscar Garnica ◽  
Diego Gómez ◽  
Víctor Ramos ◽  
J. Ignacio Hidalgo ◽  
José M. Ruiz-Giardín
Keyword(s):  
Machine Learning ◽  
Blood Culture ◽  
Electronic Health Records ◽  
Antibiotic Treatment ◽  
Personalised Medicine ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Health Records ◽  
Learning Techniques ◽  
Electronic Health

Abstract Background The bacteraemia prediction is relevant because sepsis is one of the most important causes of morbidity and mortality. Bacteraemia prognosis primarily depends on a rapid diagnosis. The bacteraemia prediction would shorten up to 6 days the diagnosis, and, in conjunction with individual patient variables, should be considered to start the early administration of personalised antibiotic treatment and medical services, the election of specific diagnostic techniques and the determination of additional treatments, such as surgery, that would prevent subsequent complications. Machine learning techniques could help physicians make these informed decisions by predicting bacteraemia using the data already available in electronic hospital records. Objective This study presents the application of machine learning techniques to these records to predict the blood culture’s outcome, which would reduce the lag in starting a personalised antibiotic treatment and the medical costs associated with erroneous treatments due to conservative assumptions about blood culture outcomes. Methods Six supervised classifiers were created using three machine learning techniques, Support Vector Machine, Random Forest and K-Nearest Neighbours, on the electronic health records of hospital patients. The best approach to handle missing data was chosen and, for each machine learning technique, two classification models were created: the first uses the features known at the time of blood extraction, whereas the second uses four extra features revealed during the blood culture. Results The six classifiers were trained and tested using a dataset of 4357 patients with 117 features per patient. The models obtain predictions that, for the best case, are up to a state-of-the-art accuracy of 85.9%, a sensitivity of 87.4% and an AUC of 0.93. Conclusions Our results provide cutting-edge metrics of interest in predictive medical models with values that exceed the medical practice threshold and previous results in the literature using classical modelling techniques in specific types of bacteraemia. Additionally, the consistency of results is reasserted because the three classifiers’ importance ranking shows similar features that coincide with those that physicians use in their manual heuristics. Therefore, the efficacy of these machine learning techniques confirms their viability to assist in the aims of predictive and personalised medicine once the disease presents bacteraemia-compatible symptoms and to assist in improving the healthcare economy.

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