scholarly journals Plasmodium Detection using Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 289-292
Keyword(s):  
Machine Learning ◽  
Health Care Providers ◽  
Contour Detection ◽  
Data Sets ◽  
Early Prediction ◽  
Care Providers ◽  
Health Organizations ◽  
Health Ministry ◽  
Machine Learning Model ◽  
Malaria Epidemic

Plasmodium is one of India's biggest public health problems. Early prediction of a malaria epidemic is that the secret to malaria morbidity management, mortality as well as reducing the risk of malaria transmission in the community will benefit politicians, health care providers, medical officers, health ministry and other health organizations to better target medical resources to areas of greatest need. In this project, we acquire data sets from hospital databases, which have the information about the causes of malaria, and the images of cells infected with malaria. We then analyze these data sets and feed them to our machine-learning model. Here we are using contour detection and random forest algorithms for training the model and predicting the output

scholarly journals Generation of geometric interpolations of building types with deep variational autoencoders

2020 ◽  
Vol 6 ◽  
Author(s):  
Jaime de Miguel Rodríguez ◽  
Maria Eugenia Villafañe ◽  
Luka Piškorec ◽  
Fernando Sancho Caparrini
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Large Data ◽  
Learning Model ◽  
Large Data Sets ◽  
Data Sets ◽  
Connectivity Map ◽  
Data Set ◽  
3D Objects ◽  
Machine Learning Model

Abstract This work presents a methodology for the generation of novel 3D objects resembling wireframes of building types. These result from the reconstruction of interpolated locations within the learnt distribution of variational autoencoders (VAEs), a deep generative machine learning model based on neural networks. The data set used features a scheme for geometry representation based on a ‘connectivity map’ that is especially suited to express the wireframe objects that compose it. Additionally, the input samples are generated through ‘parametric augmentation’, a strategy proposed in this study that creates coherent variations among data by enabling a set of parameters to alter representative features on a given building type. In the experiments that are described in this paper, more than 150 k input samples belonging to two building types have been processed during the training of a VAE model. The main contribution of this paper has been to explore parametric augmentation for the generation of large data sets of 3D geometries, showcasing its problems and limitations in the context of neural networks and VAEs. Results show that the generation of interpolated hybrid geometries is a challenging task. Despite the difficulty of the endeavour, promising advances are presented.

scholarly journals Channel-independent recreation of artefactual signals in chronically recorded local field potentials using machine learning

2022 ◽  
Vol 9 (1) ◽  
Author(s):  
Marcos Fabietti ◽  
Mufti Mahmud ◽  
Ahmad Lotfi
Keyword(s):  
Machine Learning ◽  
Open Access ◽  
Short Term Memory ◽  
The Body ◽  
Data Sets ◽  
Additional Information ◽  
Machine Learning Model ◽  
Signal Characteristics ◽  
Wide Range ◽  
Memory Network

AbstractAcquisition of neuronal signals involves a wide range of devices with specific electrical properties. Combined with other physiological sources within the body, the signals sensed by the devices are often distorted. Sometimes these distortions are visually identifiable, other times, they overlay with the signal characteristics making them very difficult to detect. To remove these distortions, the recordings are visually inspected and manually processed. However, this manual annotation process is time-consuming and automatic computational methods are needed to identify and remove these artefacts. Most of the existing artefact removal approaches rely on additional information from other recorded channels and fail when global artefacts are present or the affected channels constitute the majority of the recording system. Addressing this issue, this paper reports a novel channel-independent machine learning model to accurately identify and replace the artefactual segments present in the signals. Discarding these artifactual segments by the existing approaches causes discontinuities in the reproduced signals which may introduce errors in subsequent analyses. To avoid this, the proposed method predicts multiple values of the artefactual region using long–short term memory network to recreate the temporal and spectral properties of the recorded signal. The method has been tested on two open-access data sets and incorporated into the open-access SANTIA (SigMate Advanced: a Novel Tool for Identification of Artefacts in Neuronal Signals) toolbox for community use.

scholarly journals Data Watch: Research Data in Health Economics

2000 ◽  
Vol 14 (4) ◽  
pp. 203-216 ◽  
Author(s):  
William N Evans ◽  
Helen Levy ◽  
Kosali I Simon
Keyword(s):  
Health Economics ◽  
Health Care Providers ◽  
Vital Statistics ◽  
Economic Behavior ◽  
Data Sets ◽  
Care Providers ◽  
Important Data ◽  
Cross Sectional ◽  
Longitudinal Surveys ◽  
Conducting Research

In this paper, we discuss some important data sets that can be used by economists interested in conducting research in health economics. We describe six types of data sets: health components of data sets traditionally used by economists; longitudinal surveys of health and economic behavior; data on employer-provided insurance; cross-sectional surveys of households that focus on health; data on health care providers; and vital statistics. We summarize some of the leading surveys, discuss the availability of the data, identify how researchers have utilized these data and when possible, include a web address that contains more detailed information about each survey.

scholarly journals Prediction of Breast Tumor Malignancy Using Neural Network and Whale Optimization Algorithms (WOA)

2019 ◽  
Vol 12 (3) ◽  
pp. 26-35
Author(s):  
Ali Sharifi
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Early Diagnosis ◽  
Health Care Providers ◽  
Breast Tumor ◽  
Care Providers ◽  
Data Set ◽  
Whale Optimization ◽  
Effective Decision Making

Introduction: Breast cancer is the most prevalent cause of cancer mortality among women. Early diagnosis of breast cancer gives patients greater survival time. The present study aims to provide an algorithm for more accurate prediction and more effective decision-making in the treatment of patients with breast cancer. Methods: The present study was applied, descriptive-analytical, based on the use of computerized methods. We obtained 699 independent records containing nine clinical variables from the UCI machine learning. The EM algorithm was used to analyze the data before normalizing them. Following that, a combination of neural network model based on multilayer perceptron structure with the Whale Optimization Algorithm (WOA) was used to predict the breast tumor malignancy. Results: After preprocessing the disease data set and reducing data dimensions, the accuracy of the proposed algorithm for training and testing data was 99.6% and 99%, respectively. The prediction accuracy of the proposed model was 99.4%, which would be a satisfying result compared to different methods of machine learning in other studies. Conclusion: Considering the importance of early diagnosis of breast cancer, the results of this study may have highly useful implications for health care providers and planners so as to achieve the early diagnosis of the disease.

scholarly journals Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach

10.2196/30940 ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. e30940
Author(s):  
David Wiljer ◽  
Mohammad Salhia ◽  
Elham Dolatabadi ◽  
Azra Dhalla ◽  
Caitlin Gillan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Health Care Providers ◽  
Scoping Review ◽  
The United States ◽  
Data Driven ◽  
Data Sets ◽  
Health Providers ◽  
Care Providers ◽  
Environmental Scan

Background Significant investments and advances in health care technologies and practices have created a need for digital and data-literate health care providers. Artificial intelligence (AI) algorithms transform the analysis, diagnosis, and treatment of medical conditions. Complex and massive data sets are informing significant health care decisions and clinical practices. The ability to read, manage, and interpret large data sets to provide data-driven care and to protect patient privacy are increasingly critical skills for today’s health care providers. Objective The aim of this study is to accelerate the appropriate adoption of data-driven and AI-enhanced care by focusing on the mindsets, skillsets, and toolsets of point-of-care health providers and their leaders in the health system. Methods To accelerate the adoption of AI and the need for organizational change at a national level, our multistepped approach includes creating awareness and capacity building, learning through innovation and adoption, developing appropriate and strategic partnerships, and building effective knowledge exchange initiatives. Education interventions designed to adapt knowledge to the local context and address any challenges to knowledge use include engagement activities to increase awareness, educational curricula for health care providers and leaders, and the development of a coaching and practice-based innovation hub. Framed by the Knowledge-to-Action framework, we are currently in the knowledge creation stage to inform the curricula for each deliverable. An environmental scan and scoping review were conducted to understand the current state of AI education programs as reported in the academic literature. Results The environmental scan identified 24 AI-accredited programs specific to health providers, of which 11 were from the United States, 6 from Canada, 4 from the United Kingdom, and 3 from Asian countries. The most common curriculum topics across the environmental scan and scoping review included AI fundamentals, applications of AI, applied machine learning in health care, ethics, data science, and challenges to and opportunities for using AI. Conclusions Technologies are advancing more rapidly than organizations, and professionals can adopt and adapt to them. To help shape AI practices, health care providers must have the skills and abilities to initiate change and shape the future of their discipline and practices for advancing high-quality care within the digital ecosystem. International Registered Report Identifier (IRRID) PRR1-10.2196/30940

scholarly journals Data Analysis and Forecasting of the COVID-19 Spread: A Comparison of Recurrent Neural Networks and Time Series Models

2021 ◽  
Author(s):  
Daniela A. Gomez-Cravioto ◽  
Ramon E. Diaz-Ramos ◽  
Francisco J. Cantu-Ortiz ◽  
Hector G. Ceballos
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Health Care Providers ◽  
Time Series Data ◽  
Machine Learning Techniques ◽  
Series Data ◽  
Logistic Growth ◽  
Care Providers ◽  
Modern Approach ◽  
Feature Importance

AbstractTo understand and approach the spread of the SARS-CoV-2 epidemic, machine learning offers fundamental tools. This study presents the use of machine learning techniques for projecting COVID-19 infections and deaths in Mexico. The research has three main objectives: first, to identify which function adjusts the best to the infected population growth in Mexico; second, to determine the feature importance of climate and mobility; third, to compare the results of a traditional time series statistical model with a modern approach in machine learning. The motivation for this work is to support health care providers in their preparation and planning. The methods compared are linear, polynomial, and generalized logistic regression models to describe the growth of COVID-19 incidents in Mexico. Additionally, machine learning and time series techniques are used to identify feature importance and perform forecasting for daily cases and fatalities. The study uses the publicly available data sets from the John Hopkins University of Medicine in conjunction with the mobility rates obtained from Google’s Mobility Reports and climate variables acquired from the Weather Online API. The results suggest that the logistic growth model fits best the pandemic’s behavior, that there is enough correlation of climate and mobility variables with the disease numbers, and that the Long short-term memory network can be exploited for predicting daily cases. Given this, we propose a model to predict daily cases and fatalities for SARS-CoV-2 using time series data, mobility, and weather variables.

scholarly journals Comparing of Data Mining Techniques for Predicting In-Hospital Mortality Among Patients with COVID-19

2021 ◽  
Author(s):  
Mostafa Shanbehzadeh ◽  
Azam Orooji ◽  
Hadi Kazemi-Arpanahi
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Hospital Mortality ◽  
Health Care Providers ◽  
Prediction Models ◽  
Confusion Matrix ◽  
World Health ◽  
Medical Decision ◽  
Support Vector ◽  
Care Providers

Introduction: The COVID-19 epidemic is currently fronting the worldwide health care systems with many qualms and unexpected challenges in medical decision-making and the effective sharing of medical resources. Machine Learning (ML)-based prediction models can be potentially advantageous to overcome these uncertainties. Objective: This study aims to train several ML algorithms to predict the COVID-19 in-hospital mortality and compare their performance to choose the best performing algorithm. Finally, the contributing factors scored using some feature selection methods. Material and Methods: Using a single-center registry, we studied the records of 1353 confirmed COVID-19 hospitalized patients from Ayatollah Taleghani hospital, Abadan city, Iran. We applied six feature scoring techniques and nine well-known ML algorithms. To evaluate the models’ performances, the metrics derived from the confusion matrix calculated. Results: The study participants were 1353 patients, the male sex found to be higher than the women (742 vs. 611), and the median age was 57.25 (interquartile 18-100). After feature scoring, out of 54 variables, absolute neutrophil/lymphocyte count and loss of taste and smell were found the top three predictors. On the other hand, platelet count, magnesium, and headache gained the lowest importance for predicting the COVID-19 mortality. Experimental results indicated that the Bayesian network algorithm with an accuracy of 89.31% and a sensitivity of 64.2 % has been more successful in predicting mortality. Conclusion: ML provides a reasonable level of accuracy in predicting. So, using the ML-based prediction models facilitate more responsive health systems and would be beneficial for timely identification of vulnerable patients to inform appropriate judgment by the health care providers. Abbreviation: Coronavirus Disease 2019 (COVID‐19), World Health Organization (WHO), Machine Learning (ML), Artificial Intelligence (AI), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Locally Weighted Learning (LWL), Clinical Decision Support System (CDSS)

scholarly journals What Do Patients Say About Doctors Online? A Systematic Review of Studies on Patient Online Reviews (Preprint)

2018 ◽  
Author(s):  
Y Alicia Hong ◽  
Chen Liang ◽  
Tiffany A Radcliff ◽  
Lisa T Wigfall ◽  
Richard L Street
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health Care Providers ◽  
Health Care Quality ◽  
Online Reviews ◽  
The United States ◽  
Care Quality ◽  
Small Sample ◽  
Care Providers ◽  
Study Characteristics

BACKGROUND The number of patient online reviews (PORs) has grown significantly, and PORs have played an increasingly important role in patients’ choice of health care providers. OBJECTIVE The objective of our study was to systematically review studies on PORs, summarize the major findings and study characteristics, identify literature gaps, and make recommendations for future research. METHODS A major database search was completed in January 2019. Studies were included if they (1) focused on PORs of physicians and hospitals, (2) reported qualitative or quantitative results from analysis of PORs, and (3) peer-reviewed empirical studies. Study characteristics and major findings were synthesized using predesigned tables. RESULTS A total of 63 studies (69 articles) that met the above criteria were included in the review. Most studies (n=48) were conducted in the United States, including Puerto Rico, and the remaining were from Europe, Australia, and China. Earlier studies (published before 2010) used content analysis with small sample sizes; more recent studies retrieved and analyzed larger datasets using machine learning technologies. The number of PORs ranged from fewer than 200 to over 700,000. About 90% of the studies were focused on clinicians, typically specialists such as surgeons; 27% covered health care organizations, typically hospitals; and some studied both. A majority of PORs were positive and patients’ comments on their providers were favorable. Although most studies were descriptive, some compared PORs with traditional surveys of patient experience and found a high degree of correlation and some compared PORs with clinical outcomes but found a low level of correlation. CONCLUSIONS PORs contain valuable information that can generate insights into quality of care and patient-provider relationship, but it has not been systematically used for studies of health care quality. With the advancement of machine learning and data analysis tools, we anticipate more research on PORs based on testable hypotheses and rigorous analytic methods. CLINICALTRIAL International Prospective Register of Systematic Reviews (PROSPERO) CRD42018085057; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=85057 (Archived by WebCite at http://www.webcitation.org/76ddvTZ1C)

scholarly journals An Exploration and Forecast of COVID-19 in Mexico with Machine Learning

2020 ◽  
Author(s):  
Daniela A. Gomez-Cravioto ◽  
Ramon E. Diaz-Ramos ◽  
Francisco J. Cantu-Ortiz ◽  
Hector G. Ceballos
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Health Care Providers ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Logistic Growth ◽  
Care Providers ◽  
Modern Approach ◽  
Logistic Regression Models ◽  
Feature Importance

Abstract Background: To understand and approach the COVID-19 spread, Machine Learning offers fundamental tools. This study presents the use of machine learning techniques for the projection of COVID-19 infections and deaths in Mexico. The research has three main objectives: first, to identify which function adjusts the best to the infected population growth in Mexico; second, to determine the feature importance of climate and mobility; third, to compare the results of a traditional time series statistical model with a modern approach in machine learning. The motivation for this work is to support health care providers in their preparation and planning. Methods: The methods used are linear, polynomial, and generalized logistic regression models to evaluate the growth of the COVID-19 incidents in the country. Additionally, machine learning and time-series techniques are used to identify feature importance and perform forecasting for daily cases and fatalities. The study uses the publicly available data sets from the John Hopkins University of Medicine in conjunction with mobility rates obtained from Google’s Mobility Reports and climate variables acquired from Weather Online. Results: The results suggest that the logistic growth model fits best the behavior of the pandemic in Mexico, that there is a significant correlation of climate and mobility variables with the disease numbers, and that LSTM is a more suitable approach for the prediction of daily cases. Conclusion: We hope that this study can make some contributions to the world’s response to this epidemic as well as give some references for future research.

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