Multi-phase Prediction Models for Crowdfunding Success: A Machine Learning Approach

2019 ◽  
Vol 19 (3) ◽  
pp. 129-147
Author(s):  
Hyunsang Lee ◽  
Sehwan Oh
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Phase Prediction ◽  
Multi Phase

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

scholarly journals COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

2020 ◽  
Author(s):  
Amir Mosavi
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Future Research ◽  
Complex Nature ◽  
Learning Approach ◽  
Epidemiological Models ◽  
Inference System ◽  
Proper Actions ◽  
Machine Learning Approach ◽  
Hybrid Machine

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

scholarly journals Prediction of treatment outcome in burning mouth syndrome patients using machine learning based on clinical data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Moon-Jong Kim ◽  
Pil-Jong Kim ◽  
Hong-Gee Kim ◽  
Hong-Seop Kho
Keyword(s):  
Machine Learning ◽  
Treatment Outcomes ◽  
Clinical Data ◽  
Prediction Models ◽  
Burning Mouth Syndrome ◽  
Learning Approach ◽  
Initial Approach ◽  
Machine Learning Approach ◽  
Burning Mouth ◽  
Clonazepam Therapy

AbstractThe purpose of this study is to apply a machine learning approach to predict whether patients with burning mouth syndrome (BMS) respond to the initial approach and clonazepam therapy based on clinical data. Among the patients with the primary type of BMS who visited the clinic from 2006 to 2015, those treated with the initial approach of detailed explanation regarding home care instruction and use of oral topical lubricants, or who were prescribed clonazepam for a minimum of 1 month were included in this study. The clinical data and treatment outcomes were collected from medical records. Extreme Gradient-Boosted Decision Trees was used for machine learning algorithms to construct prediction models. Accuracy of the prediction models was evaluated and feature importance calculated. The accuracy of the prediction models for the initial approach and clonazepam therapy was 67.6% and 67.4%, respectively. Aggravating factors and psychological distress were important features in the prediction model for the initial approach, and intensity of symptoms before administration was the important feature in the prediction model for clonazepam therapy. In conclusion, the analysis of treatment outcomes in patients with BMS using a machine learning approach showed meaningful results of clinical applicability.

scholarly journals COVID-19 Pandemic Prediction for Hungary: A Hybrid Machine Learning Approach

2020 ◽  
Author(s):  
Gergo Pinter ◽  
Imre Felde ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Richard Gloaguen
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Future Research ◽  
Complex Nature ◽  
Learning Approach ◽  
Epidemiological Models ◽  
Inference System ◽  
Proper Actions ◽  
Machine Learning Approach ◽  
Hybrid Machine

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

scholarly journals COVID-19 diagnosis prediction by symptoms of tested individuals: a machine learning approach

2020 ◽  
Author(s):  
Yazeed Zoabi ◽  
Noam Shomron
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Clinical Symptoms ◽  
Close Contact ◽  
Infected Individual ◽  
Learning Approach ◽  
Test Results ◽  
Machine Learning Approach ◽  
Simple Features ◽  
Limited Testing

AbstractEffective screening of SARS-CoV-2 enables quick and efficient diagnosis of COVID-19 and can mitigate the burden on healthcare systems. Prediction models that combine several features to estimate the risk of infection have been developed in hopes of assisting medical staff worldwide in triaging patients when allocating limited healthcare resources. We established a machine learning approach that trained on records from 51,831 tested individuals (of whom 4,769 were confirmed COVID-19 cases) while the test set contained data from the following week (47,401 tested individuals of whom 3,624 were confirmed COVID-19 cases). Our model predicts COVID-19 test results with high accuracy using only 8 features: gender, whether the age is above 60, information about close contact with an infected individual, and 5 initial clinical symptoms. Overall, using nationwide data representing the general population, we developed a model that enables screening suspected COVID-19 patients according to simple features accessed by asking them basic questions. Our model can be used, among other considerations, to prioritize testing for COVID-19 when allocating limited testing resources.

scholarly journals A Machine Learning Approach in Analyzing Bioaccumulation of Heavy Metals in Turbot Tissues

Molecules ◽  
2020 ◽  
Vol 25 (20) ◽  
pp. 4696
Author(s):  
Ștefan-Mihai Petrea ◽  
Mioara Costache ◽  
Dragoș Cristea ◽  
Ștefan-Adrian Strungaru ◽  
Ira-Adeline Simionov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heavy Metals ◽  
Heavy Metal ◽  
Liver Tissue ◽  
Linear Models ◽  
Prediction Models ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Non Linear ◽  
Liver Tissues

Metals are considered to be one of the most hazardous substances due to their potential for accumulation, magnification, persistence, and wide distribution in water, sediments, and aquatic organisms. Demersal fish species, such as turbot (Psetta maxima maeotica), are accepted by the scientific communities as suitable bioindicators of heavy metal pollution in the aquatic environment. The present study uses a machine learning approach, which is based on multiple linear and non-linear models, in order to effectively estimate the concentrations of heavy metals in both turbot muscle and liver tissues. For multiple linear regression (MLR) models, the stepwise method was used, while non-linear models were developed by applying random forest (RF) algorithm. The models were based on data that were provided from scientific literature, attributed to 11 heavy metals (As, Ca, Cd, Cu, Fe, K, Mg, Mn, Na, Ni, Zn) from both muscle and liver tissues of turbot exemplars. Significant MLR models were recorded for Ca, Fe, Mg, and Na in muscle tissue and K, Cu, Zn, and Na in turbot liver tissue. The non-linear tree-based RF prediction models (over 70% prediction accuracy) were identified for As, Cd, Cu, K, Mg, and Zn in muscle tissue and As, Ca, Cd, Mg, and Fe in turbot liver tissue. Both machine learning MLR and non-linear tree-based RF prediction models were identified to be suitable for predicting the heavy metal concentration from both turbot muscle and liver tissues. The models can be used for improving the knowledge and economic efficiency of linked heavy metals food safety and environment pollution studies.

scholarly journals Decision Algorithm for Heuristic Donor-Recipient Matching

MENDEL ◽  
2017 ◽  
Vol 23 (1) ◽  
pp. 33-40
Author(s):  
Ivars Namatevs ◽  
Ludmila Aleksejeva
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Optimal Allocation ◽  
Prediction Models ◽  
Decision Makers ◽  
Clinical Settings ◽  
Learning Approach ◽  
Decision Algorithm ◽  
Intelligent Computing ◽  
Machine Learning Approach

This paper introduces the application of artificial intelligence paradigm towards precision medicine in renal transplantation. The match of the optimal donor-recipient pair in kidney transplantation in Latvian Transplant Centre (LTC) has been constrained by the lack of prediction models and algorithms. Consequently, LTC seeks for practical intelligent computing solution to assist the clinical setting decision-makers during their search for the optimal donor-recipient match. Therefore, by optimizing both the donor and recipient profiles, prioritizing importance of the features, and based on greedy algorithm approach, advanced decision algorithm has been created. The strength of proposed algorithm lies in identification of suitable donors for a specific recipient based on evaluation of criteria by points principle. Experimental study demonstrates that the decision algorithm for heuristic donor-recipient matching integrated in machine learning approach improves the ability of optimal allocation of renal in LTC. It is an important step towards personalized medicine in clinical settings.

scholarly journals COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

2020 ◽  
Author(s):  
Gergo Pinter ◽  
Imre Felde ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Richard Gloaguen
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Future Research ◽  
Complex Nature ◽  
Learning Approach ◽  
Epidemiological Models ◽  
Inference System ◽  
Proper Actions ◽  
Machine Learning Approach ◽  
Hybrid Machine

AbstractSeveral epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

scholarly journals Machine learning-based prediction of COVID-19 diagnosis based on symptoms

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yazeed Zoabi ◽  
Shira Deri-Rozov ◽  
Noam Shomron
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Clinical Symptoms ◽  
Infected Individual ◽  
Learning Approach ◽  
Test Results ◽  
Risk Of Infection ◽  
Machine Learning Approach ◽  
Binary Features ◽  
Simple Features

AbstractEffective screening of SARS-CoV-2 enables quick and efficient diagnosis of COVID-19 and can mitigate the burden on healthcare systems. Prediction models that combine several features to estimate the risk of infection have been developed. These aim to assist medical staff worldwide in triaging patients, especially in the context of limited healthcare resources. We established a machine-learning approach that trained on records from 51,831 tested individuals (of whom 4769 were confirmed to have COVID-19). The test set contained data from the subsequent week (47,401 tested individuals of whom 3624 were confirmed to have COVID-19). Our model predicted COVID-19 test results with high accuracy using only eight binary features: sex, age ≥60 years, known contact with an infected individual, and the appearance of five initial clinical symptoms. Overall, based on the nationwide data publicly reported by the Israeli Ministry of Health, we developed a model that detects COVID-19 cases by simple features accessed by asking basic questions. Our framework can be used, among other considerations, to prioritize testing for COVID-19 when testing resources are limited.

Sign in / Sign up

Export Citation Format

Share Document