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.

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 Inter-Rater Reliability of Unstructured Text Labeling: Artificially vs. Naturally Intelligent Approaches

2021 ◽  
Author(s):  
Gleb Danilov ◽  
Alexandra Kosyrkova ◽  
Maria Shults ◽  
Semen Melchenko ◽  
Tatyana Tsukanova ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Pulmonary Embolism ◽  
Language Processing ◽  
Learning Approach ◽  
Medical Text ◽  
Unstructured Text ◽  
Textual Data ◽  
Machine Learning Approach ◽  
Rfbr Grant

Unstructured medical text labeling technologies are expected to be highly demanded since the interest in artificial intelligence and natural language processing arises in the medical domain. Our study aimed to assess the agreement between experts who judged on the fact of pulmonary embolism (PE) in neurosurgical cases retrospectively based on electronic health records and assess the utility of the machine learning approach to automate this process. We observed a moderate agreement between 3 independent raters on PE detection (Light’s kappa = 0.568, p = 0). Labeling sentences with the method we proposed earlier might improve the machine learning results (accuracy = 0.97, ROC AUC = 0.98) even in those cases that could not be agreed between 3 independent raters. Medical text labeling techniques might be more efficient when strict rules and semi-automated approaches are implemented. Machine learning might be a good option for unstructured text labeling when the reliability of textual data is properly addressed. This project was supported by the RFBR grant 18-29-22085.

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.

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