scholarly journals Cervical cancer risk prediction with robust ensemble and explainable black boxes method

2021 ◽  
Author(s):  
Francesco Curia
Keyword(s):  
Machine Learning ◽  
Cervical Cancer ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Intelligent Systems ◽  
Data Privacy ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Machine Learning Techniques ◽  
Female Population

AbstractClinical decision support systems (CDSS) that make use of algorithms based on intelligent systems, such as machine learning or deep learning, they suffer from the fact that often the methods used are hard to interpret and difficult to understand on how some decisions are made; the opacity of some methods, sometimes voluntary due to problems such as data privacy or the techniques used to protect intellectual property, makes these systems very complicated. Besides this series of problems, the results obtained also suffer from the poor possibility of being interpreted; in the clinical context therefore it is required that the methods used are as accurate as possible, transparent techniques and explainable results. In this work the problem of the development of cervical cancer is treated, a disease that mainly affects the female population. In order to introduce advanced machine learning techniques in a clinical decision support system that can be transparent and explainable, a robust, accurate ensemble method is presented, in terms of error and sensitivity linked to the classification of possible development of the aforementioned pathology and advanced techniques are also presented of explainability and interpretability (Explanaible Machine Learning) applied to the context of CDSS such as Lime and Shapley. The results obtained, as well as being interesting, are understandable and can be implemented in the treatment of this type of problem.

GUESS: projecting machine learning scores to well-calibrated probability estimates for clinical decision-making

2018 ◽  
Vol 35 (14) ◽  
pp. 2458-2465 ◽  
Author(s):  
Johanna Schwarz ◽  
Dominik Heider
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Clinical Decision Support ◽  
State Of The Art ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Calibration Method ◽  
Probability Estimates ◽  
Calibration Methods ◽  
Novel Method

Abstract Motivation Clinical decision support systems have been applied in numerous fields, ranging from cancer survival toward drug resistance prediction. Nevertheless, clinical decision support systems typically have a caveat: many of them are perceived as black-boxes by non-experts and, unfortunately, the obtained scores cannot usually be interpreted as class probability estimates. In probability-focused medical applications, it is not sufficient to perform well with regards to discrimination and, consequently, various calibration methods have been developed to enable probabilistic interpretation. The aims of this study were (i) to develop a tool for fast and comparative analysis of different calibration methods, (ii) to demonstrate their limitations for the use on clinical data and (iii) to introduce our novel method GUESS. Results We compared the performances of two different state-of-the-art calibration methods, namely histogram binning and Bayesian Binning in Quantiles, as well as our novel method GUESS on both, simulated and real-world datasets. GUESS demonstrated calibration performance comparable to the state-of-the-art methods and always retained accurate class discrimination. GUESS showed superior calibration performance in small datasets and therefore may be an optimal calibration method for typical clinical datasets. Moreover, we provide a framework (CalibratR) for R, which can be used to identify the most suitable calibration method for novel datasets in a timely and efficient manner. Using calibrated probability estimates instead of original classifier scores will contribute to the acceptance and dissemination of machine learning based classification models in cost-sensitive applications, such as clinical research. Availability and implementation GUESS as part of CalibratR can be downloaded at CRAN.

Variables Influencing Machine Learning-Based Cardiac Decision Support System: A Systematic Literature Review

2019 ◽  
Vol 892 ◽  
pp. 274-283
Author(s):  
Mohammed Ashikur Rahman ◽  
Afidalina Tumian
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision ◽  
Machine Learning Techniques ◽  
Cardiac Care ◽  
Learning Techniques

Now a day, clinical decision support systems (CDSS) are widely used in the cardiac care due to the complexity of the cardiac disease. The objective of this systematic literature review (SLR) is to identify the most common variables and machine learning techniques used to build machine learning-based clinical decision support system for cardiac care. This SLR adopts the Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) format. Out of 530 papers, only 21 papers met the inclusion criteria. Amongst the 22 most common variables are age, gender, heart rate, respiration rate, systolic blood pressure and medical information variables. In addition, our results have shown that Simplified Acute Physiology Score (SAPS), Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) are some of the most common assessment scales used in CDSS for cardiac care. Logistic regression and support vector machine are the most common machine learning techniques applied in CDSS to predict mortality and other cardiac diseases like sepsis, cardiac arrest, heart failure and septic shock. These variables and assessment tools can be used to build a machine learning-based CDSS.

Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review

2020 ◽  
Vol 102 ◽  
pp. 101762 ◽  
Author(s):  
Marta Fernandes ◽  
Susana M. Vieira ◽  
Francisca Leite ◽  
Carlos Palos ◽  
Stan Finkelstein ◽  
...  
Keyword(s):  
Emergency Department ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Intelligent Systems ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

scholarly journals Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1728
Author(s):  
Goran Medic ◽  
Melodi Kosaner Kließ ◽  
Louis Atallah ◽  
Jochen Weichert ◽  
Saswat Panda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Critical Care ◽  
Decision Support ◽  
Respiratory Distress ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Hemodynamic Instability ◽  
Evidence Based

Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.

scholarly journals Improving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms

2021 ◽  
Vol 17 (11) ◽  
pp. 60
Author(s):  
Halima EL Hamdaoui ◽  
Said Boujraf ◽  
Nour El Houda Chaoui ◽  
Badr Alami ◽  
Mustapha Maaroufi
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Decision Support ◽  
Random Forest ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Random Forest Algorithm ◽  
Adaboost Algorithm

heart disease is a major cause of death worldwide. Thus, diagnosis and prediction of heart disease remain mandatory. Clinical decision support systems based on machine learning techniques have become the primary tool to assist clinicians and contribute to automated diagnosis. This paper aims to predict heart disease using Random Forest algorithm enhanced with the boosting algorithm Adaboost. The model is trained and tested on University of California Irvine (UCI) Cleveland and Statlog heart disease datasets using the most relevant features 14 attributes. The result shows that Random Forest algorithm combined with AdaBoost algorithm achieved higher accuracy than applying only Radom Forest algorithm, 96.16%, 95.98%, respectively. We compare our suggested model to report machine learning classifiers. Indeed, the obtained result is supporting the efficiency and validity of our model. Besides, the proposed model achieved high accuracy compared to existing studies in the literature that confirmed that a clinical decision support system could be used to predict heart disease based on machine learning algorithms.

scholarly journals Machine Learning to Assist in Pneumonia Decision Making: A Systematic Review of the Literature

2020 ◽  
Author(s):  
Victor Silva ◽  
Amanda Days Ramos Novo ◽  
Damires Souza ◽  
Alex Rêgo
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Research Area ◽  
Review Of The Literature

Clinical decision support systems is a research area in which Machine Learning (ML) techniques can be applied. Nevertheless, specifically in assisting pneumonia decision making, the use of ML has not been so expressive. To help matters, this work aims to contribute to the evolution of the intersection of such areas by presenting a Systematic Review of the Literature. It provides results which may help to identify, interpret and evaluate how ML techniques have been applied and some research enhancements yet to be done.

scholarly journals Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1728
Author(s):  
Goran Medic ◽  
Melodi Kosaner Kließ ◽  
Louis Atallah ◽  
Jochen Weichert ◽  
Saswat Panda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Critical Care ◽  
Decision Support ◽  
Respiratory Distress ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Hemodynamic Instability ◽  
Evidence Based

Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.

The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

2018 ◽  
Vol 3 (2) ◽  
pp. 31-47 ◽  
Author(s):  
Steven Walczak
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Intelligent Systems ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.

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