A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care

2017 ◽  
Vol 41 (4) ◽  
Author(s):  
Hamdan O. Alanazi ◽  
Abdul Hanan Abdullah ◽  
Kashif Naseer Qureshi
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Predictive Models ◽  
Critical Review ◽  
Learning Methods ◽  
Machine Learning Methods

Using of data science in healthcare

2021 ◽  
pp. 149-156
Author(s):  
Ihor Ponomarenko ◽  
Oleksandra Lubkovska
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Business Intelligence ◽  
Data Science ◽  
Large Data ◽  
Science Methods ◽  
Medical Field ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data

The subject of the research is the approach to the possibility of using data science methods in the field of health care for integrated data processing and analysis in order to optimize economic and specialized processes The purpose of writing this article is to address issues related to the specifics of the use of Data Science methods in the field of health care on the basis of comprehensive information obtained from various sources. Methodology. The research methodology is system-structural and comparative analyzes (to study the application of BI-systems in the process of working with large data sets); monograph (the study of various software solutions in the market of business intelligence); economic analysis (when assessing the possibility of using business intelligence systems to strengthen the competitive position of companies). The scientific novelty the main sources of data on key processes in the medical field. Examples of innovative methods of collecting information in the field of health care, which are becoming widespread in the context of digitalization, are presented. The main sources of data in the field of health care used in Data Science are revealed. The specifics of the application of machine learning methods in the field of health care in the conditions of increasing competition between market participants and increasing demand for relevant products from the population are presented. Conclusions. The intensification of the integration of Data Science in the medical field is due to the increase of digitized data (statistics, textual informa- tion, visualizations, etc.). Through the use of machine learning methods, doctors and other health professionals have new opportunities to improve the efficiency of the health care system as a whole. Key words: Data science, efficiency, information, machine learning, medicine, Python, healthcare.

scholarly journals Development of Predictive Models for Identifying Potential S100A9 Inhibitors Based on Machine Learning Methods

2019 ◽  
Vol 7 ◽  
Author(s):  
Jihyeun Lee ◽  
Surendra Kumar ◽  
Sang-Yoon Lee ◽  
Sung Jean Park ◽  
Mi-hyun Kim
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials

2020 ◽  
Author(s):  
Edwin Tse ◽  
Laksh Aithani ◽  
Mark Anderson ◽  
Jonathan Cardoso-Silva ◽  
Giovanni Cincilla ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Predictive Models ◽  
Structural Information ◽  
Open Data ◽  
Ion Pump ◽  
Learning Methods ◽  
Machine Learning Methods

<p>The discovery of new antimalarial medicines with novel mechanisms of action is key to combating the problem of increasing resistance to our frontline treatments. The Open Source Malaria (OSM) consortium has been developing compounds ("Series 4") that have potent activity against <i>Plasmodium falciparum</i> <i>in vitro</i> and <i>in vivo</i> and that have been suggested to act through the inhibition of <i>Pf</i>ATP4, an essential membrane ion pump that regulates the parasite’s intracellular Na<sup>+</sup> concentration. The structure of <i>Pf</i>ATP4 is yet to be determined. In the absence of structural information about this target, a public competition was created to develop a model that would allow the prediction of anti-<i>Pf</i>ATP4 activity among Series 4 compounds, thereby reducing project costs associated with the unnecessary synthesis of inactive compounds.</p>In the first round, in 2016, six participants used the open data collated by OSM to develop moderately predictive models using diverse methods. Notably, all submitted models were available to all other participants in real time. Since then further bioactivity data have been acquired and machine learning methods have rapidly developed, so a second round of the competition was undertaken, in 2019, again with freely-donated models that other participants could see. The best-performing models from this second round were used to predict novel inhibitory molecules, of which several were synthesised and evaluated against the parasite. One such compound, containing a motif that the human chemists familiar with this series would have dismissed as ill-advised, was active. The project demonstrated the abilities of new machine learning methods in the prediction of active compounds where there is no biological target structure, frequently the central problem in phenotypic drug discovery. Since all data and participant interactions remain in the public domain, this research project “lives” and may be improved by others.

scholarly journals The Prediction-Explanation Fallacy: A Pervasive Problem in Scientific Applications of Machine Learning

2021 ◽  
Author(s):  
Marco Del Giudice
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Complex Structure ◽  
Multiple Models ◽  
Similar Data ◽  
Scientific Applications ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Mitigating Factors ◽  
Applications Of Machine Learning

In this paper, I highlight a problem that has become ubiquitous in scientific applications of machine learning methods, and can lead to seriously distorted inferences about the phenomena under study. I call it the prediction-explanation fallacy. The fallacy occurs when researchers use prediction-optimized models for explanatory purposes, without considering the tradeoffs between explanation and prediction. This is a problem for at least two reasons. First, prediction-optimized models are often deliberately biased and unrealistic in order to prevent overfitting, and hence fail to accurately explain the phenomenon of interest. In other cases, they have an exceedingly complex structure that is hard or impossible to interpret, which greatly limits their explanatory value. Second, different predictive models trained on the same or similar data can be biased in different ways, so that multiple models may predict equally well but suggest conflicting explanations of the underlying phenomenon. In this note I introduce the tradeoffs between prediction and explanation in a non-technical fashion, present some illustrative examples from neuroscience, and end by discussing some mitigating factors and methods that can be used to limit or circumvent the problem.

scholarly journals A Health Care Platform Design: Applying Novel Machine Learning Methods to Predict Chronic Cardiac Disease

2020 ◽  
Author(s):  
Chien-Hsiang Chang ◽  
◽  
You-Hsun Wu ◽  
Chih-Chun Yang ◽  
Meng-Ting Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Cardiac Disease ◽  
Learning Methods ◽  
Platform Design ◽  
Machine Learning Methods

scholarly journals Application of CT-Based Radiomics in Discriminating Pancreatic Cystadenomas From Pancreatic Neuroendocrine Tumors Using Machine Learning Methods

2021 ◽  
Vol 11 ◽  
Author(s):  
Xuejiao Han ◽  
Jing Yang ◽  
Jingwen Luo ◽  
Pengan Chen ◽  
Zilong Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Pancreatic Neuroendocrine Tumors ◽  
Selection Methods ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Testing Group

ObjectivesThe purpose of this study aimed at investigating the reliability of radiomics features extracted from contrast-enhanced CT in differentiating pancreatic cystadenomas from pancreatic neuroendocrine tumors (PNETs) using machine-learning methods.MethodsIn this study, a total number of 120 patients, including 66 pancreatic cystadenomas patients and 54 PNETs patients were enrolled. Forty-eight radiomic features were extracted from contrast-enhanced CT images using LIFEx software. Five feature selection methods were adopted to determine the appropriate features for classifiers. Then, nine machine learning classifiers were employed to build predictive models. The performance of the forty-five models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, and F1 score in the testing group.ResultsThe predictive models exhibited reliable ability of differentiating pancreatic cystadenomas from PNETs when combined with suitable selection methods. A combination of DC as the selection method and RF as the classifier, as well as Xgboost+RF, demonstrated the best discriminative ability, with the highest AUC of 0.997 in the testing group.ConclusionsRadiomics-based machine learning methods might be a noninvasive tool to assist in differentiating pancreatic cystadenomas and PNETs.

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