scholarly journals A Machine Learning Perspective on Personalized Medicine: An Automized, Comprehensive Knowledge Base with Ontology for Pattern Recognition

2018 ◽  
Vol 1 (1) ◽  
pp. 149-156 ◽  
Author(s):  
Frank Emmert-Streib ◽  
Matthias Dehmer
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Personalized Medicine ◽  
Knowledge Base ◽  
Great Promise ◽  
Key Factors ◽  
New Paradigm ◽  
Comprehensive Knowledge ◽  
Practical Guidelines ◽  
Patient Profiles

Personalized or precision medicine is a new paradigm that holds great promise for individualized patient diagnosis, treatment, and care. However, personalized medicine has only been described on an informal level rather than through rigorous practical guidelines and statistical protocols that would allow its robust practical realization for implementation in day-to-day clinical practice. In this paper, we discuss three key factors, which we consider dimensions that effect the experimental design for personalized medicine: (I) phenotype categories; (II) population size; and (III) statistical analysis. This formalization allows us to define personalized medicine from a machine learning perspective, as an automized, comprehensive knowledge base with an ontology that performs pattern recognition of patient profiles.

EXPERT MANAGEMENT SYSTEMS OF AGROTECHNOLOGIES IN CLOUD INFORMATION TECHNOLOGY

1970 ◽  
pp. 12-17
Author(s):  
I. М. Mikhaylenko ◽  
V. N. Timoshin
Keyword(s):  
Decision Making ◽  
Pattern Recognition ◽  
Information System ◽  
Knowledge Base ◽  
Information Technologies ◽  
Agricultural Sector ◽  
Perennial Grasses ◽  
Management Decision ◽  
Agricultural Technologies ◽  
Automation Systems

The transition to "intellectual" agriculture is the main vector of modernization of the agricultural sector of the economy. It is based on integrated automation and robotization of production, the use of automated decision-making systems. This is inevitably accompanied by a significant increase in data flow from sensors, monitoring systems, meteorological stations, drones, satellites and other external systems. Farm management has the opportunity to use various online applications for accurate recommendations and making various kinds of management decisions. In this regard, the most effective use of cloud information technologies, allowing implementing the most complex information and technical level of automation systems for management of agricultural technologies. The purpose of this work is to test the approach to creating expert management decision support systems (DSS) through the knowledge base (KB), formed in the cloud information system. For this, we consider an example of constructing a DSS for choosing the optimal date for preparing forage from perennial grasses. A complete theoretical and algorithmic database of the analytical DSS implemented in the data processing center of the cloud information system is given. On its basis, a KB is formed for a variety of different decision-making conditions. This knowledge base is transmitted to the local DSS. To make decisions about the optimal dates for the preparation of the local DSS, two variants of algorithms are used. The first option is based on management models, and the second uses the pattern recognition method. The approbation of the algorithms was carried out according to the BZ from 50 cases. According to the results of testing, the method of pattern recognition proved to be more accurate, which provides a more flexible adjustment of the situation on the local DSS to a similar situation in the KB. The considered technique can be extended to other crops.

Artificial neural network models for coronary artery disease

2020 ◽  
Vol 15 ◽  
Author(s):  
Elham Shamsara ◽  
Sara Saffar Soflaei ◽  
Mohammad Tajfard ◽  
Ivan Yamshchikov ◽  
Habibollah Esmaili ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Diagnostic Model ◽  
Early Prediction ◽  
Artificial Neural ◽  
Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among different machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Different methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant difference among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among different methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

scholarly journals Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Muhammad Javed Iqbal ◽  
Zeeshan Javed ◽  
Haleema Sadia ◽  
Ijaz A. Qureshi ◽  
Asma Irshad ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Diagnosis ◽  
Disease Risk ◽  
Clinical Applications ◽  
Optimal Decision ◽  
Great Promise ◽  
Time Range ◽  
Base System ◽  
The Future

AbstractArtificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment.

scholarly journals Application of Supervised Machine Learning to Recognize Competent Level and Mixed Antinuclear Antibody Patterns Based on ICAP International Consensus

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 642
Author(s):  
Yi-Da Wu ◽  
Ruey-Kai Sheu ◽  
Chih-Wei Chung ◽  
Yen-Ching Wu ◽  
Chiao-Chi Ou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Excellent Agreement ◽  
Antinuclear Antibody ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Observer Agreement ◽  
Supervised Machine Learning ◽  
International Consensus ◽  
Testing Data

Background: Antinuclear antibody pattern recognition is vital for autoimmune disease diagnosis but labor-intensive for manual interpretation. To develop an automated pattern recognition system, we established machine learning models based on the International Consensus on Antinuclear Antibody Patterns (ICAP) at a competent level, mixed patterns recognition, and evaluated their consistency with human reading. Methods: 51,694 human epithelial cells (HEp-2) cell images with patterns assigned by experienced medical technologists collected in a medical center were used to train six machine learning algorithms and were compared by their performance. Next, we choose the best performing model to test the consistency with five experienced readers and two beginners. Results: The mean F1 score in each classification of the best performing model was 0.86 evaluated by Testing Data 1. For the inter-observer agreement test on Testing Data 2, the average agreement was 0.849 (?) among five experienced readers, 0.844 between the best performing model and experienced readers, 0.528 between experienced readers and beginners. The results indicate that the proposed model outperformed beginners and achieved an excellent agreement with experienced readers. Conclusions: This study demonstrated that the developed model could reach an excellent agreement with experienced human readers using machine learning methods.

scholarly journals Prediction of activity and selectivity profiles of human Carbonic Anhydrase inhibitors using machine learning classification models

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Annachiara Tinivella ◽  
Luca Pinzi ◽  
Giulio Rastelli
Keyword(s):  
Machine Learning ◽  
Carbonic Anhydrase ◽  
A Priori ◽  
Selective Inhibition ◽  
Great Promise ◽  
Classification Models ◽  
Machine Learning Classification ◽  
Central Interest ◽  
Human Carbonic Anhydrase ◽  
In Silico Models

AbstractThe development of selective inhibitors of the clinically relevant human Carbonic Anhydrase (hCA) isoforms IX and XII has become a major topic in drug research, due to their deregulation in several types of cancer. Indeed, the selective inhibition of these two isoforms, especially with respect to the homeostatic isoform II, holds great promise to develop anticancer drugs with limited side effects. Therefore, the development of in silico models able to predict the activity and selectivity against the desired isoform(s) is of central interest. In this work, we have developed a series of machine learning classification models, trained on high confidence data extracted from ChEMBL, able to predict the activity and selectivity profiles of ligands for human Carbonic Anhydrase isoforms II, IX and XII. The training datasets were built with a procedure that made use of flexible bioactivity thresholds to obtain well-balanced active and inactive classes. We used multiple algorithms and sampling sizes to finally select activity models able to classify active or inactive molecules with excellent performances. Remarkably, the results herein reported turned out to be better than those obtained by models built with the classic approach of selecting an a priori activity threshold. The sequential application of such validated models enables virtual screening to be performed in a fast and more reliable way to predict the activity and selectivity profiles against the investigated isoforms.

scholarly journals Pattern discovery and disentanglement on relational datasets

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrew K. C. Wong ◽  
Pei-Yuan Zhou ◽  
Zahid A. Butt
Keyword(s):  
Machine Learning ◽  
Knowledge Base ◽  
Case Studies ◽  
Prediction Accuracy ◽  
Pattern Discovery ◽  
Explicit Representation ◽  
Human Cognition ◽  
Source Level ◽  
Low Volume

AbstractMachine Learning has made impressive advances in many applications akin to human cognition for discernment. However, success has been limited in the areas of relational datasets, particularly for data with low volume, imbalanced groups, and mislabeled cases, with outputs that typically lack transparency and interpretability. The difficulties arise from the subtle overlapping and entanglement of functional and statistical relations at the source level. Hence, we have developed Pattern Discovery and Disentanglement System (PDD), which is able to discover explicit patterns from the data with various sizes, imbalanced groups, and screen out anomalies. We present herein four case studies on biomedical datasets to substantiate the efficacy of PDD. It improves prediction accuracy and facilitates transparent interpretation of discovered knowledge in an explicit representation framework PDD Knowledge Base that links the sources, the patterns, and individual patients. Hence, PDD promises broad and ground-breaking applications in genomic and biomedical machine learning.

HEALTH TECHNOLOGY ASSESSMENT AND PERSONALIZED MEDICINE: ARE ECONOMIC EVALUATION GUIDELINES SUFFICIENT TO SUPPORT DECISION MAKING?

2014 ◽  
Vol 30 (2) ◽  
pp. 179-187 ◽  
Author(s):  
Don Husereau ◽  
Deborah A. Marshall ◽  
Adrian R. Levy ◽  
Stuart Peacock ◽  
Jeffrey S. Hoch
Keyword(s):  
Decision Making ◽  
Economic Evaluation ◽  
Personalized Medicine ◽  
Health Technology Assessment ◽  
Technology Assessment ◽  
Health Technology ◽  
Population Based ◽  
New Paradigm ◽  
Current Guidance ◽  
Support Decision Making

Background: Many jurisdictions delivering health care, including Canada, have developed guidance for conducting economic evaluation, often in the service of larger health technology assessment (HTA) and reimbursement processes. Like any health intervention, personalized medical (PM) interventions have costs and consequences that must be considered by reimbursement authorities with limited resources. However, current approaches to economic evaluation to support decision making have been largely developed from population-based approaches to therapy—that is, evaluating the costs and consequences of single interventions across single populations. This raises the issue as to whether these methods, as they are or more refined, are adequate to address more targeted approaches to therapy, or whether a new paradigm for assessing value in PM is required.Objectives: We describe specific issues relevant to the economic evaluation of diagnostics-based PM and assess whether current guidance for economic evaluation is sufficient to support decision making for PM interventions.Methods: Issues were identified through literature review and informal interviews with national and international experts (n = 10) in these analyses. This article elaborates on findings and discussion at a workshop held in Ottawa, Canada, in January 2012.Results: Specific issues related to better guiding economic evaluation of personalized medicine interventions include: how study questions are developed, populations are characterized, comparators are defined, effectiveness is evaluated, outcomes are valued and how resources are measured. Diagnostics-based PM also highlights the need for analyses outside of economic evaluation to support decision making.Conclusions: The consensus of this group of experts is that the economic evaluation of diagnostics-based PM may not require a new paradigm. However, greater complexity means that existing approaches and tools may require improvement to undertake these more analyses.

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