Abstract No. 563 Machine learning techniques to translate follow-up imaging post RFA and TACE for HCC: toward greater shared decision making in IR clinic

2019 ◽  
Vol 30 (3) ◽  
pp. S243-S244
Author(s):  
A. Brown ◽  
J. Kachura ◽  
A. Brown
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Shared Decision Making ◽  
Machine Learning Techniques ◽  
Shared Decision ◽  
Learning Techniques

The Sydney Triage to Admission Risk Tool (START2) using machine learning techniques to support disposition decision‐making

2018 ◽  
Vol 31 (3) ◽  
pp. 429-435 ◽  
Author(s):  
Kathryn Rendell ◽  
Irena Koprinska ◽  
Andre Kyme ◽  
Anja A Ebker‐White ◽  
Michael M Dinh
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Decision making via semi-supervised machine learning techniques

2016 ◽  
Author(s):  
Ευτύχιος Πρωτοπαπαδάκης
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques

Ο όρος μάθηση με μερική επίβλεψη αναφέρεται σε ένα ευρύ πεδίο τεχνικών μηχανικής μάθησης, οι οποίες χρησιμοποιούν τα μη τιτλοφορημένα δεδομένα για να εξάγουν επιπλέον ωφέλιμη πληροφορία. Η μερική επίβλεψη αντιμετωπίζει προβλήματα που σχετίζονται με την επεξεργασία και την αξιοποίηση μεγάλου όγκου δεδομένων και τα όποια κόστη σχετίζονται με αυτά (π.χ. χρόνος επεξεργασίας, ανθρώπινα λάθη). Απώτερος σκοπός είναι η ασφαλή εξαγωγή συμπερασμάτων, κανόνων ή προτάσεων. Τα μοντέλα λήψης απόφασης που χρησιμοποιούν τεχνικές μερικής μάθησης έχουν ποικίλα πλεονεκτήματα. Σε πρώτη φάση, χρειάζονται μικρό πλήθος τιτλοφορημένων δεδομένων για την αρχικοποίηση τους. Στη συνέχεια, τα νέα δεδομένα που θα εμφανιστούν αξιοποιούνται και τροποποιούν κατάλληλα το μοντέλο. Ως εκ τούτου, έχουμε ένα συνεχώς εξελισσόμενο μοντέλο λήψης αποφάσεων, με την ελάχιστη δυνατή προσπάθεια.Τεχνικές που προσαρμόζονται εύκολα και οικονομικά είναι οι κατεξοχήν κατάλληλες για τον έλεγχο συστημάτων, στα οποία παρατηρούνται συχνές αλλαγές στον τρόπο λειτουργίας. Ενδεικτικά πεδία εφαρμογής εφαρμογής ευέλικτων συστημάτων υποστήριξης λήψης αποφάσεων με μερική μάθηση είναι: η επίβλεψη γραμμών παραγωγής, η επιτήρηση θαλάσσιων συνόρων, η φροντίδα ηλικιωμένων, η εκτίμηση χρηματοπιστωτικού κινδύνου, ο έλεγχος για δομικές ατέλειες και η διαφύλαξη της πολιτιστικής κληρονομιάς.

Predicting Diabetes Mellitus With Machine Learning Techniques Using Multi-Criteria Decision Making

2021 ◽  
Vol 11 (2) ◽  
pp. 38-52
Author(s):  
Abhinav Juneja ◽  
Sapna Juneja ◽  
Sehajpreet Kaur ◽  
Vivek Kumar
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Healthy Lifestyle ◽  
Age Groups ◽  
Machine Learning Techniques ◽  
Multi Criteria Decision Making ◽  
Learning Techniques ◽  
Supervised Learning Algorithms ◽  
Unsupervised Algorithms ◽  
Early Diagnosis Of Diabetes

Diabetes has become one of the common health issues in people of all age groups. The disease is responsible for many difficulties in lifestyle and is represented by imbalance in hyperglycemia. If kept untreated, diabetes can raise the chance of heart attack, diabetic nephropathy, and other disorders. Early diagnosis of diabetes helps to maintain a healthy lifestyle. Machine learning is a capability of machine to learn from past pattern and occurrences and converge with experience to optimise and give decision. In the current research, the authors have employed machine learning techniques and used multi-criteria decision-making approach in Pima Indian diabetes dataset. To classify the patients, they examined several different supervised and unsupervised predictive models. After detailed analysis, it has been observed that the supervised learning algorithms outweigh the unsupervised algorithms due to the output class being a nominal classified domain.

Primary Factors Influencing the Decision to Vaccinate Against COVID-19 in the United States: A Predictive Analytics Approach (Preprint)

2021 ◽  
Author(s):  
Serkan Varol ◽  
Serkan Catma ◽  
Diana Reindl ◽  
Elizabeth Serieux
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Predictive Analytics ◽  
The United States ◽  
Machine Learning Techniques ◽  
Vaccine Hesitancy ◽  
Willingness To Accept ◽  
Perceptions And Attitudes ◽  
Learning Techniques ◽  
Vaccine Decision Making

BACKGROUND Vaccine refusal still poses a risk to reaching herd immunity in the United States. The existing literature focuses on identifying the predictors that would impact the willingness to accept (WTA) vaccines using survey data. These variables range from the socio-demographic characteristics of the participants to the perceptions and attitudes towards the vaccines so each variable’s statistical relationship with the WTA a vaccine can be investigated. However, while the results of these studies may have important implications for understanding vaccine hesitancy by offering interpretation of the statistical relationships, the prediction of vaccine decision-making has rarely been investigated OBJECTIVE We aimed to identify the factors that contribute to the prediction of COVID-19 vaccine acceptors and refusers using machine learning METHODS A nationwide survey was administered online in November, 2020 to assess American public perceptions and attitudes towards COVID-19 vaccines. Seven machine learning techniques were utilized to identify the model with the highest predictive power. Moreover, a set of variables that would contribute the most to the predictions of vaccine acceptors and refusers was identified using Gini importance based on Random Forest structure RESULTS The resulting machine learning algorithm has better prediction ability for willingness to accept (82%) versus reject (51%) a COVID-19 vaccine. In terms of predictive success, the Random Forest model outperformed the other machine learning techniques with a 69.52% accuracy rate. Worrying about (re) contracting Covid 19 and opinions regarding mandatory face covering were identified as the most important predictors of vaccine decision-making CONCLUSIONS The complexity of vaccine hesitancy needs to be investigated thoroughly before the threshold needed to reach population immunity can be achieved. Predictive analytics can help the public health officials design and deliver individually tailored vaccination programs that would increase the overall vaccine uptake.

Interactive Media for Diabetes Self-Management: Issues in Maximizing Public Health Impact

2010 ◽  
Vol 30 (6) ◽  
pp. 745-758 ◽  
Author(s):  
Russell E. Glasgow
Keyword(s):  
Public Health ◽  
Primary Care ◽  
Decision Making ◽  
Shared Decision Making ◽  
Interactive Media ◽  
Health Impact ◽  
Self Management ◽  
Public Health Impact ◽  
Shared Decision

Background . Diabetes self-management presents a series of challenging tasks, and primary care, where the majority of cases of adult diabetes are treated, is hard-pressed to address these issues given competing demands. This article discusses how interactive media (IM) can be used to support diabetes self-management. Methods . Following a brief review of the literature, the 5 As framework for enhancing the effectiveness of health behavior counseling and the RE-AIM model for estimating and enhancing public health impact are used to frame discussion of the strengths and limitations of IM for diabetes shared decision making and self-management support. Results . Data and lessons learned from a series of randomized trials of IM for diabetes self-management education are summarized around 2 key issues. The first is enhancing patient engagement in decision making and includes enhancing user experience and engagement, improving quality of care, and promoting collaborative action planning and follow-up. The second is getting such resources into place and sustaining them in real-world primary care settings and involves enhancing participation at patient, clinician, and health care system levels and enhancing the generalizability of results. Conclusions . Key opportunities for IM to support diabetes self-management include assessment of information for shared decision making, assistance with problem-solving self-management challenges, and provision of follow-up support. A key current challenge is the linkage of IM supports to the rest of the patient’s care, and collection of cost-effectiveness data is a key need for future research.

Sign in / Sign up

Export Citation Format

Share Document