Multidisciplinary Decision Making: The Complexity of Some Potential “Real World” Clinical Scenarios

2017 ◽  
pp. 325-406
Keyword(s):  
Decision Making ◽  
Real World ◽  
Clinical Scenarios

Multidisciplinary Decision-Making: The “Real-World” Clinical Scenarios

2015 ◽  
pp. 247-315
Author(s):  
Leandro Chambrone ◽  
Luiz Armando Chambrone ◽  
Manuel De la Rosa-Garza ◽  
Marco Antonio Serna Gonzalez ◽  
Gerardo Guzman Pérez ◽  
...  
Keyword(s):  
Decision Making ◽  
Real World ◽  
The Real ◽  
Clinical Scenarios

VP33 Decision Making Clinical Scenarios: A Support Method For Health Technology Assessment

2017 ◽  
Vol 33 (S1) ◽  
pp. 163-164
Author(s):  
Luiz Santoro Neto
Keyword(s):  
Decision Making ◽  
Health Technology Assessment ◽  
Technology Assessment ◽  
Real World ◽  
Health Technology ◽  
Public Healthcare ◽  
Value Judgments ◽  
New Drug ◽  
Trade Offs ◽  
Clinical Scenarios

INTRODUCTION:The method appraises the stakeholders value judgments in the Health Technology Assessment (HTA) process, through a new model of research that addresses clinical scenarios to simulate real world HTA dilemmas and support decision making. The scenarios are based on criteria, such as clinical and epidemiological elements, and also, economic, social and ethical factors. The stakeholders decisions can induce strategic impacts in different HTA fields. We agreed to call this model Decision Making Clinical Scenarios (DMCS).METHODS:The model of research is based on a cross exploratory research, through a DMCS questionnaire applied to stakeholder respondents. The first survey was composed of four scenarios. The scenarios introduce value judgments, preferences and structuring choices, under specific circumstances. The scenarios are based on trade-offs involving HTA, such as budget impact, sources of funding, patients eligibility, technology characteristics and disease epidemiology. The stakeholders points of view are analyzed, through groups that represent payers, suppliers, developers, researchers, prescribers, regulators, government, patients and society.RESULTS:The scenarios have been shown to be understandable for all stakeholders groups. When testing the model with hypothetical dilemmas through clinical scenarios, the results are strongly influenced by each presented trade-off. We can observe specific trends and motivations when analyzing the stakeholders groups separately. The results are always evaluated and validated through statistical analysis. A total of 193 stakeholders answered the survey. The majority were male (n = 104; 53.9 percent) and aged between 31 and 40 years (n = 71; 36.8 percent). In scenario 1, almost half of respondents (n = 95; 49.2 percent opted not to incorporate the new drug and in scenario 2, an even higher proportion chose not to incorporate the new drug (n = 112; 58.0 percent). In scenario 3, most have responded to not incorporate the new treatment for any age group (n = 81; 42.0 percent). In scenario 4, 65 percent of respondents opted for the preferential allocation for prevention, rather than treatment (n = 125; 64.8 percent). Overall results showed a conservative trend, considering the presented criteria and trade-offs.CONCLUSIONS:We concluded that most stakeholders are not guided only by the clinical benefit of a decision. They valorize the importance of funding mechanisms and budget control, and consider economic, social, ethical, clinical and epidemiological aspects. This study model seems to be useful to evaluate the trends of decision makers conduct. We understand that the use of clinical scenarios brings the discussion into the enviroment and dynamics of the HTA process, where outcome impacts can be analyzed properly. This model can be explored in further research, using flexible criteria for each desired scenario, through real world situations. This model can be used to evaluate impacts in strategic subjects, as budget allocation, public healthcare policies, and patient-shared decision making.

scholarly journals Movie Recommendation through Multiple Bias Analysis

2021 ◽  
Vol 11 (6) ◽  
pp. 2817
Author(s):  
Tae-Gyu Hwang ◽  
Sung Kwon Kim
Keyword(s):  
Decision Making ◽  
Recommender System ◽  
Real World ◽  
Matrix Factorization ◽  
User Preferences ◽  
Improve Performance ◽  
Bias Analysis ◽  
Useful Knowledge ◽  
Existing Problems ◽  
Movie Recommendation

A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF). Therefore, a new method is required for analyzing preference patterns, which could not be derived by existing studies. This study aimed at solving the existing problems through bias analysis. By analyzing users’ and items’ biases of user preferences, the bias-based predictor (BBP) was developed and shown to outperform memory-based CF. In this paper, in order to enhance BBP, multiple bias analysis (MBA) was proposed to efficiently reflect the decision-making in real world. The experimental results using movie data revealed that MBA enhanced BBP accuracy, and that the hybrid models outperformed MF and SVD++. Based on this result, MBA is expected to improve performance when used as a system in related studies and provide useful knowledge in any areas that need features that can represent users.

Real‐world evidence to support regulatory decision making: New or expanded medical product indications

2021 ◽  
Author(s):  
Jessica M. Franklin ◽  
Kai‐Li Liaw ◽  
Solomon Iyasu ◽  
Cathy Critchlow ◽  
Nancy Dreyer
Keyword(s):  
Decision Making ◽  
Real World ◽  
Medical Product ◽  
Regulatory Decision ◽  
Real World Evidence

scholarly journals Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alan Brnabic ◽  
Lisa M. Hess
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Real World ◽  
Learning Algorithms ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract Background Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making. Methods This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist. Results A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies. Conclusions A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.

Review Summary Generation in Online Systems: Frameworks for Supervised and Unsupervised Scenarios

2021 ◽  
Vol 15 (3) ◽  
pp. 1-33
Author(s):  
Wenjun Jiang ◽  
Jing Chen ◽  
Xiaofei Ding ◽  
Jie Wu ◽  
Jiawei He ◽  
...  
Keyword(s):  
Decision Making ◽  
Real World ◽  
Text Summarization ◽  
Experimental Results ◽  
Product Review ◽  
Comprehensive Review ◽  
Online Systems ◽  
Real World Datasets ◽  
Different Characteristics

In online systems, including e-commerce platforms, many users resort to the reviews or comments generated by previous consumers for decision making, while their time is limited to deal with many reviews. Therefore, a review summary, which contains all important features in user-generated reviews, is expected. In this article, we study “how to generate a comprehensive review summary from a large number of user-generated reviews.” This can be implemented by text summarization, which mainly has two types of extractive and abstractive approaches. Both of these approaches can deal with both supervised and unsupervised scenarios, but the former may generate redundant and incoherent summaries, while the latter can avoid redundancy but usually can only deal with short sequences. Moreover, both approaches may neglect the sentiment information. To address the above issues, we propose comprehensive Review Summary Generation frameworks to deal with the supervised and unsupervised scenarios. We design two different preprocess models of re-ranking and selecting to identify the important sentences while keeping users’ sentiment in the original reviews. These sentences can be further used to generate review summaries with text summarization methods. Experimental results in seven real-world datasets (Idebate, Rotten Tomatoes Amazon, Yelp, and three unlabelled product review datasets in Amazon) demonstrate that our work performs well in review summary generation. Moreover, the re-ranking and selecting models show different characteristics.

scholarly journals Postlaunch evidence-generation studies for medical devices in Spain: the RedETS approach to integrate real-world evidence into decision making

2021 ◽  
Vol 37 (1) ◽  
Author(s):  
Pedro Serrano-Aguilar ◽  
Iñaki Gutierrez-Ibarluzea ◽  
Pilar Díaz ◽  
Iñaki Imaz-Iglesia ◽  
Jesús González-Enríquez ◽  
...  
Keyword(s):  
Decision Making ◽  
Medical Devices ◽  
Real World ◽  
Policy Decision ◽  
Assessment Process ◽  
The European Union ◽  
Political Commitment ◽  
Real World Evidence ◽  
Health Policy Decision ◽  
Regional Health Authorities

Abstract The Monitoring Studies (MS) program, the approach developed by RedETS to generate postlaunch real-world evidence (RWE), is intended to complement and enhance the conventional health technology assessment process to support health policy decision making in Spain, besides informing other interested stakeholders, including clinicians and patients. The MS program is focused on specific uncertainties about the real effect, safety, costs, and routine use of new and insufficiently assessed relevant medical devices carefully selected to ensure the value of the additional research needed, by means of structured, controlled, participative, and transparent procedures. However, despite a clear political commitment and economic support from national and regional health authorities, several difficulties were identified along the development and implementation of the first wave of MS, delaying its execution and final reporting. Resolution of these difficulties at the regional and national levels and a greater collaborative impulse in the European Union, given the availability of an appropriate methodological framework already provided by EUnetHTA, might provide a faster and more efficient comparative RWE of improved quality and reliability at the national and international levels.

scholarly journals Goal-driven active learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Process ◽  
Real World ◽  
Imitation Learning ◽  
Learning Approaches ◽  
Wide Range ◽  
Fixed Set ◽  
Complex Decision Making ◽  
Complex Decision

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.

An algebraic approach to N-soft sets with application in decision-making using TOPSIS

2021 ◽  
pp. 1-21
Author(s):  
Muhammad Shabir ◽  
Rimsha Mushtaq ◽  
Munazza Naz
Keyword(s):  
Decision Making ◽  
Mathematical Models ◽  
Real World ◽  
Algebraic Approach ◽  
Multi Criteria Decision Making ◽  
Commutative Monoids ◽  
Soft Sets ◽  
Algebraic Properties ◽  
Different Types ◽  
Selection Of

In this paper, we focus on two main objectives. Firstly, we define some binary and unary operations on N-soft sets and study their algebraic properties. In unary operations, three different types of complements are studied. We prove De Morgan’s laws concerning top complements and for bottom complements for N-soft sets where N is fixed and provide a counterexample to show that De Morgan’s laws do not hold if we take different N. Then, we study different collections of N-soft sets which become idempotent commutative monoids and consequently show, that, these monoids give rise to hemirings of N-soft sets. Some of these hemirings are turned out as lattices. Finally, we show that the collection of all N-soft sets with full parameter set E and collection of all N-soft sets with parameter subset A are Stone Algebras. The second objective is to integrate the well-known technique of TOPSIS and N-soft set-based mathematical models from the real world. We discuss a hybrid model of multi-criteria decision-making combining the TOPSIS and N-soft sets and present an algorithm with implementation on the selection of the best model of laptop.

scholarly journals Elite Decision-making and International Law: Promises and Perils of the Behavioral Revolution

AJIL Unbound ◽  
2021 ◽  
Vol 115 ◽  
pp. 242-247
Author(s):  
Emilie M. Hafner-Burton
Keyword(s):  
Decision Making ◽  
International Law ◽  
Real World ◽  
General Public ◽  
Decision Makers ◽  
Direct Study ◽  
The People ◽  
Growing Body ◽  
Actual Decision

A growing body of research applies behavioral approaches to the study of international law, mainly by studying convenience samples of students or other segments of the general public. Alongside the promises of this agenda are concerns about applying findings from non-elite populations to the people, and groups of people, charged with most real-world decision-making in the domain of law and governance. This concern is compounded by the fact that it is extremely difficult to recruit these actual decision-makers in a way that allows for direct study.

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