Multiple perspective dynamic decision making

Synergies of Text Mining and Multiple Attribute Decision Making: A Criteria Selection and Weighting System in a Prospective MADM Outline

In this study, a new way of criteria selection and a weighting system will be presented in a multi-disciplinary framework. Weighting criteria in Multi-Attribute Decision Making (MADM) has been developing as the most attractive section in the field. Although many ideas have been developed during the last decades, there is no such great diversity that can be mentioned in the literature. This study is looking from outside the box and is presenting something totally new by using big data and text mining in a Prospective MADM outline. PMADM is a hybrid interconnected concept between the Futures Studies and MADM fields. Text mining, which is known as a useful tool in Futures Studies, is applied to create a widespread pilot system for weighting and criteria selection in the PMADM outline. Latent Semantic Analysis (LSA), as an influential method inside the general concept of text mining, is applied to show how a data warehouse’s output, which in this case is Scopus, can reach the final criteria selection and weighting of the criteria.

The Multi-Aspect Criterion in the PMADM Outline and Its Possible Application to Sustainability Assessment

Over the past few centuries, the process of decision-making has become more complicated in different respects. Since the initial phase of Multiple Criteria Decision Making (MCDM) around fifty years ago, Multiple Attribute Decision Making (MADM) has continued developing over the years as a sub-concept of MCDM. Noticeably, the importance of the decision-making process is increasingly expanding to such an extent that it necessarily blends into the undeniable processes of MADM actual models. Novel methods with different perspectives have been introduced considering the dynamic MADM concepts of time and future in classical frameworks; however, they do not overcome challenges in practice. Recently, Prospective MADM (PMADM) as a specific approach has presented future-oriented models using already known approaches of MCDM, and it has innovative items which show barriers of classic model of MADM. However, PMADM practically needs more conceptual bases to illustrate and plan the future of real decision-making problems. The Multi-Aspect Criterion is a new concept in mapping the future of the PMADM outline. In this regard, two examples of sustainability will be analyzed, and different requirements and aspects associated with PMADM will be discussed in this study. This new approach can support the PMADM outline in more detail and deal with a decision-making structure that can be considered as novel to industry experts.

Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems

OBJECTIVE

One of the hardest technical tasks in employing Bayesian network models in practice is obtaining their numerical parameters. In the light of this difficulty, a pressing question, one that has immediate implications on the knowledge engineering effort, is whether precision of these parameters is important. In this paper, we address experimentally the question whether medical diagnostic systems based on Bayesian networks are sensitive to precision of their parameters.

METHODS AND MATERIALS

The test networks include Hepar II, a sizeable Bayesian network model for diagnosis of liver disorders and six other medical diagnostic networks constructed from medical data sets available through the Irvine Machine Learning Repository. Assuming that the original model parameters are perfectly accurate, we lower systematically their precision by rounding them to progressively courser scales and check the impact of this rounding on the models' accuracy.

RESULTS

Our main result, consistent across all tested networks, is that imprecision in numerical parameters has minimal impact on the diagnostic accuracy of models, as long as we avoid zeroes among parameters.

CONCLUSION

The experiments' results provide evidence that as long as we avoid zeroes among model parameters, diagnostic accuracy of Bayesian network models does not suffer from decreased precision of their parameters.

The Pittsburgh Cervical Cancer Screening Model: a risk assessment tool

CONTEXT

Evaluation of cervical cancer screening has grown increasingly complex with the introduction of human papillomavirus (HPV) vaccination and newer screening technologies approved by the US Food and Drug Administration.

OBJECTIVE

To create a unique Pittsburgh Cervical Cancer Screening Model (PCCSM) that quantifies risk for histopathologic cervical precancer (cervical intraepithelial neoplasia [CIN] 2, CIN3, and adenocarcinoma in situ) and cervical cancer in an environment predominantly using newer screening technologies.

DESIGN

The PCCSM is a dynamic Bayesian network consisting of 19 variables available in the laboratory information system, including patient history data (most recent HPV vaccination data), Papanicolaou test results, high-risk HPV results, procedure data, and histopathologic results. The model's graphic structure was based on the published literature. Results from 375 441 patient records from 2005 through 2008 were used to build and train the model. Additional data from 45 930 patients were used to test the model.

RESULTS

The PCCSM compares risk quantitatively over time for histopathologically verifiable CIN2, CIN3, adenocarcinoma in situ, and cervical cancer in screened patients for each current cytology result category and for each HPV result. For each current cytology result, HPV test results affect risk; however, the degree of cytologic abnormality remains the largest positive predictor of risk. Prior history also alters the CIN2, CIN3, adenocarcinoma in situ, and cervical cancer risk for patients with common current cytology and HPV test results. The PCCSM can also generate negative risk projections, estimating the likelihood of the absence of histopathologic CIN2, CIN3, adenocarcinoma in situ, and cervical cancer in screened patients.

CONCLUSIONS

The PCCSM is a dynamic Bayesian network that computes quantitative cervical disease risk estimates for patients undergoing cervical screening. Continuously updatable with current system data, the PCCSM provides a new tool to monitor cervical disease risk in the evolving postvaccination era.

NasoNet, modeling the spread of nasopharyngeal cancer with networks of probabilistic events in discrete time

The spread of cancer is a non-deterministic dynamic process. As a consequence, the design of an assistant system for the diagnosis and prognosis of the extent of a cancer should be based on a representation method that deals with both uncertainty and time. The ultimate goal is to know the stage of development of a cancer in a patient before selecting the appropriate treatment. A network of probabilistic events in discrete time (NPEDT) is a type of Bayesian network for temporal reasoning that models the causal mechanisms associated with the time evolution of a process. This paper describes NasoNet, a system that applies NPEDTs to the diagnosis and prognosis of nasopharyngeal cancer. We have made use of temporal noisy gates to model the dynamic causal interactions that take place in the domain. The methodology we describe is general enough to be applied to any other type of cancer.