NEURAL NETWORKS FOR DECISION SUPPORT: PROBLEMS AND OPPORTUNITIES

Neural networks offer an approach to computing which - unlike conventional programming - does not necessitate a complete algorithmic specification. Furthermore, neural networks provide inductive means for gathering, storing, and using, experiential knowledge. Incidentally, these have also been some of the fundamental motivations for the development of decision support systems in general. Thus, the interest in neural networks for decision support is immediate and obvious. In this paper, we analyze the potential contribution of neural networks for decision support, on one hand, and point out at some inherent constraints that might inhibit their use, on the other. For the sake of completeness and organization, the analysis is carried out in the context of a general-purpose DSS framework that examines all the key factors that come into play in the design of any decision support system.Information Systems Working Papers Serie

A systematic review of economic evaluations assessing the cost-effectiveness of licensed drugs used for previously treated epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) negative advanced/metastatic non-small cell lung cancer

Background Non-small cell lung cancer (NSCLC) is one of the most commonly diagnosed cancers. There are many published studies of cost-effectiveness analyses of licensed treatments, but no study has compared these studies or their approaches simultaneously. Objective To investigate the methodology used in published economic analyses of licensed interventions for previously treated advanced/metastatic NSCLC in patients without anaplastic lymphoma kinase or epidermal growth factor receptor expression. Methods A systematic review was performed, including a systematic search of key databases (e.g. MEDLINE, EMBASE, Web of Knowledge, Cost-effectiveness Registry) limited to the period from 01 January 2001 to 26 July 2019. Two reviewers independently screened, extracted data and quality appraised identified studies. The reporting quality of the studies was assessed by using the Consolidated Health Economic Evaluation Reporting Standards and the Philips’ checklists. Results Thirty-one published records met the inclusion criteria, which corresponded to 30 individual cost-effectiveness analyses. Analytical approaches included partitioned survival models (n = 14), state-transition models (n = 7) and retrospective analyses of new or published data (n = 8). Model structure was generally consistent, with pre-progression, post-progression and death health states used most commonly. Other characteristics varied more widely, including the perspective of analysis, discounting, time horizon, usually to align with the country that the analysis was set in. Conclusions There are a wide range of approaches in the modelling of treatments for advanced NSCLC; however, the model structures are consistent. There is variation in the exploration of sensitivity analyses, with considerable uncertainty remaining in most evaluations. Improved reporting is necessary to ensure transparency in future analyses