2010
DOI: 10.1007/978-3-642-11628-5_13
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Abstract: This paper proposes a Web clinical decision support system for clinical oncologists and for breast cancer patients making prognostic assessments, using the particular characteristics of the individual patient. This system comprises three different prognostic modelling methodologies: the clinically widely used Nottingham prognostic index (NPI); the Cox regression modelling and a partial logistic artificial neural network with automatic relevance determination (PLANN-ARD). All three models yield a different prog… Show more

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Cited by 5 publications
(5 citation statements)
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References 9 publications
(13 reference statements)
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“…This implies that despite the system's design being aimed toward its almost-autonomous operation, it is required, especially in the first implementation stages, a constant revision of the symbolic (not as much of the numerical) models to guarantee an appropriate use reliability that is comparable to other simpler decision models. In this sense, simpler multi-criteria decision models, or even models incorporating expert systems or Machine Learning algorithms [27,28,34,35,[39][40][41][42], reach success results that are similar to those presented in this article but always addressing problems with a lower complexity than breast cancer diagnosis has. However, it is necessary to highlight a key aspect: the presented system achieves the mentioned results with very few use iterations by combining different inference and analysis models together with rule modeling without a ready medical supervision.…”
supporting
confidence: 64%
See 1 more Smart Citation
“…This implies that despite the system's design being aimed toward its almost-autonomous operation, it is required, especially in the first implementation stages, a constant revision of the symbolic (not as much of the numerical) models to guarantee an appropriate use reliability that is comparable to other simpler decision models. In this sense, simpler multi-criteria decision models, or even models incorporating expert systems or Machine Learning algorithms [27,28,34,35,[39][40][41][42], reach success results that are similar to those presented in this article but always addressing problems with a lower complexity than breast cancer diagnosis has. However, it is necessary to highlight a key aspect: the presented system achieves the mentioned results with very few use iterations by combining different inference and analysis models together with rule modeling without a ready medical supervision.…”
supporting
confidence: 64%
“…Within this healthcare context, which is common in countries having a centralized healthcare system, and in light of all those matters previously commented, in this field, it is key to have tools available that allow providing support to the difficult process of evaluating and diagnosing breast cancer, trying to minimize as much as possible its subjective variability, which is translated into medical terms as false positive and false negative diagnosis cases. Following this line, in the last years, many diverse methodologies and systems have been presented, aiming to provide support to clinical decision processes focusing on the task of helping the professionals in the arduous diagnosis task [19][20][21][22][23][24][25][26][27][28][29][30] (this to all practical effects considered as a decision) about the presence and evolution of different cancer types [31][32][33][34][35][36][37][38][39][40][41][42].…”
Section: Introductionmentioning
confidence: 99%
“…In order to give a solution to the issue of knowledge representation and discovery, data-driven analytic methods, such as clinical data warehousing and data mining, have been explored to find treatment plans directly from clinical data (Duan, Street, & Xu, 2011). Fernandes et al (2010) designed a clinical decision support system on the Web to calculate the risk scores and the risk groups of cancer patients according to the clinical data of the patients. The Web system collects patients' prognoses and clinical data and displays these patient histories visually to clinical oncologists in order to improve the efficiency of medical decisions.…”
Section: Journal Of Management Analyticsmentioning
confidence: 99%
“…In [7] a web support system for clinical decision for oncologists and patients with breast cancer is proposed. This system comprises three different forecasting methodologies: the first one is the Nottingham Prognostic Index (NPI) used clinically; second is the Cox regression model and the third one is a Partial Artificial Neural Network with Automatic Relevance Determination (PLANN-ARD).…”
Section: Related Workmentioning
confidence: 99%