Paulo J. G. Lisboa

A review of evidence of health benefit from artificial neural networks in medical intervention

The purpose of this review is to assess the evidence of healthcare benefits involving the application of artificial neural networks to the clinical functions of diagnosis, prognosis and survival analysis, in the medical domains of oncology, critical care and cardiovascular medicine. The primary source of publications is PUBMED listings under Randomised Controlled Trials and Clinical Trials. The rĵle of neural networks is introduced within the context of advances in medical decision support arising from parallel developments in statistics and artificial intelligence. This is followed by a survey of published Randomised Controlled Trials and Clinical Trials, leading to recommendations for good practice in the design and evaluation of neural networks for use in medical intervention.

Neural networks in business: a survey of applications (1992–1998)

Abstract During the last decade, neural networks have established themselves as a theoretically sound alternative to traditional statistical models, and a large body of research on their application to business has been produced. The comprehensive range of business and financial applications is such that a focus is required for an in-depth analysis, therefore this review addresses applications related to management, marketing and decision making. Also, given that previous reviews have dealt with earlier publications, the time span of the review is limited to the period 1992–1998. The presentation is centred on summary tables with links between them. These tables classify the studies according to their application areas, the main contributions rendered by the use of neural networks, and the alleged advantages and disadvantages of this, as well as the journal of publication. Further information on the neural network models, other statistical methods against which they have been compared, and features of the analysed data are also provided. The more controversial issues concerning real-world applications of neural networks are discussed as a part of a critical analysis. Many of the studies are shown to be first attempts to apply these new techniques to established areas of research, whereas only a few tackle real-world cases. Although still regarded as a novel methodology, neural networks are shown to have matured to the point of offering real practical benefits in many of their applications.

The use of artificial neural networks in decision support in cancer: A systematic review

Artificial neural networks have featured in a wide range of medical journals, often with promising results. This paper reports on a systematic review that was conducted to assess the benefit of artificial neural networks (ANNs) as decision making tools in the field of cancer. The number of clinical trials (CTs) and randomised controlled trials (RCTs) involving the use of ANNs in diagnosis and prognosis increased from 1 to 38 in the last decade. However, out of 396 studies involving the use of ANNs in cancer, only 27 were either CTs or RCTs. Out of these trials, 21 showed an increase in benefit to healthcare provision and 6 did not. None of these studies however showed a decrease in benefit. This paper reviews the clinical fields where neural network methods figure most prominently, the main algorithms featured, methodologies for model selection and the need for rigorous evaluation of results.

Translation, rotation, and scale invariant pattern recognition by high-order neural networks and moment classifiers

The classification and recognition of two-dimensional patterns independently of their position, orientation, and size by using high-order networks are discussed. A method is introduced for reducing and controlling the number of weights of a third-order network used for invariant pattern recognition. The method leads to economical networks that exhibit high recognition rates for translated, rotated, and scaled, as well as locally distorted, patterns. The performance of these networks at recognizing types and handwritten numerals independently of their position, size, and orientation is compared with and found superior to the performance of a layered feedforward network to which image features extracted by the method of moments are presented as input.

A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer

A Bayesian framework is introduced to carry out Automatic Relevance Determination (ARD) in feedforward neural networks to model censored data. A procedure to identify and interpret the prognostic group allocation is also described. These methodologies are applied to 1616 records routinely collected at Christie Hospital, in a monthly cohort study with 5-year follow-up. Two cohort studies are presented, for low- and high-risk patients allocated by standard clinical staging. The results of contrasting the Partial Logistic Artificial Neural Network (PLANN)-ARD model with the proportional hazards model are that the two are consistent, but the neural network may be more specific in the allocation of patients into prognostic groups. With automatic model selection, the regularised neural network is more conservative than the default stepwise forward selection procedure implemented by SPSS with the Akaike Information Criterion.

The value of personalised recommender systems to e-business: a case study

Recommender systems have recently grown in popularity both in e-commerce and in research. However, there is little, if any, direct evidence in the literature of the value of recommender systems to e-Businesses, especially relating to consumer packaged goods (CPG) sold in a supermarket setting. We have been working in collaboration with LeShop (www.LeShop.ch), to gather real evidence of the added business value of a personalised recommender system. In this paper, we present our initial evaluation of the performance of our model-based personalised recommender systems over the 21-month period from May 2006 to January 2008, with particular focus on the added-value to the business. Our analysis covers shopper penetration, as well as the direct and indirect extra revenue generated by our recommender systems. One of the key lessons we have learnt during this case study is that the effect of a recommender system extends far beyond the direct extra revenue generated from the purchase of recommended items. The importance of maintaining updated model files was also found to be key to maintaining the performance of such model-based systems.

Risk factors for low birthweight in the public-hospitals at Peshawar, NWFP-Pakistan

BackgroundLow birthweight is a widely used indicator of newborn health. This study investigates the association of birthweight <2.5 kg (LBW) with a wide range of factors related to geo-demographics, maternal health and pregnancy history in public hospitals at Peshawar, North West Frontier Province (NWFP) Pakistan. It is noted that that Low birthweight may arise for two different reasons, one related to gestational age and the other corresponding to births that are small for gestational age (SGA).MethodsData on geo-demographics, maternal health indicators, pregnancy history and outcome scores for newborn babies and their families (n = 1039) were collected prospectively between August and November 2003 in a cross-sectional survey of four public hospitals in Peshawar, NWFP-Pakistan. Crude and adjusted odds ratios were used to investigate the factors affecting incidence of LBW, by multivariate logistic regression. Gestational age was included as an explanatory variable therefore the additional covariates identified by model selection are expected to account for SGA.ResultsThe main geo-demographic risk factors for SGA identified in this study, controlling for gestational age of less than 37 weeks, are maternal age, nationality and consanguinity. Presentation with anaemia and the history of previous abortion/miscarriage were also found to be significant independent factors. The adjusted odds ratio for gestational age showed the largest effect in explaining the incidence of LBW. The next highest odds ratio was for maternal age below 20 years. The explanatory model included two pairwise interactions, for which the predicted incidence figures for LBW show an increase among the Tribal area with presentation of anaemia, and among full term babies with their mothers having a previous history of abortion/miscarriage.ConclusionIn addition to gestational age, specific factors related to geo-demographics (maternal age, consanguinity and nationality), maternal health (anaemia) and pregnancy history (abortion/miscarriage) were significantly associated with the incidence of LBW observed at the four hospitals surveyed in Peshawar. These results indicate that cultural factors can adversely affect the incidence of SGA in this area of Pakistan.

The Static Baryon Baryon Potential in the Skyrme Model

Abstract Methods for deriving a static baryon-baryon (nucleon-nucleon as well as nucleon-delta and transition) potential from the Skyrme model are briefly reported. Some results obtained with these methods are also presented and discussed.

Quantitative characterization and prediction of on-line purchasing behavior: a latent variable approach

Abstract: Realizing the full potential of the on-line consumer market requires careful identification of customer needs and expectations. As research on Internet consumer behavior is still in its infancy, a quantitative framework to characterize user profiles for e-commerce has not yet been established. This study proposes a quantitative framework that uses factor analysis to identify latent factor descriptors of Internet users’ opinions on Web vendors and on-line shopping. Predictive models based on logistic discrimination and neural networks then select the factors most predictive of the propensity to buy on-line and classify Internet users accordingly. The application of this framework shows that the obtained latent factors agree in general with the major indicators identified in previous qualitative research. A small subset of the obtained factors is shown to retain the predictive power of the whole set. Neural networks are found to perform only marginally better than logistic discrimination in the task of classification.

p53 Status Identifies Two Subgroups of Triple-negative Breast Cancers with Distinct Biological Features

OBJECTIVE Despite the clinical similarities triple-negative and basal-like breast cancer are not synonymous. Indeed, not all basal-like cancers are negative for estrogen receptor, progesterone receptor and HER2 expression while triple-negative also encompasses other cancer types. P53 protein appears heterogeneously expressed in triple-negative breast cancers, suggesting that it may be associated with specific biological subgroups with a different outcome. METHODS We comparatively analyzed p53 expression in triple-negative tumors from two independent breast cancer case series (633 cases from the University of Ferrara and 1076 cases from the University of Nottingham). RESULTS In both case series, p53 protein expression was able to subdivide the triple-negative cases into two distinct subsets consistent with a different outcome. In fact, triple-negative patients with a p53 expressing tumor showed worse overall and event-free survival. CONCLUSIONS The immunohistochemical evaluation of p53 expression may help in taming the currently stormy relationship between pathological (triple-negative tumors) and biological (basal breast cancers) classifications and in selecting patient subgroups with different biological features providing a potentially powerful prognostic contribution in triple-negative breast cancers.