People with obesity exhibit losses in muscle proteostasis that are partly improved by exercise training.

This pilot experiment examines if a loss in muscle proteostasis occurs in people with obesity and whether endurance exercise positively influences either the abundance profile or turnover rate of proteins in this population. Men with (n = 3) or without (n = 4) obesity were recruited and underwent a 14-d measurement protocol of daily deuterium oxide (D2 O) consumption and serial biopsies of vastus lateralis muscle. Men with obesity then completed 10-weeks of high-intensity interval training (HIIT), encompassing 3 sessions per week of cycle ergometer exercise with 1 min intervals at 100% maximum aerobic power interspersed by 1 min recovery periods. The number of intervals per session progressed from 4 to 8, and during weeks 8-10 the 14-d measurement protocol was repeated. Proteomic analysis detected 352 differences (p < 0.05, false discovery rate < 5%) in protein abundance and 19 (p < 0.05) differences in protein turnover, including components of the ubiquitin-proteasome system. HIIT altered the abundance of 53 proteins and increased the turnover rate of 22 proteins (p < 0.05) and tended to benefit proteostasis by increasing muscle protein turnover rates. Obesity and insulin resistance are associated with compromised muscle proteostasis, which may be partially restored by endurance exercise.

Adaptation of rat fast-twitch muscle to endurance activity is underpinned by changes to protein degradation as well as protein synthesis.

Muscle adaptations to exercise are underpinned by alterations to the abundance of individual proteins, which may occur through a change either to the synthesis or degradation of each protein. We used deuterium oxide (2 H2 O) labeling and chronic low-frequency stimulation (CLFS) in vivo to investigate the synthesis, abundance, and degradation of individual proteins during exercise-induced muscle adaptation. Independent groups of rats received CLFS (10 Hz, 24 h/d) and 2 H2 O for 0, 10, 20, or 30 days. The extensor digitorum longus (EDL) was isolated from stimulated (Stim) and contralateral non-stimulated (Ctrl) legs. Proteomic analysis encompassed 38 myofibrillar and 46 soluble proteins and the rates of change in abundance, synthesis, and degradation were reported in absolute (ng/d) units. Overall, synthesis and degradation made equal contributions to the adaptation of the proteome, including instances where a decrease in protein-specific degradation primarily accounted for the increase in abundance of the protein.

Reliability of Protein Abundance and Synthesis Measurements in Human Skeletal Muscle.

The repeatability of dynamic proteome profiling (DPP), which is a novel technique for measuring the relative abundance (ABD) and fractional synthesis rate (FSR) of proteins in humans, is investigated. LC-MS analysis is performed on muscle samples taken from male participants (n = 4) that consumed 4 × 50 mL doses of deuterium oxide (2 H2 O) per day for 14 days. ABD is measured by label-free quantitation and FSR is calculated from time-dependent changes in peptide mass isotopomer abundances. One-hundred one proteins have at least one unique peptide and are used in the assessment of protein ABD. Fifty-four of these proteins meet more stringent criteria and are used in the assessment of FSR data. The median (M), lower-, (Q1 ) and upper-quartile (Q3 ) values for protein FSR (%/d) are M = 1.63, Q1  = 1.07, and Q3  = 3.24, respectively. The technical CV of ABD data has a median value of 3.6% (Q1 1.7% to Q3 6.7%), whereas the median CV of FSR data is 10.1% (Q1 3.5% to Q3 16.5%). These values compare favorably against other assessments of technical repeatability of proteomics data, which often set a CV of 20% as the upper bound of acceptability.

Match Physical Performance of Elite Female Soccer Players During International Competition.

Datson, N, Drust, B, Weston, M, Jarman, IH, Lisboa, P, and Gregson, W. Match physical performance of elite female soccer players during international competition. J Strength Cond Res 31(9): 2379-2387, 2017-The purpose of this study was to provide a detailed analysis of the physical demands of competitive international female soccer match play. A total of 148 individual match observations were undertaken on 107 outfield players competing in competitive international matches during the 2011-2012 and 2012-2013 seasons, using a computerized tracking system (Prozone Sports Ltd., Leeds, England). Total distance and total high-speed running distances were influenced by playing position, with central midfielders completing the highest (10,985 ± 706 m and 2,882 ± 500 m) and central defenders the lowest (9,489 ± 562 m and 1,901 ± 268 m) distances, respectively. Greater total very high-speed running distances were completed when a team was without (399 ± 143 m) compared to with (313 ± 210 m) possession of the ball. Most sprints were over short distances with 76% and 95% being less than 5 and 10 m, respectively. Between half reductions in physical performance were present for all variables, independent of playing position. This study provides novel findings regarding the physical demands of different playing positions in competitive international female match play and provides important insights for physical coaches preparing elite female players for competition.

Supervised wavelet method to predict patient survival from gene expression data.

In microarray studies, the number of samples is relatively small compared to the number of genes per sample. An important aspect of microarray studies is the prediction of patient survival based on their gene expression profile. This naturally calls for the use of a dimension reduction procedure together with the survival prediction model. In this study, a new method based on combining wavelet approximation coefficients and Cox regression was presented. The proposed method was compared with supervised principal component and supervised partial least squares methods. The different fitted Cox models based on supervised wavelet approximation coefficients, the top number of supervised principal components, and partial least squares components were applied to the data. The results showed that the prediction performance of the Cox model based on supervised wavelet feature extraction was superior to the supervised principal components and partial least squares components. The results suggested the possibility of developing new tools based on wavelets for the dimensionally reduction of microarray data sets in the context of survival analysis.

Finding reproducible cluster partitions for the k-means algorithm.

K-means clustering is widely used for exploratory data analysis. While its dependence on initialisation is well-known, it is common practice to assume that the partition with lowest sum-of-squares (SSQ) total i.e. within cluster variance, is both reproducible under repeated initialisations and also the closest that k-means can provide to true structure, when applied to synthetic data. We show that this is generally the case for small numbers of clusters, but for values of k that are still of theoretical and practical interest, similar values of SSQ can correspond to markedly different cluster partitions. This paper extends stability measures previously presented in the context of finding optimal values of cluster number, into a component of a 2-d map of the local minima found by the k-means algorithm, from which not only can values of k be identified for further analysis but, more importantly, it is made clear whether the best SSQ is a suitable solution or whether obtaining a consistently good partition requires further application of the stability index. The proposed method is illustrated by application to five synthetic datasets replicating a real world breast cancer dataset with varying data density, and a large bioinformatics dataset.

The effects of deprivation and relative deprivation on self-reported morbidity in England: an area-level ecological study.

BACKGROUND: Socioeconomic status gradients in health outcomes are well recognised and may operate in part through the psychological effect of observing disparities in affluence. At an area-level, we explored whether the deprivation differential between neighbouring areas influenced self-reported morbidity over and above the known effect of the deprivation of the area itself. METHODS: Deprivation differentials between small areas (population size approximately 1,500) and their immediate neighbours were derived (from the Index of Multiple Deprivation (IMD)) for Lower Super Output Area (LSOA) in the whole of England (n=32482). Outcome variables were self-reported from the 2001 UK Census: the proportion of the population suffering Limiting Long-Term Illness (LLTI) and 'not good health'. Linear regression was used to identify the effect of the deprivation differential on morbidity in different segments of the population, controlling for the absolute deprivation. The population was segmented using IMD tertiles and P2 People and Places geodemographic classification. P2 is a commercial market segmentation tool, which classifies small areas according to the characteristics of the population. The classifications range in deprivation, with the most affluent type being 'Mature Oaks' and the least being 'Urban Challenge'. RESULTS: Areas that were deprived compared to their immediate neighbours suffered higher rates of 'not good health' (ß=0.312, p<0.001) and LLTI (ß=0.278, p<0.001), after controlling for the deprivation of the area itself ('not good health'-ß=0.655, p<0.001; LLTI-ß=0.548, p<0.001). The effect of the deprivation differential relative to the effect of deprivation was strongest in least deprived segments (e.g., for 'not good health', P2 segments 'Mature Oaks'-ß=0.638; 'Rooted Households'-ß=0.555). CONCLUSIONS: Living in an area that is surrounded by areas of greater affluence has a negative impact on health in England. A possible explanation for this phenomenon is that negative social comparisons between areas cause ill-health. This 'psychosocial effect' is greater still in least deprived segments of the population, supporting the notion that psychosocial effects become more important when material (absolute) deprivation is less relevant.

Non-negative matrix factorisation methods for the spectral decomposition of MRS data from human brain tumours.

BACKGROUND: In-vivo single voxel proton magnetic resonance spectroscopy (SV 1H-MRS), coupled with supervised pattern recognition (PR) methods, has been widely used in clinical studies of discrimination of brain tumour types and follow-up of patients bearing abnormal brain masses. SV 1H-MRS provides useful biochemical information about the metabolic state of tumours and can be performed at short (< 45 ms) or long (> 45 ms) echo time (TE), each with particular advantages. Short-TE spectra are more adequate for detecting lipids, while the long-TE provides a much flatter signal baseline in between peaks but also negative signals for metabolites such as lactate. Both, lipids and lactate, are respectively indicative of specific metabolic processes taking place. Ideally, the information provided by both TE should be of use for clinical purposes. In this study, we characterise the performance of a range of Non-negative Matrix Factorisation (NMF) methods in two respects: first, to derive sources correlated with the mean spectra of known tissue types (tumours and normal tissue); second, taking the best performing NMF method for source separation, we compare its accuracy for class assignment when using the mixing matrix directly as a basis for classification, as against using the method for dimensionality reduction (DR). For this, we used SV 1H-MRS data with positive and negative peaks, from a widely tested SV 1H-MRS human brain tumour database. RESULTS: The results reported in this paper reveal the advantage of using a recently described variant of NMF, namely Convex-NMF, as an unsupervised method of source extraction from SV1H-MRS. Most of the sources extracted in our experiments closely correspond to the mean spectra of some of the analysed tumour types. This similarity allows accurate diagnostic predictions to be made both in fully unsupervised mode and using Convex-NMF as a DR step previous to standard supervised classification. The obtained results are comparable to, or more accurate than those obtained with supervised techniques. CONCLUSIONS: The unsupervised properties of Convex-NMF place this approach one step ahead of classical label-requiring supervised methods for the discrimination of brain tumour types, as it accounts for their increasingly recognised molecular subtype heterogeneity. The application of Convex-NMF in computer assisted decision support systems is expected to facilitate further improvements in the uptake of MRS-derived information by clinicians.

Enhanced survival prediction using explainable artificial intelligence in heart transplantation.

The most limiting factor in heart transplantation is the lack of donor organs. With enhanced prediction of outcome, it may be possible to increase the life-years from the organs that become available. Applications of machine learning to tabular data, typical of clinical decision support, pose the practical question of interpretation, which has technical and potential ethical implications. In particular, there is an issue of principle about the predictability of complex data and whether this is inherent in the data or strongly dependent on the choice of machine learning model, leading to the so-called accuracy-interpretability trade-off. We model 1-year mortality in heart transplantation data with a self-explaining neural network, which is benchmarked against a deep learning model on the same development data, in an external validation study with two data sets: (1) UNOS transplants in 2017-2018 (n = 4750) for which the self-explaining and deep learning models are comparable in their AUROC 0.628 [0.602,0.654] cf. 0.635 [0.609,0.662] and (2) Scandinavian transplants during 1997-2018 (n = 2293), showing good calibration with AUROCs of 0.626 [0.588,0.665] and 0.634 [0.570, 0.698], respectively, with and without missing data (n = 982). This shows that for tabular data, predictive models can be transparent and capture important nonlinearities, retaining full predictive performance.

Breast cancer patient characterisation and visualisation using deep learning and fisher information networks.

Breast cancer is the most commonly diagnosed female malignancy globally, with better survival rates if diagnosed early. Mammography is the gold standard in screening programmes for breast cancer, but despite technological advances, high error rates are still reported. Machine learning techniques, and in particular deep learning (DL), have been successfully used for breast cancer detection and classification. However, the added complexity that makes DL models so successful reduces their ability to explain which features are relevant to the model, or whether the model is biased. The main aim of this study is to propose a novel visualisation to help characterise breast cancer patients using Fisher Information Networks on features extracted from mammograms using a DL model. In the proposed visualisation, patients are mapped out according to their similarities and can be used to study new patients as a 'patient-like-me' approach. When applied to the CBIS-DDSM dataset, it was shown that it is a competitive methodology that can (i) facilitate the analysis and decision-making process in breast cancer diagnosis with the assistance of the FIN visualisations and 'patient-like-me' analysis, and (ii) help improve diagnostic accuracy and reduce overdiagnosis by identifying the most likely diagnosis based on clinical similarities with neighbouring patients.

Maternal risk factors in Afghan-refugees compared to Pakistani mothers in Peshawar, NWFP Pakistan.

OBJECTIVE: To determine the health risk factors associated with Afghan refugee mothers compared to Pakistani mothers. METHOD: A total of 1039 records of newborn and their mothers collected prospectively from the four public-hospitals in Peshawar during August-November 2003 were analysed, using crude and adjusted odds ratios for the comparison. RESULTS: The data revealed that low birthweight was 2.6 times higher in Afghan refugees compared to Pakistani mothers adjusting for all other important covariates. The univariate analysis highlighted a number of factors, however, the multivariate method established significant association of Afghan refugees with Tribal areas, older age and an un-registered pregnancy compared to Pakistani mothers. CONCLUSIONS: High geo-demographic risk factors were seen in Afghan refugee mothers; for which an appropriate strategy is required to provide reasonable healthcare facilities in the Tribal areas, and disseminate information regarding the risks involved in non-registration and old age pregnancies.

The effect of nursing interventions on the intracranial pressure in paediatric traumatic brain injury.

AIMS AND OBJECTIVES: The aim of this research was to investigate the effect of five selected intensive care nursing interventions on the intracranial pressure (ICP) of moderate to severe traumatic brain-injured children in intensive care. BACKGROUND: The physiological effects of many nursing interventions in paediatric intensive care (PIC) are not known. This results in the lack of an evidence base for many PIC nursing practices. DESIGN: Prospective observational cohort study conducted over 3 years in a single tertiary referral paediatric intensive care unit (PICU) in the North West of England. METHODS: Five selected commonly performed nursing interventions were studied: endotracheal suctioning and manual ventilation (ETSMV), turning via a log-rolling (LR) approach, eye care, oral care and washing. These were studied in the first 72 h after injury. RESULTS: A total of 25 children with moderate to severe traumatic brain injury and intraparenchymal ICP monitoring in intensive care (aged 2-17 years) were enrolled. Both ETSMV and LR were associated with clinically and statistically significant changes in ICP from baseline to maximal ICP (p = 0·001 ETSMV; p = < 0·001 LR) and from maximal post-ICP (p = < 0·001 ETSMV; p = < 0.001 LR). Eye care, oral care or washing did not cause any clinically significant change in ICP from baseline. After decompressive craniectomy, none of the interventions caused significant changes in ICP. CONCLUSIONS: Only two of the five nursing interventions, endotracheal suctioning and LR, caused intracranial hypertension in moderate to severe traumatic brain-injured children, and after craniectomy, no care interventions caused any significant change in ICP. RELEVANCE TO CLINICAL PRACTICE: Knowledge about the physiological effects of many intensive care nursing interventions is lacking and this is magnified in paediatrics. This study provides a significant addition to the evidence base in this area and allows intensive care nurses to plan, implement and evaluate more effectively their nursing care for brain-injured children.

Fractional Synthesis Rates of Individual Proteins in Rat Soleus and Plantaris Muscles.

Differences in the protein composition of fast- and slow-twitch muscle may be maintained by different rates of protein turnover. We investigated protein turnover rates in slow-twitch soleus and fast-twitch plantaris of male Wistar rats (body weight 412 ± 69 g). Animals were assigned to four groups (n = 3, in each), including a control group (0 d) and three groups that received deuterium oxide (D2O) for either 10 days, 20 days or 30 days. D2O administration was initiated by an intraperitoneal injection of 20 µL of 99% D2O-saline per g body weight, and maintained by provision of 4% (v/v) D2O in the drinking water available ad libitum. Soluble proteins from harvested muscles were analysed by liquid chromatography-tandem mass spectrometry and identified against the SwissProt database. The enrichment of D2O and rate constant (k) of protein synthesis was calculated from the abundance of peptide mass isotopomers. The fractional synthesis rate (FSR) of 44 proteins in soleus and 34 proteins in plantaris spanned from 0.58%/day (CO1A1: Collagen alpha-1 chain) to 5.40%/day NDRG2 (N-myc downstream-regulated gene 2 protein). Eight out of 18 proteins identified in both muscles had a different FSR in soleus than in plantaris (p < 0.05).

Robust Conditional Independence maps of single-voxel Magnetic Resonance Spectra to elucidate associations between brain tumours and metabolites.

The aim of the paper is two-fold. First, we show that structure finding with the PC algorithm can be inherently unstable and requires further operational constraints in order to consistently obtain models that are faithful to the data. We propose a methodology to stabilise the structure finding process, minimising both false positive and false negative error rates. This is demonstrated with synthetic data. Second, to apply the proposed structure finding methodology to a data set comprising single-voxel Magnetic Resonance Spectra of normal brain and three classes of brain tumours, to elucidate the associations between brain tumour types and a range of observed metabolites that are known to be relevant for their characterisation. The data set is bootstrapped in order to maximise the robustness of feature selection for nominated target variables. Specifically, Conditional Independence maps (CI-maps) built from the data and their derived Bayesian networks have been used. A Directed Acyclic Graph (DAG) is built from CI-maps, being a major challenge the minimization of errors in the graph structure. This work presents empirical evidence on how to reduce false positive errors via the False Discovery Rate, and how to identify appropriate parameter settings to improve the False Negative Reduction. In addition, several node ordering policies are investigated that transform the graph into a DAG. The obtained results show that ordering nodes by strength of mutual information can recover a representative DAG in a reasonable time, although a more accurate graph can be recovered using a random order of samples at the expense of increasing the computation time.

Stair negotiation behaviour of older individuals: Do step dimensions matter?

Stair falls are a major health problem for older people. Most studies on identification of stair fall risk factors are limited to staircases set in given step dimensions. However, it remains unknown whether the conclusions drawn would still apply if the dimensions had been changed to represent more challenging or easier step dimensions encountered in domestic and public buildings. The purpose was to investigate whether the self-selected biomechanical stepping behaviours are maintained when the dimensions of a staircase are altered. Sixty-eight older adults (>65 years) negotiated a seven-step staircase set in two step dimensions (shallow staircase: rise 15 cm, going 28 cm; steep staircase: rise 20 cm, going 25 cm). Six biomechanical outcome measures indicative of stair fall risk were measured. K-means clustering profiled the overall stair-negotiating behaviour and cluster profiles were calculated. A Cramer's V measured the degree of association in membership between clusters. The cluster profiles revealed that the biomechanically risky and conservative factors that characterized the overall behaviour in the clusters did not differ for the majority of older adults between staircases for ascent and descent. A strong association of membership between the clusters on the shallow staircase and the steep staircase was found for stair ascent (Cramer's V: 0.412, p < 0.001) and descent (Cramer's V: 0.380, p = 0.003). The findings indicate that manipulating the demand of the task would not affect the underpinning mechanism of a potential stair fall. Therefore, for most individuals, detection of stair fall risk might not require testing using a staircase with challenging step dimensions.

How to find simple and accurate rules for viral protease cleavage specificities.

BACKGROUND: Proteases of human pathogens are becoming increasingly important drug targets, hence it is necessary to understand their substrate specificity and to interpret this knowledge in practically useful ways. New methods are being developed that produce large amounts of cleavage information for individual proteases and some have been applied to extract cleavage rules from data. However, the hitherto proposed methods for extracting rules have been neither easy to understand nor very accurate. To be practically useful, cleavage rules should be accurate, compact, and expressed in an easily understandable way. RESULTS: A new method is presented for producing cleavage rules for viral proteases with seemingly complex cleavage profiles. The method is based on orthogonal search-based rule extraction (OSRE) combined with spectral clustering. It is demonstrated on substrate data sets for human immunodeficiency virus type 1 (HIV-1) protease and hepatitis C (HCV) NS3/4A protease, showing excellent prediction performance for both HIV-1 cleavage and HCV NS3/4A cleavage, agreeing with observed HCV genotype differences. New cleavage rules (consensus sequences) are suggested for HIV-1 and HCV NS3/4A cleavages. The practical usability of the method is also demonstrated by using it to predict the location of an internal cleavage site in the HCV NS3 protease and to correct the location of a previously reported internal cleavage site in the HCV NS3 protease. The method is fast to converge and yields accurate rules, on par with previous results for HIV-1 protease and better than previous state-of-the-art for HCV NS3/4A protease. Moreover, the rules are fewer and simpler than previously obtained with rule extraction methods. CONCLUSION: A rule extraction methodology by searching for multivariate low-order predicates yields results that significantly outperform existing rule bases on out-of-sample data, but are more transparent to expert users. The approach yields rules that are easy to use and useful for interpreting experimental data.

A novel multivariate approach for biomechanical profiling of stair negotiation.

Stair falls, especially during stair descent, are a major problem for older people. Stair fall risk has typically been assessed by quantifying mean differences between subject groups (e.g. older vs. younger individuals) for a number of biomechanical parameters individually indicative of risk, e.g., a reduced foot clearance with respect to the stair edge, which increases the chances of a trip. This approach neglects that individuals within a particular group may also exhibit other concurrent conservative strategies that could reduce the overall risk for a fall, e.g. a decreased variance in foot clearance. The purpose of the present study was to establish a multivariate approach that characterises the overall stepping behaviour of an individual. Twenty-five younger adults (age: 24.5 ±â€¯3.3 y) and 70 older adults (age: 71.1 ±â€¯4.1 y) descended a custom-built instrumented seven-step staircase at their self-selected pace in a step-over-step manner without using the handrails. Measured biomechanical parameters included: 1) Maximal centre of mass angular acceleration, 2) Foot clearance, 3) Proportion of foot length in contact with stair, 4) Required coefficient of friction, 5) Cadence, 6) Variance of these parameters. As a conventional analysis, a one-way ANOVA followed by Bonferroni post-hoc testing was used to identify differences between younger adults, older fallers and non-fallers. To examine differences in overall biomechanical stair descent behaviours between individuals, k-means clustering was used. The conventional grouping approach showed an effect of age and fall history on several single risk factors. The multivariate approach identified four clusters. Three clusters differed from the overall mean by showing both risky and conservative strategies on the biomechanical outcome measures, whereas the fourth cluster did not display any particularly risky or conservative strategies. In contrast to the conventional approach, the multivariate approach showed the stepping behaviours identified did not contain only older adults or previous fallers. This highlights the limited predictive power for stair fall risk of approaches based on single-parameter comparisons between predetermined groups. Establishing the predictive power of the current approach for future stair falls in older people is imperative for its implementation as a falls prevention tool.

Time-to-event analysis with artificial neural networks: an integrated analytical and rule-based study for breast cancer.

This paper presents an analysis of censored survival data for breast cancer specific mortality and disease-free survival. There are three stages to the process, namely time-to-event modelling, risk stratification by predicted outcome and model interpretation using rule extraction. Model selection was carried out using the benchmark linear model, Cox regression but risk staging was derived with Cox regression and with Partial Logistic Regression Artificial Neural Networks regularised with Automatic Relevance Determination (PLANN-ARD). This analysis compares the two approaches showing the benefit of using the neural network framework especially for patients at high risk. The neural network model also has results in a smooth model of the hazard without the need for limiting assumptions of proportionality. The model predictions were verified using out-of-sample testing with the mortality model also compared with two other prognostic models called TNG and the NPI rule model. Further verification was carried out by comparing marginal estimates of the predicted and actual cumulative hazards. It was also observed that doctors seem to treat mortality and disease-free models as equivalent, so a further analysis was performed to observe if this was the case. The analysis was extended with automatic rule generation using Orthogonal Search Rule Extraction (OSRE). This methodology translates analytical risk scores into the language of the clinical domain, enabling direct validation of the operation of the Cox or neural network model. This paper extends the existing OSRE methodology to data sets that include continuous-valued variables.

An integrated framework for risk profiling of breast cancer patients following surgery.

OBJECTIVE: An integrated decision support framework is proposed for clinical oncologists making prognostic assessments of patients with operable breast cancer. The framework may be delivered over a web interface. It comprises a triangulation of prognostic modelling, visualisation of historical patient data and an explanatory facility to interpret risk group assignments using empirically derived Boolean rules expressed directly in clinical terms. METHODS AND MATERIALS: The prognostic inferences in the interface are validated in a multicentre longitudinal cohort study by modelling retrospective data from 917 patients recruited at Christie Hospital, Wilmslow between 1983 and 1989 and predicting for 931 patients recruited in the same centre during 1990-1993. There were also 291 patients recruited between 1984 and 1998 at the Clatterbridge Centre for Oncology and the Linda McCartney Centre, Liverpool, UK. RESULTS AND CONCLUSIONS: There are three novel contributions relating this paper to breast cancer cases. First, the widely used Nottingham prognostic index (NPI) is enhanced with additional clinical features from which prognostic assessments can be made more specific for patients in need of adjuvant treatment. This is shown with a cross matching of the NPI and a new prognostic index which also provides a two-dimensional visualisation of the complete patient database by risk of negative outcome. Second, a principled rule-extraction method, orthogonal search rule extraction, generates readily interpretable explanations of risk group allocations derived from a partial logistic artificial neural network with automatic relevance determination (PLANN-ARD). Third, 95% confidence intervals for individual predictions of survival are obtained by Monte Carlo sampling from the PLANN-ARD model.