Dynamic early warning scores for predicting clinical deterioration in patients with respiratory disease.

BACKGROUND: The National Early Warning Score-2 (NEWS-2) is used to detect patient deterioration in UK hospitals but fails to take account of the detailed granularity or temporal trends in clinical observations. We used data-driven methods to develop dynamic early warning scores (DEWS) to address these deficiencies, and tested their accuracy in patients with respiratory disease for predicting (1) death or intensive care unit admission, occurring within 24 h (D/ICU), and (2) clinically significant deterioration requiring urgent intervention, occurring within 4 h (CSD). METHODS: Clinical observations data were extracted from electronic records for 31,590 respiratory in-patient episodes from April 2015 to December 2020 at a large acute NHS Trust. The timing of D/ICU was extracted for all episodes. 1100 in-patient episodes were annotated manually to record the timing of CSD, defined as a specific event requiring a change in treatment. Time series features were entered into logistic regression models to derive DEWS for each of the clinical outcomes. Area under the receiver operating characteristic curve (AUROC) was the primary measure of model accuracy. RESULTS: AUROC (95% confidence interval) for predicting D/ICU was 0.857 (0.852-0.862) for NEWS-2 and 0.906 (0.899-0.914) for DEWS in the validation data. AUROC for predicting CSD was 0.829 (0.817-0.842) for NEWS-2 and 0.877 (0.862-0.892) for DEWS. NEWS-2 ≥ 5 had sensitivity of 88.2% and specificity of 54.2% for predicting CSD, while DEWS ≥ 0.021 had higher sensitivity of 93.6% and approximately the same specificity of 54.3% for the same outcome. Using these cut-offs, 315 out of 347 (90.8%) CSD events were detected by both NEWS-2 and DEWS, at the time of the event or within the previous 4 h; 12 (3.5%) were detected by DEWS but not by NEWS-2, while 4 (1.2%) were detected by NEWS-2 but not by DEWS; 16 (4.6%) were not detected by either scoring system. CONCLUSION: We have developed DEWS that display greater accuracy than NEWS-2 for predicting clinical deterioration events in patients with respiratory disease. Prospective validation studies are required to assess whether DEWS can be used to reduce missed deteriorations and false alarms in real-life clinical settings.

Machine learning can predict disease manifestations and outcomes in lymphangioleiomyomatosis.

BACKGROUND: Lymphangioleiomyomatosis (LAM) is a rare multisystem disease with variable clinical manifestations and differing rates of progression that make management decisions and giving prognostic advice difficult. We used machine learning to identify clusters of associated features which could be used to stratify patients and predict outcomes in individuals. PATIENTS AND METHODS: Using unsupervised machine learning we generated patient clusters using data from 173 women with LAM from the UK and 186 replication subjects from the US National Heart, Lung, and Blood Institute (NHLBI) LAM registry. Prospective outcomes were associated with cluster results. RESULTS: Two- and three-cluster models were developed. A three-cluster model separated a large group of subjects presenting with dyspnoea or pneumothorax from a second cluster with a high prevalence of angiomyolipoma symptoms (p=0.0001) and tuberous sclerosis complex (TSC) (p=0.041). Patients in the third cluster were older, never presented with dyspnoea or pneumothorax (p=0.0001) and had better lung function. Similar clusters were reproduced in the NHLBI cohort. Assigning patients to clusters predicted prospective outcomes: in a two-cluster model the future risk of pneumothorax was 3.3 (95% CI 1.7-5.6)-fold greater in cluster 1 than cluster 2 (p=0.0002). Using the three-cluster model, the need for intervention for angiomyolipoma was lower in clusters 2 and 3 than cluster 1 (p<0.00001). In the NHLBI cohort, the incidence of death or lung transplant was much lower in clusters 2 and 3 (p=0.0045). CONCLUSIONS: Machine learning has identified clinically relevant clusters associated with complications and outcome. Assigning individuals to clusters could improve decision making and prognostic information for patients.

KI67 and DLX2 predict increased risk of metastasis formation in prostate cancer-a targeted molecular approach.

BACKGROUND: There remains a need to identify and validate biomarkers for predicting prostate cancer (CaP) outcomes using robust and routinely available pathology techniques to identify men at most risk of premature death due to prostate cancer. Previous immunohistochemical studies suggest the proliferation marker Ki67 might be a predictor of survival, independently of PSA and Gleason score. We performed a validation study of Ki67 as a marker of survival and disease progression and compared its performance against another candidate biomarker, DLX2, selected using artificial neural network analysis. METHODS: A tissue microarray (TMA) was constructed from transurethral resected prostatectomy histology samples (n=192). Artificial neural network analysis was used to identify candidate markers conferring increased risk of death and metastasis in a public cDNA array. Immunohistochemical analysis of the TMA was carried out and univariate and multivariate tests performed to explore the association of tumour protein levels of Ki67 and DLX2 with time to death and metastasis. RESULTS: Univariate analysis demonstrated Ki67 as predictive of CaP-specific survival (DSS; P=0.022), and both Ki67 (P=0.025) and DLX2 (P=0.001) as predictive of future metastases. Multivariate analysis demonstrated Ki67 as independent of PSA, Gleason score and D'Amico risk category for DSS (HR=2.436, P=0.029) and both Ki67 (HR=3.296, P=0.023) and DLX2 (HR=3.051, P=0.003) as independent for future metastases. CONCLUSIONS: High Ki67 expression is only present in 6.8% of CaP patients and is predictive of reduced survival and increased risk of metastasis, independent of PSA, Gleason score and D'Amico risk category. DLX2 is a novel marker of increased metastasis risk found in 73% patients and 8.2% showed co-expression with a high Ki67 score. Two cancer cell proliferation markers, Ki67 and DLX2, may be able to inform clinical decision-making when identifying patients for active surveillance.

A hybrid model for automatic identification of risk factors for heart disease.

Coronary artery disease (CAD) is the leading cause of death in both the UK and worldwide. The detection of related risk factors and tracking their progress over time is of great importance for early prevention and treatment of CAD. This paper describes an information extraction system that was developed to automatically identify risk factors for heart disease in medical records while the authors participated in the 2014 i2b2/UTHealth NLP Challenge. Our approaches rely on several nature language processing (NLP) techniques such as machine learning, rule-based methods, and dictionary-based keyword spotting to cope with complicated clinical contexts inherent in a wide variety of risk factors. Our system achieved encouraging performance on the challenge test data with an overall micro-averaged F-measure of 0.915, which was competitive to the best system (F-measure of 0.927) of this challenge task.

Automatic detection of protected health information from clinic narratives.

This paper presents a natural language processing (NLP) system that was designed to participate in the 2014 i2b2 de-identification challenge. The challenge task aims to identify and classify seven main Protected Health Information (PHI) categories and 25 associated sub-categories. A hybrid model was proposed which combines machine learning techniques with keyword-based and rule-based approaches to deal with the complexity inherent in PHI categories. Our proposed approaches exploit a rich set of linguistic features, both syntactic and word surface-oriented, which are further enriched by task-specific features and regular expression template patterns to characterize the semantics of various PHI categories. Our system achieved promising accuracy on the challenge test data with an overall micro-averaged F-measure of 93.6%, which was the winner of this de-identification challenge.

A supervised adverse drug reaction signalling framework imitating Bradford Hill's causality considerations.

Big longitudinal observational medical data potentially hold a wealth of information and have been recognised as potential sources for gaining new drug safety knowledge. Unfortunately there are many complexities and underlying issues when analysing longitudinal observational data. Due to these complexities, existing methods for large-scale detection of negative side effects using observational data all tend to have issues distinguishing between association and causality. New methods that can better discriminate causal and non-causal relationships need to be developed to fully utilise the data. In this paper we propose using a set of causality considerations developed by the epidemiologist Bradford Hill as a basis for engineering features that enable the application of supervised learning for the problem of detecting negative side effects. The Bradford Hill considerations look at various perspectives of a drug and outcome relationship to determine whether it shows causal traits. We taught a classifier to find patterns within these perspectives and it learned to discriminate between association and causality. The novelty of this research is the combination of supervised learning and Bradford Hill's causality considerations to automate the Bradford Hill's causality assessment. We evaluated the framework on a drug safety gold standard known as the observational medical outcomes partnership's non-specified association reference set. The methodology obtained excellent discrimination ability with area under the curves ranging between 0.792 and 0.940 (existing method optimal: 0.73) and a mean average precision of 0.640 (existing method optimal: 0.141). The proposed features can be calculated efficiently and be readily updated, making the framework suitable for big observational data.

Root gravitropism is regulated by a transient lateral auxin gradient controlled by a tipping-point mechanism.

Gravity profoundly influences plant growth and development. Plants respond to changes in orientation by using gravitropic responses to modify their growth. Cholodny and Went hypothesized over 80 years ago that plants bend in response to a gravity stimulus by generating a lateral gradient of a growth regulator at an organ's apex, later found to be auxin. Auxin regulates root growth by targeting Aux/IAA repressor proteins for degradation. We used an Aux/IAA-based reporter, domain II (DII)-VENUS, in conjunction with a mathematical model to quantify auxin redistribution following a gravity stimulus. Our multidisciplinary approach revealed that auxin is rapidly redistributed to the lower side of the root within minutes of a 90° gravity stimulus. Unexpectedly, auxin asymmetry was rapidly lost as bending root tips reached an angle of 40° to the horizontal. We hypothesize roots use a "tipping point" mechanism that operates to reverse the asymmetric auxin flow at the midpoint of root bending. These mechanistic insights illustrate the scientific value of developing quantitative reporters such as DII-VENUS in conjunction with parameterized mathematical models to provide high-resolution kinetics of hormone redistribution.

Characteristics of basal cytokeratin expression in breast cancer.

Breast cancer is recognised to be a heterogeneous disease and the second most common cause of morbidity and mortality worldwide in women. Basal-like breast cancer (BLBC) is associated with aggressive characteristics including development of recurrent disease and reduced survival. BLBC has been defined in some studies as tumours lacking both oestrogen receptor and progesterone receptor protein expression. Gene expression studies have shown that these tumours are also associated with expression of basal-type cytokeratins, the phenotypic patterns of basal cytokeratin expression in BLBC have not been widely studied. A well-characterised series of 995 invasive breast cancers with a long-term follow up were investigated using immunohistochemical staining for four basal cytokeratins (CK5, CK5/6, CK14 and CK17). The data were analysed using univariate and clustering analysis. As a result BLBC, as defined by negativity for ER and HER2 showed variable positivity for basal cytokeratin expression: 61.7 % CK5, 50.5 % CK5/6, 24.2 % CK14 and 23 % CK17. These characteristics were associated with poor outcome characteristics including high histological grade, mitosis, pleomorphism and tumour size >1.5 cm. CK5 positivity was more associated with ER(-), PgR(-), TN and double ER(-)PgR(-), than the other cytokeratins. Four different clusters of basal cytokeratin expression patterns were identified: (1) negativity for all basal cytokeratins, (2) CK5(+)/CK17(-), (3) CK5(-)/CK17(+) and (4) CK5(+)/CK17(+). These patterns of basal cytokeratin expression associated with differences in patient outcome, clusters 1 and 3 showed better outcomes than cluster 4 and 2, with cluster 2 having the poorest prognosis. In conclusion, four basal cytokeratin expression patterns were identified in human breast cancer using unsupervised clustering analysis and these patterns are associated with differences in patient outcome.

MysiRNA: improving siRNA efficacy prediction using a machine-learning model combining multi-tools and whole stacking energy (ΔG).

The investigation of small interfering RNA (siRNA) and its posttranscriptional gene-regulation has become an extremely important research topic, both for fundamental reasons and for potential longer-term therapeutic benefits. Several factors affect the functionality of siRNA including positional preferences, target accessibility and other thermodynamic features. State of the art tools aim to optimize the selection of target siRNAs by identifying those that may have high experimental inhibition. Such tools implement artificial neural network models as Biopredsi and ThermoComposition21, and linear regression models as DSIR, i-Score and Scales, among others. However, all these models have limitations in performance. In this work, a neural-network trained new siRNA scoring/efficacy prediction model was developed based on combining two existing scoring algorithms (ThermoComposition21 and i-Score), together with the whole stacking energy (ΔG), in a multi-layer artificial neural network. These three parameters were chosen after a comparative combinatorial study between five well known tools. Our developed model, 'MysiRNA' was trained on 2431 siRNA records and tested using three further datasets. MysiRNA was compared with 11 alternative existing scoring tools in an evaluation study to assess the predicted and experimental siRNA efficiency where it achieved the highest performance both in terms of correlation coefficient (R(2)=0.600) and receiver operating characteristics analysis (AUC=0.808), improving the prediction accuracy by up to 18% with respect to sensitivity and specificity of the best available tools. MysiRNA is a novel, freely accessible model capable of predicting siRNA inhibition efficiency with improved specificity and sensitivity. This multiclassifier approach could help improve the performance of prediction in several bioinformatics areas. MysiRNA model, part of MysiRNA-Designer package [1], is expected to play a key role in siRNA selection and evaluation.

Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models.

It has been often demonstrated that clinicians exhibit both inter-expert and intra-expert variability when making difficult decisions. In contrast, the vast majority of computerized models that aim to provide automated support for such decisions do not explicitly recognize or replicate this variability. Furthermore, the perfect consistency of computerized models is often presented as a de facto benefit. In this paper, we describe a novel approach to incorporate variability within a fuzzy inference system using non-stationary fuzzy sets in order to replicate human variability. We apply our approach to a decision problem concerning the recommendation of post-operative breast cancer treatment; specifically, whether or not to administer chemotherapy based on assessment of five clinical variables: NPI (the Nottingham Prognostic Index), estrogen receptor status, vascular invasion, age and lymph node status. In doing so, we explore whether such explicit modeling of variability provides any performance advantage over a more conventional fuzzy approach, when tested on a set of 1310 unselected cases collected over a fourteen year period at the Nottingham University Hospitals NHS Trust, UK. The experimental results show that the standard fuzzy inference system (that does not model variability) achieves overall agreement to clinical practice around 84.6% (95% CI: 84.1-84.9%), while the non-stationary fuzzy model can significantly increase performance to around 88.1% (95% CI: 88.0-88.2%), p<0.001. We conclude that non-stationary fuzzy models provide a valuable new approach that may be applied to clinical decision support systems in any application domain.

LMISA: A lightweight multi-modality image segmentation network via domain adaptation using gradient magnitude and shape constraint.

In medical image segmentation, supervised machine learning models trained using one image modality (e.g. computed tomography (CT)) are often prone to failure when applied to another image modality (e.g. magnetic resonance imaging (MRI)) even for the same organ. This is due to the significant intensity variations of different image modalities. In this paper, we propose a novel end-to-end deep neural network to achieve multi-modality image segmentation, where image labels of only one modality (source domain) are available for model training and the image labels for the other modality (target domain) are not available. In our method, a multi-resolution locally normalized gradient magnitude approach is firstly applied to images of both domains for minimizing the intensity discrepancy. Subsequently, a dual task encoder-decoder network including image segmentation and reconstruction is utilized to effectively adapt a segmentation network to the unlabeled target domain. Additionally, a shape constraint is imposed by leveraging adversarial learning. Finally, images from the target domain are segmented, as the network learns a consistent latent feature representation with shape awareness from both domains. We implement both 2D and 3D versions of our method, in which we evaluate CT and MRI images for kidney and cardiac tissue segmentation. For kidney, a public CT dataset (KiTS19, MICCAI 2019) and a local MRI dataset were utilized. The cardiac dataset was from the Multi-Modality Whole Heart Segmentation (MMWHS) challenge 2017. Experimental results reveal that our proposed method achieves significantly higher performance with a much lower model complexity in comparison with other state-of-the-art methods. More importantly, our method is also capable of producing superior segmentation results than other methods for images of an unseen target domain without model retraining. The code is available at GitHub (https://github.com/MinaJf/LMISA) to encourage method comparison and further research.

RERG (Ras-like, oestrogen-regulated, growth-inhibitor) expression in breast cancer: a marker of ER-positive luminal-like subtype.

Global gene expression profiling studies have classified breast cancer into a number of distinct biological and molecular classes with clinical relevance. The heterogeneous luminal group, which is largely characterised by oestrogen receptor (ER) expression, appears to contain distinct subgroups with differing behaviour. In this study, we analysed 47,293 gene transcripts in 128 invasive breast carcinomas (BC) using Artificial Neural Networks and a cross-validation analysis in combination with an ensemble sample classification to identify genes that can be used to subclassify ER+ luminal tumours. The results were validated using immunohistochemistry on TMAs containing 1,140 invasive breast cancers. Our results showed that the RERG gene is one of the highest ranked genes to differentiate between ER+ luminal-like and ER- non-luminal cancers based on a 10-fold external cross-validation analysis with an average classification accuracy of 89%. This was confirmed in our protein expression studies that showed RERG positive associations with markers of luminal differentiation including ER, luminal cytokeratins (CK19, CK18 and CK7/8) and FOXA1 (P = 0.004) and other markers of good prognosis in BC including small size, lower histologic grade and positive expression of androgen receptor, nuclear BRCA1, FHIT and cell cycle inhibitors p27 and p21. RERG expression was inversely associated with the proliferation marker MIB1 (P = 0.005) and p53. Strong RERG expression showed an association with longer breast cancer specific survival and distant metastasis free interval in the whole series as well as in the ER+ luminal group and these associations were independent of other prognostic variables. In conclusion, we used novel bioinformatics methods to identify candidate genes to characterise ER+ luminal-like breast cancer. RERG gene is a key marker of the luminal BC class and can be used to separate distinct prognostic subgroups.

The Barriers and Motivators to Using Human Tissues for Research: The Views of UK-Based Biomedical Researchers.

Introduction: The use of human-derived samples is vital to numerous areas of biological and medical research. Despite this, researchers often find or anticipate difficulty in sourcing samples. There are ongoing efforts to increase the visibility and accessibility of UK human tissue biobanking, but minimal (if any) research on the reasons behind researchers' choice of sample source has been undertaken. We have analyzed UK researchers' motivations on using their preferred sample sources and their perceived barriers to human sample use. Methods: The study was based on an online survey of academic and industry researchers, followed by focus groups, with participants across the United Kingdom. Both the survey and focus groups probed participants' views on the barriers to finding and using human samples in research. Results: One hundred ninety-eight academic and industry researchers completed the survey on their human sample use, and five focus groups consisting of 21 total participants took place. The top cited reasons for choosing sources included the availability of linked clinical data (40%), the geographical location of the resource (39%), and preexisting collaboration (33%). Focus group participants highlighted their strong preference for local or known sample sources, which were preferred because additional scientific and logistical input could be obtained for their work and they were more confident that the samples would be of good quality. Discussion: We found that there were significant perceptions of governance barriers to sample access. As a consequence, researchers preferred local and known suppliers because of the perception that these could assist with the governance, would be reliable, and able to provide the additional support they required. Equally, data availability was a major contributor to the selection of a new source of samples. These observations are of significant value to those seeking to improve the access to existing sample resources via online discovery tools.

ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization.

BACKGROUND: Statistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data sets and analysis methods, it is desirable to combine both different algorithms and data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods for this purpose in an almost fully automated process and linking different analysis modules together under a single interface would simplify many microarray analysis tasks. RESULTS: We present ArrayMining.net, a web-application for microarray analysis that provides easy access to a wide choice of feature selection, clustering, prediction, gene set analysis and cross-study normalization methods. In contrast to other microarray-related web-tools, multiple algorithms and data sets for an analysis task can be combined using ensemble feature selection, ensemble prediction, consensus clustering and cross-platform data integration. By interlinking different analysis tools in a modular fashion, new exploratory routes become available, e.g. ensemble sample classification using features obtained from a gene set analysis and data from multiple studies. The analysis is further simplified by automatic parameter selection mechanisms and linkage to web tools and databases for functional annotation and literature mining. CONCLUSION: ArrayMining.net is a free web-application for microarray analysis combining a broad choice of algorithms based on ensemble and consensus methods, using automatic parameter selection and integration with annotation databases.

Young adults' attitudes to sharing whole-genome sequencing information: a university-based survey.

BACKGROUND: Genomic services are increasingly accessible to young adults starting their independent lives with responsibility for their self-care, yet their attitudes to sharing genomic information remain under-researched. This study explored attitudes of university-based 18-25 year-olds towards sharing personal whole-genome sequencing (WGS) information with relatives. METHODS: We surveyed 112 young adults. Hypotheses were tested regarding the relationships between their preferences for sharing personal WGS information with relatives and factors including their gender, previous genetics-specific education, general educational attainment level and current study in a science, technology, engineering, maths or medicine (STEMM) field. RESULTS: Most participants were positive about both their intention to share their WGS results with their parents and siblings, and their desire to know their relatives' results. Being female and having a university-level genetics education were consistently positively correlated with intention to share one's results with parents and with siblings as well as the desire to know relatives' results. Additionally, females who had undertaken a genetics course at university had significantly greater intentions and desires than females who had not. Lower general educational attainment was related to a lower intention to share with siblings. Participants who were in a STEMM field had a greater desire to know their relatives' results. CONCLUSIONS: Participants' gender and prior genetics education were consistently related to their intentions to share WGS results with relatives and their desire to know relatives' results. Educational attainment was found to be positively correlated with intention to share with siblings. Being in a STEMM field was related to participants' desire to know their relatives' results. These findings indicate that gender and genetics education are particularly important influencers on young adults' stated sharing preferences. More research is required to examine the dependent variables studied to further understand their influence on attitudes to sharing WGS results. These findings are particularly interesting for information provision and support before genomic sequencing and post-results to improve the outcomes for individuals and their relatives.

Search strategies in structural bioinformatics.

Optimisation problems pervade structural bioinformatics. In this review, we describe recent work addressing a selection of bioinformatics challenges. We begin with a discussion of research into protein structure comparison, and highlight the utility of Kolmogorov complexity as a measure of structural similarity. We then turn to research into de novo protein structure prediction, in which structures are generated from first principles. In this endeavour, there is a compromise between the detail of the model and the extent to which the conformational space of the protein can be sampled. We discuss some developments in this area, including off-lattice structure prediction using the great deluge algorithm. One strategy to reduce the size of the search space is to restrict the protein chain to sites on a regular lattice. In this context, we highlight the use of memetic algorithms, which combine genetic algorithms with local optimisation, to the study of simple protein models on the two-dimensional square lattice and the face-centred cubic lattice.

Idiotypic immune networks in mobile-robot control.

Jerne's idiotypic-network theory postulates that the immune response involves interantibody stimulation and suppression, as well as matching to antigens. The theory has proved the most popular artificial immune system (AIS) model for incorporation into behavior-based robotics, but guidelines for implementing idiotypic selection are scarce. Furthermore, the direct effects of employing the technique have not been demonstrated in the form of a comparison with nonidiotypic systems. This paper aims to address these issues. A method for integrating an idiotypic AIS network with a reinforcement-learning (RL)-based control system is described, and the mechanisms underlying antibody stimulation and suppression are explained in detail. Some hypotheses that account for the network advantage are put forward and tested using three systems with increasing idiotypic complexity. The basic RL, a simplified hybrid AIS-RL that implements idiotypic selection independently of derived concentration levels, and a full hybrid AIS-RL scheme are examined. The test bed takes the form of a simulated Pioneer robot that is required to navigate through maze worlds detecting and tracking door markers.

A new accuracy measure based on bounded relative error for time series forecasting.

Many accuracy measures have been proposed in the past for time series forecasting comparisons. However, many of these measures suffer from one or more issues such as poor resistance to outliers and scale dependence. In this paper, while summarising commonly used accuracy measures, a special review is made on the symmetric mean absolute percentage error. Moreover, a new accuracy measure called the Unscaled Mean Bounded Relative Absolute Error (UMBRAE), which combines the best features of various alternative measures, is proposed to address the common issues of existing measures. A comparative evaluation on the proposed and related measures has been made with both synthetic and real-world data. The results indicate that the proposed measure, with user selectable benchmark, performs as well as or better than other measures on selected criteria. Though it has been commonly accepted that there is no single best accuracy measure, we suggest that UMBRAE could be a good choice to evaluate forecasting methods, especially for cases where measures based on geometric mean of relative errors, such as the geometric mean relative absolute error, are preferred.

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

BACKGROUND: Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. METHODS: Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the 'receiver operating curve' (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). FINDINGS: 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723-0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739-0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755-0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755-0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759-0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. CONCLUSIONS: Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others.

Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets.

BACKGROUND AND OBJECTIVE: In human-machine (HM) hybrid control systems, human operator and machine cooperate to achieve the control objectives. To enhance the overall HM system performance, the discrete manual control task-load by the operator must be dynamically allocated in accordance with continuous-time fluctuation of psychophysiological functional status of the operator, so-called operator functional state (OFS). The behavior of the HM system is hybrid in nature due to the co-existence of discrete task-load (control) variable and continuous operator performance (system output) variable. METHODS: Petri net is an effective tool for modeling discrete event systems, but for hybrid system involving discrete dynamics, generally Petri net model has to be extended. Instead of using different tools to represent continuous and discrete components of a hybrid system, this paper proposed a method of fuzzy inference Petri nets (FIPN) to represent the HM hybrid system comprising a Mamdani-type fuzzy model of OFS and a logical switching controller in a unified framework, in which the task-load level is dynamically reallocated between the operator and machine based on the model-predicted OFS. Furthermore, this paper used a multi-model approach to predict the operator performance based on three electroencephalographic (EEG) input variables (features) via the Wang-Mendel (WM) fuzzy modeling method. The membership function parameters of fuzzy OFS model for each experimental participant were optimized using artificial bee colony (ABC) evolutionary algorithm. Three performance indices, RMSE, MRE, and EPR, were computed to evaluate the overall modeling accuracy. RESULTS: Experiment data from six participants are analyzed. The results show that the proposed method (FIPN with adaptive task allocation) yields lower breakdown rate (from 14.8% to 3.27%) and higher human performance (from 90.30% to 91.99%). CONCLUSION: The simulation results of the FIPN-based adaptive HM (AHM) system on six experimental participants demonstrate that the FIPN framework provides an effective way to model and regulate/optimize the OFS in HM hybrid systems composed of continuous-time OFS model and discrete-event switching controller.