A System for Accessible Artificial Intelligence

While artificial intelligence (AI) has become widespread, many commercial AI systems are not yet accessible to individual researchers nor the general public due to the deep knowledge of the systems required to use them. We believe that AI has matured to the point where it should be an accessible technology for everyone. We present an ongoing project whose ultimate goal is to deliver an open source, user-friendly AI system that is specialized for machine learning analysis of complex data in the biomedical and health care domains. We discuss how genetic programming can aid in this endeavor, and highlight specific examples where genetic programming has automated machine learning analyses in previous projects.Comment: 14 pages, 5 figures, submitted to Genetic Programming Theory and Practice 2017 worksho

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Identification and location of hot and cold spots of treated prevalence of depression in Catalonia (Spain)

Abstract Background Spatial analysis is a relevant set of tools for studying the geographical distribution of diseases, although its methods and techniques for analysis may yield very different results. A new hybrid approach has been applied to the spatial analysis of treated prevalence of depression in Catalonia (Spain) according to the following descriptive hypotheses: 1) spatial clusters of treated prevalence of depression (hot and cold spots) exist and, 2) these clusters are related to the administrative divisions of mental health care (catchment areas) in this region. Methods In this ecological study, morbidity data per municipality have been extracted from the regional outpatient mental health database (CMBD-SMA) for the year 2009. The second level of analysis mapped small mental health catchment areas or groups of municipalities covered by a single mental health community centre. Spatial analysis has been performed using a Multi-Objective Evolutionary Algorithm (MOEA) which identified geographical clusters (hot spots and cold spots) of depression through the optimization of its treated prevalence. Catchment areas, where hot and cold spots are located, have been described by four domains: urbanicity, availability, accessibility and adequacy of provision of mental health care. Results MOEA has identified 6 hot spots and 4 cold spots of depression in Catalonia. Our results show a clear spatial pattern where one cold spot contributed to define the exact location, shape and borders of three hot spots. Analysing the corresponding domain values for the identified hot and cold spots no common pattern has been detected. Conclusions MOEA has effectively identified hot/cold spots of depression in Catalonia. However these hot/cold spots comprised municipalities from different catchment areas and we could not relate them to the administrative distribution of mental care in the region. By combining the analysis of hot/cold spots, a better statistical and operational-based visual representation of the geographical distribution is obtained. This technology may be incorporated into Decision Support Systems to enhance local evidence-informed policy in health system research.</p

GP-HD: Using Genetic Programming to Generate Dynamical Systems Models for Health Care

The huge wealth of data in the health domain can be exploited to create models that predict development of health states over time. Temporal learning algorithms are well suited to learn relationships between health states and make predictions about their future developments. However, these algorithms: (1) either focus on learning one generic model for all patients, providing general insights but often with limited predictive performance, or (2) learn individualized models from which it is hard to derive generic concepts. In this paper, we present a middle ground, namely parameterized dynamical systems models that are generated from data using a Genetic Programming (GP) framework. A fitness function suitable for the health domain is exploited. An evaluation of the approach in the mental health domain shows that performance of the model generated by the GP is on par with a dynamical systems model developed based on domain knowledge, significantly outperforms a generic Long Term Short Term Memory (LSTM) model and in some cases also outperforms an individualized LSTM model

Using machine learning methods to determine a typology of patients with HIV-HCV infection to be treated with antivirals

Several European countries have established criteria for prioritising initiation of treatment in patients infected with the hepatitis C virus (HCV) by grouping patients according to clinical characteristics. Based on neural network techniques, our objective was to identify those factors for HIV/HCV co-infected patients (to which clinicians have given careful consideration before treatment uptake) that have not being included among the prioritisation criteria. This study was based on the Spanish HERACLES cohort (NCT02511496) (April-September 2015, 2940 patients) and involved application of different neural network models with different basis functions (product-unit, sigmoid unit and radial basis function neural networks) for automatic classification of patients for treatment. An evolutionary algorithm was used to determine the architecture and estimate the coefficients of the model. This machine learning methodology found that radial basis neural networks provided a very simple model in terms of the number of patient characteristics to be considered by the classifier (in this case, six), returning a good overall classification accuracy of 0.767 and a minimum sensitivity (for the classification of the minority class, untreated patients) of 0.550. Finally, the area under the ROC curve was 0.802, which proved to be exceptional. The parsimony of the model makes it especially attractive, using just eight connections. The independent variable "recent PWID" is compulsory due to its importance. The simplicity of the model means that it is possible to analyse the relationship between patient characteristics and the probability of belonging to the treated group

The usefulness of rapid diagnostic tests in the new context of low malaria transmission in zanzibar.

BACKGROUND\ud \ud We assessed if histidine-rich-protein-2 (HRP2) based rapid diagnostic test (RDT) remains an efficient tool for Plasmodium falciparum case detection among fever patients in Zanzibar and if primary health care workers continue to adhere to RDT results in the new epidemiological context of low malaria transmission. Further, we evaluated the performance of RDT within the newly adopted integrated management of childhood illness (IMCI) algorithm in Zanzibar.\ud \ud METHODS AND FINDINGS\ud \ud We enrolled 3890 patients aged ≥2 months with uncomplicated febrile illness in this health facility based observational study conducted in 12 primary health care facilities in Zanzibar, between May-July 2010. One patient had an inconclusive RDT result. Overall 121/3889 (3.1%) patients were RDT positive. The highest RDT positivity rate, 32/528 (6.1%), was found in children aged 5-14 years. RDT sensitivity and specificity against PCR was 76.5% (95% CI 69.0-83.9%) and 99.9% (95% CI 99.7-100%), and against blood smear microscopy 78.6% (95% CI 70.8-85.1%) and 99.7% (95% CI 99.6-99.9%), respectively. All RDT positive, but only 3/3768 RDT negative patients received anti-malarial treatment. Adherence to RDT results was thus 3887/3889 (99.9%). RDT performed well in the IMCI algorithm with equally high adherence among children <5 years as compared with other age groups.\ud \ud CONCLUSIONS\ud \ud The sensitivity of HRP-2 based RDT in the hands of health care workers compared with both PCR and microscopy for P. falciparum case detection was relatively low, whereas adherence to test results with anti-malarial treatment was excellent. Moreover, the results provide evidence that RDT can be reliably integrated in IMCI as a tool for improved childhood fever management. However, the relatively low RDT sensitivity highlights the need for improved quality control of RDT use in primary health care facilities, but also for more sensitive point-of-care malaria diagnostic tools in the new epidemiological context of low malaria transmission in Zanzibar.\ud \ud TRIAL REGISTRATION\ud \ud ClinicalTrials.gov NCT01002066