A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Variation in compulsory psychiatric inpatient admission in England:a cross-sectional, multilevel analysis

Background: Rates of compulsory admission have increased in England in recent decades, and this trend is accelerating. Studying variation in rates between people and places can help identify modifiable causes. Objectives: To quantify and model variances in the rate of compulsory admission in England at different spatial levels and to assess the extent to which this was explained by characteristics of people and places. Design: Cross-sectional analysis using multilevel statistical modelling. Setting: England, including 98% of Census lower layer super output areas (LSOAs), 95% of primary care trusts (PCTs), 93% of general practices and all 69 NHS providers of specialist mental health services. Participants: 1,287,730 patients. Main outcome measure: The study outcome was compulsory admission, defined as time spent in an inpatient mental illness bed subject to the Mental Health Act (2007) in 2010/11. We excluded patients detained under sections applying to emergency assessment only (including those in places of safety), guardianship or supervision of community treatment. The control group comprised all other users of specialist mental health services during the same period. Data sources: The Mental Health Minimum Data Set (MHMDS). Data on explanatory variables, characterising each of the spatial levels in the data set, were obtained from a wide range of sources, and were linked using MHMDS identifiers. Results: A total of 3.5% of patients had at least one compulsory admission in 2010/11. Of (unexplained) variance in the null model, 84.5% occurred between individuals. Statistically significant variance occurred between LSOAs [6.7%, 95% confidence interval (CI) 6.2% to 7.2%] and provider trusts (6.9%, 95% CI 4.3% to 9.5%). Variances at these higher levels remained statistically significant even after adjusting for a large number of explanatory variables, which together explained only 10.2% of variance in the study outcome. The number of provider trusts whose observed rate of compulsory admission differed from the model average to a statistically significant extent fell from 45 in the null model to 20 in the fully adjusted model. We found statistically significant associations between compulsory admission and age, gender, ethnicity, local area deprivation and ethnic density. There was a small but statistically significant association between (higher) bed occupancy and compulsory admission, but this was subsequently confounded by other covariates. Adjusting for PCT investment in mental health services did not improve model fit in the fully adjusted models. Conclusions: This was the largest study of compulsory admissions in England. While 85% of the variance in this outcome occurred between individuals, statistically significant variance (around 7% each) occurred between places (LSOAs) and provider trusts. This higher-level variance in compulsory admission remained largely unchanged even after adjusting for a large number of explanatory variables. We were constrained by data available to us, and therefore our results must be interpreted with caution. We were also unable to consider many hypotheses suggested by the service users, carers and professionals who we consulted. There is an imperative to develop and evaluate interventions to reduce compulsory admission rates. This requires further research to extend our understanding of the reasons why these rates remain so high. Funding: The National Institute for Health Research Health Services and Delivery Research programme

Adaptation to criticality through organizational invariance in embodied agents

Many biological and cognitive systems do not operate deep within one or other regime of activity. Instead, they are poised at critical points located at phase transitions in their parameter space. The pervasiveness of criticality suggests that there may be general principles inducing this behaviour, yet there is no well-founded theory for understanding how criticality is generated at a wide span of levels and contexts. In order to explore how criticality might emerge from general adaptive mechanisms, we propose a simple learning rule that maintains an internal organizational structure from a specific family of systems at criticality. We implement the mechanism in artificial embodied agents controlled by a neural network maintaining a correlation structure randomly sampled from an Ising model at critical temperature. Agents are evaluated in two classical reinforcement learning scenarios: the Mountain Car and the Acrobot double pendulum. In both cases the neural controller appears to reach a point of criticality, which coincides with a transition point between two regimes of the agent's behaviour. These results suggest that adaptation to criticality could be used as a general adaptive mechanism in some circumstances, providing an alternative explanation for the pervasive presence of criticality in biological and cognitive systems.Comment: arXiv admin note: substantial text overlap with arXiv:1704.0525

Theoretical Interpretations and Applications of Radial Basis Function Networks

Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains

Enhanced motivational interviewing for reducing weight and increasing physical activity in adults with high cardiovascular risk: the MOVE IT three-arm RCT.

BACKGROUND: Motivational interviewing (MI) enhanced with behaviour change techniques (BCTs) and deployed by health trainers targeting multiple risk factors for cardiovascular disease (CVD) may be more effective than interventions targeting a single risk factor. OBJECTIVES: The clinical effectiveness and cost-effectiveness of an enhanced lifestyle motivational interviewing intervention for patients at high risk of CVD in group settings versus individual settings and usual care (UC) in reducing weight and increasing physical activity (PA) were tested. DESIGN: This was a three-arm, single-blind, parallel randomised controlled trial. SETTING: A total of 135 general practices across all 12 South London Clinical Commissioning Groups were recruited. PARTICIPANTS: A total of 1742 participants aged 40-74 years with a ≥ 20.0% risk of a CVD event in the following 10 years were randomised. INTERVENTIONS: The intervention was designed to integrate MI and cognitive-behavioural therapy (CBT), delivered by trained healthy lifestyle facilitators in 10 sessions over 1 year, in group or individual format. The control group received UC. RANDOMISATION: Simple randomisation was used with computer-generated randomisation blocks. In each block, 10 participants were randomised to the group, individual or UC arm in a 4 : 3 : 3 ratio. Researchers were blind to the allocation. MAIN OUTCOME MEASURES: The primary outcomes are change in weight (kg) from baseline and change in PA (average number of steps per day over 1 week) from baseline at the 24-month follow-up, with an interim follow-up at 12 months. An economic evaluation estimates the relative cost-effectiveness of each intervention. Secondary outcomes include changes in low-density lipoprotein cholesterol and CVD risk score. RESULTS: The mean age of participants was 69.75 years (standard deviation 4.11 years), 85.5% were male and 89.4% were white. At the 24-month follow-up, the group and individual intervention arms were not more effective than UC in increasing PA [mean 70.05 steps, 95% confidence interval (CI) -288 to 147.9 steps, and mean 7.24 steps, 95% CI -224.01 to 238.5 steps, respectively] or in reducing weight (mean -0.03 kg, 95% CI -0.49 to 0.44 kg, and mean -0.42 kg, 95% CI -0.93 to 0.09 kg, respectively). At the 12-month follow-up, the group and individual intervention arms were not more effective than UC in increasing PA (mean 131.1 steps, 95% CI -85.28 to 347.48 steps, and mean 210.22 steps, 95% CI -19.46 to 439.91 steps, respectively), but there were reductions in weight for the group and individual intervention arms compared with UC (mean -0.52 kg, 95% CI -0.90 to -0.13 kg, and mean -0.55 kg, 95% CI -0.95 to -0.14 kg, respectively). The group intervention arm was not more effective than the individual intervention arm in improving outcomes at either follow-up point. The group and individual interventions were not cost-effective. CONCLUSIONS: Enhanced MI, in group or individual formats, targeted at members of the general population with high CVD risk is not effective in reducing weight or increasing PA compared with UC. Future work should focus on ensuring objective evidence of high competency in BCTs, identifying those with modifiable factors for CVD risk and improving engagement of patients and primary care. TRIAL REGISTRATION: Current Controlled Trials ISRCTN84864870. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 23, No. 69. See the NIHR Journals Library website for further project information. This research was part-funded by the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London

Clinical effectiveness and cost-effectiveness of cognitive behavioural therapy as an adjunct to pharmacotherapy for treatment-resistant depression in primary care: the CoBalT randomised controlled trial

Background: Only one-third of patients with depression respond fully to treatment with antidepressant medication. However, there is little robust evidence to guide the management of those whose symptoms are 'treatment resistant'.<p></p> Objective: The CoBalT trial examined the clinical effectiveness and cost-effectiveness of cognitive behavioural therapy (CBT) as an adjunct to usual care (including pharmacotherapy) for primary care patients with treatment-resistant depression (TRD) compared with usual care alone.<p></p> Design: Pragmatic, multicentre individually randomised controlled trial with follow-up at 3, 6, 9 and 12 months. A subset took part in a qualitative study investigating views and experiences of CBT, reasons for completing/not completing therapy, and usual care for TRD.<p></p> Setting: General practices in Bristol, Exeter and Glasgow, and surrounding areas.<p></p> Participants: Patients aged 18-75 years who had TRD [on antidepressants for 6 weeks, had adhered to medication, Beck Depression Inventory, 2nd version (BDI-II) score of 14 and fulfilled the International Classification of Diseases and Related Health Problems, Tenth edition criteria for depression]. Individuals were excluded who (1) had bipolar disorder/psychosis or major alcohol/substance abuse problems; (2) were unable to complete the questionnaires; or (3) were pregnant, as were those currently receiving CBT/other psychotherapy/secondary care for depression, or who had received CBT in the past 3 years.<p></p> Interventions: Participants were randomised, using a computer-generated code, to usual care or CBT (12-18 sessions) in addition to usual care.<p></p> Main outcome measures: The primary outcome was 'response', defined as 50% reduction in depressive symptoms (BDI-II score) at 6 months compared with baseline. Secondary outcomes included BDI-II score as a continuous variable, remission of symptoms (BDI-II score of < 10), quality of life, anxiety and antidepressant use at 6 and 12 months. Data on health and social care use, personal costs, and time off work were collected at 6 and 12 months. Costs from these three perspectives were reported using a cost-consequence analysis. A cost-utility analysis compared health and social care costs with quality adjusted life-years.<p></p> Results: A total of 469 patients were randomised (intervention: n = 234; usual care: n = 235), with 422 participants (90%) and 396 (84%) followed up at 6 and 12 months. Ninety-five participants (46.1%) in the intervention group met criteria for 'response' at 6 months compared with 46 (21.6%) in the usual-care group {odds ratio [OR] 3.26 [95% confidence interval (CI) 2.10 to 5.06], p < 0.001}. In repeated measures analyses using data from 6 and 12 months, the OR for 'response' was 2.89 (95% CI 2.03 to 4.10, p < 0.001) and for a secondary 'remission' outcome (BDI-II score of < 10) 2.74 (95% CI 1.82 to 4.13, p < 0.001). The mean cost of CBT per participant was £910, the incremental health and social care cost £850, the incremental QALY gain 0.057 and incremental cost-effectiveness ratio £14,911. Forty participants were interviewed. Patients described CBT as challenging but helping them to manage their depression; listed social, emotional and practical reasons for not completing treatment; and described usual care as mainly taking medication.<p></p> Conclusions: Among patients who have not responded to antidepressants, augmenting usual care with CBT is effective in reducing depressive symptoms, and these effects, including outcomes reflecting remission, are maintained over 12 months. The intervention was cost-effective based on the National Institute for Health and Care Excellence threshold. Patients may experience CBT as difficult but effective. Further research should evaluate long-term effectiveness, as this would have major implications for the recommended treatment of depression.<p></p&gt

Interoperable services based on activity monitoring in ambient assisted living environments

Ambient Assisted Living (AAL) is considered as the main technological solution that will enable the aged and people in recovery to maintain their independence and a consequent high quality of life for a longer period of time than would otherwise be the case. This goal is achieved by monitoring human’s activities and deploying the appropriate collection of services to set environmental features and satisfy user preferences in a given context. However, both human monitoring and services deployment are particularly hard to accomplish due to the uncertainty and ambiguity characterising human actions, and heterogeneity of hardware devices composed in an AAL system. This research addresses both the aforementioned challenges by introducing 1) an innovative system, based on Self Organising Feature Map (SOFM), for automatically classifying the resting location of a moving object in an indoor environment and 2) a strategy able to generate context-aware based Fuzzy Markup Language (FML) services in order to maximize the users’ comfort and hardware interoperability level. The overall system runs on a distributed embedded platform with a specialised ceiling- mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels, to detect specific events such as potential falls and to deploy the right sequence of fuzzy services modelled through FML for supporting people in that particular context. Experimental results show less than 20% classification error in monitoring human activities and providing the right set of services, showing the robustness of our approach over others in literature with minimal power consumption