Artificial Intelligence and Statistics

Artificial intelligence (AI) is intrinsically data-driven. It calls for the application of statistical concepts through human-machine collaboration during generation of data, development of algorithms, and evaluation of results. This paper discusses how such human-machine collaboration can be approached through the statistical concepts of population, question of interest, representativeness of training data, and scrutiny of results (PQRS). The PQRS workflow provides a conceptual framework for integrating statistical ideas with human input into AI products and research. These ideas include experimental design principles of randomization and local control as well as the principle of stability to gain reproducibility and interpretability of algorithms and data results. We discuss the use of these principles in the contexts of self-driving cars, automated medical diagnoses, and examples from the authors' collaborative research

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Use of m-Health Technology for Preventive Interventions to Tackle Cardiometabolic Conditions and Other Non-Communicable Diseases in Latin America- Challenges and Opportunities

In Latin America, cardiovascular disease (CVD) mortality rates will increase by an estimated 145% from 1990 to 2020. Several challenges related to social strains, inadequate public health infrastructure, and underfinanced healthcare systems make cardiometabolic conditions and non-communicable diseases (NCDs) difficult to prevent and control. On the other hand, the region has high mobile phone coverage, making mobile health (mHealth) particularly attractive to complement and improve strategies toward prevention and control of these conditions in low- and middle-income countries. In this article, we describe the experiences of three Centers of Excellence for prevention and control of NCDs sponsored by the National Heart, Lung, and Blood Institute with mHealth interventions to address cardiometabolic conditions and other NCDs in Argentina, Guatemala, and Peru. The nine studies described involved the design and implementation of complex interventions targeting providers, patients and the public. The rationale, design of the interventions, and evaluation of processes and outcomes of each of these studies are described, together with barriers and enabling factors associated with their implementation.Fil: Beratarrechea, Andrea Gabriela. Instituto de Efectividad Clínica y Sanitaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Diez Canseco, Francisco. Universidad Peruana Cayetano Heredia; PerúFil: Irazola, Vilma. Instituto de Efectividad Clínica y Sanitaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Miranda, Jaime. Universidad Peruana Cayetano Heredia; PerúFil: Ramirez Zea, Manuel. Institute of Nutrition of Central America and Panama; GuatemalaFil: Rubinstein, Adolfo Luis. Instituto de Efectividad Clínica y Sanitaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

PRImary care Streptococcal Management (PRISM) study:In vitro study, diagnostic cohorts and a pragmatic adaptive randomised controlled trial with nested qualitative study and cost-effectiveness study

Background: Antibiotics are still prescribed to most patients attending primary care with acute sore throat, despite evidence that there is modest benefit overall from antibiotics. Targeting antibiotics using either clinical scoring methods or rapid antigen detection tests (RADTs) could help. However, there is debate about which groups of streptococci are important (particularly Lancefield groups C and G), and uncertainty about the variables that most clearly predict the presence of streptococci. Objective: This study aimed to compare clinical scores or RADTs with delayed antibiotic prescribing. Design: The study comprised a RADT in vitro study; two diagnostic cohorts to develop streptococcal scores (score 1; score 2); and, finally, an open pragmatic randomised controlled trial with nested qualitative and cost-effectiveness studies. Setting: The setting was UK primary care general practices. Participants: Participants were patients aged ≥ 3 years with acute sore throat. Interventions: An internet program randomised patients to targeted antibiotic use according to (1) delayed antibiotics (control group), (2) clinical score or (3) RADT used according to clinical score. Main outcome measures: The main outcome measures were self-reported antibiotic use and symptom duration and severity on seven-point Likert scales (primary outcome: mean sore throat/difficulty swallowing score in the first 2-4 days). Results: The IMI TestPack Plus Strep A (Inverness Medical, Bedford, UK) was sensitive, specific and easy to use. Lancefield group A/C/G streptococci were found in 40% of cohort 2 and 34% of cohort 1. A five-point score predicting the presence of A/C/G streptococci [FeverPAIN: Fever; Purulence; Attend rapidly (≤ 3 days); severe Inflammation; and No cough or coryza] had moderate predictive value (bootstrapped estimates of area under receiver operating characteristic curve: 0.73 cohort 1, 0.71 cohort 2) and identified a substantial number of participants at low risk of streptococcal infection. In total, 38% of cohort 1 and 36% of cohort 2 scored ≤ 1 for FeverPAIN, associated with streptococcal percentages of 13% and 18%, respectively. In an adaptive trial design, the preliminary score (score 1; n = 1129) was replaced by FeverPAIN (n = 631). For score 1, there were no significant differences between groups. For FeverPAIN, symptom severity was documented in 80% of patients, and was lower in the clinical score group than in the delayed prescribing group (-0.33; 95% confidence interval -0.64 to -0.02; p = 0.039; equivalent to one in three rating sore throat a slight rather than moderately bad problem), and a similar reduction was observed for the RADT group (-0.30; -0.61 to 0.00; p = 0.053). Moderately bad or worse symptoms resolved significantly faster (30%) in the clinical score group (hazard ratio 1.30; 1.03 to 1.63) but not the RADT group (1.11; 0.88 to 1.40). In the delayed group, 75/164 (46%) used antibiotics, and 29% fewer used antibiotics in the clinical score group (risk ratio 0.71; 0.50 to 0.95; p = 0.018) and 27% fewer in the RADT group (0.73; 0.52 to 0.98; p = 0.033). No significant differences in complications or reconsultations were found. The clinical score group dominated both other groups for both the cost/quality-adjusted life-years and cost/change in symptom severity analyses, being both less costly and more effective, and cost-effectiveness acceptability curves indicated the clinical score to be the most likely to be cost-effective from an NHS perspective. Patients were positive about RADTs. Health professionals' concerns about test validity, the time the test took and medicalising self-limiting illness lessened after using the tests. For both RADTs and clinical scores, there were tensions with established clinical experience. Conclusions: Targeting antibiotics using a clinical score (FeverPAIN) efficiently improves symptoms and reduces antibiotic use. RADTs used in combination with FeverPAIN provide no clear advantages over FeverPAIN alone, and RADTs are unlikely to be incorporated into practice until health professionals' concerns are met and they have experience of using them. Clinical scores also face barriers related to clinicians' perceptions of their utility in the face of experience. This study has demonstrated the limitation of using one data set to develop a clinical score. FeverPAIN, derived from two data sets, appears to be valid and its use improves outcomes, but diagnostic studies to confirm the validity of FeverPAIN in other data sets and settings are needed. Experienced clinicians need to identify barriers to the use of clinical scoring methods. Implementation studies that address perceived barriers in the use of FeverPAIN are needed

Smartphone-based safety planning and self-monitoring for suicidal patients: Rationale and study protocol of the CASPAR (Continuous Assessment for Suicide Prevention And Research) study

Background: It remains difficult to predict and prevent suicidal behaviour, despite growing understanding of the aetiology of suicidality. Clinical guidelines recommend that health care professionals develop a safety plan in collaboration with their high-risk patients, to lower the imminent risk of suicidal behaviour. Mobile health applications provide new opportunities for safety planning, and enable daily self-monitoring of suicide-related symptoms that may enhance safety planning. This paper presents the rationale and protocol of the Continuous Assessment for Suicide Prevention And Research (CASPAR) study. The aim of the study is two-fold: to evaluate the feasibility of mobile safety planning and daily mobile self-monitoring in routine care treatment for suicidal patients, and to conduct fundamental research on suicidal processes. Methods: The study is an adaptive single cohort design among 80 adult outpatients or day-care patients, with the main diagnosis of major depressive disorder or dysthymia, who have an increased risk for suicidal behaviours. There are three measurement points, at baseline, at 1 and 3 months after baseline. Patients are instructed to use their mobile safety plan when necessary and monitor their suicidal symptoms daily. Both these apps will be used in treatment with their clinician. Conclusion: The results from this study will provide insight into the feasibility of mobile safety planning and self-monitoring in treatment of suicidal patients. Furthermore, knowledge of the suicidal process will be enhanced, especially regarding the transition from suicidal ideation to behaviour

Virginia Commonwealth University Professional Bulletin

Professional programs bulletin for Virginia Commonwealth University for the academic year 2018-2019. It includes information on academic regulations, degree requirements, course offerings, faculty, academic calendar, and tuition and expenses for graduate programs

Biosensors for cardiac biomarkers detection: a review

The cardiovascular disease (CVD) is considered as a major threat to global health. Therefore, there is a growing demand for a range of portable, rapid and low cost biosensing devices for the detection of CVD. Biosensors can play an important role in the early diagnosis of CVD without having to rely on hospital visits where expensive and time-consuming laboratory tests are recommended. Over the last decade, many biosensors have been developed to detect a wide range of cardiac marker to reduce the costs for healthcare. One of the major challenges is to find a way of predicting the risk that an individual can suffer from CVD. There has been considerable interest in finding diagnostic and prognostic biomarkers that can be detected in blood and predict CVD risk. Of these, C-reactive protein (CRP) is the best known biomarker followed by cardiac troponin I or T (cTnI/T), myoglobin, lipoprotein-associated phospholipase A(2), interlukin-6 (IL-6), interlukin-1 (IL-1), low-density lipoprotein (LDL), myeloperoxidase (MPO) and tumor necrosis factor alpha (TNF-α) has been used to predict cardiovascular events. This review provides an overview of the available biosensor platforms for the detection of various CVD markers and considerations of future prospects for the technology are addressed