An Ensemble Model of QSAR Tools for Regulatory Risk Assessment

Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa (κ): 0.63 and 0.62] for both the datasets. The ROC curves demonstrate the utility of the cut-off feature in the predictive ability of the ensemble model. This feature provides an additional control to the regulators in grading a chemical based on the severity of the toxic endpoint under study

Chapter 7: Grading a Body of Evidence on Diagnostic Tests

10.1007/s11606-012-2021-9Journal of General Internal Medicine27SUPPL.1S47-S55JGIM

Evaluating an Evidence-Based Suicide Risk Assessment Intervention for an Inpatient Mental Health Hospital Unit

Suicide is a worldwide problem that claims over 800,000 lives annually (NIMH, 2017; WHO, 2017). Almost one third of mental health professionals feel they have received inadequate training on suicide prevention (Jahn, Quinnett, & Ries, 2016). Asking mental health clients to agree to a no-suicide contract is a widely-used practice in the inpatient mental health care setting but lacks efficacy and can have ethical implications (Bryan et al., 2017). The NGASR was selected from a variety of suicide risk assessment tools for incorporation into a public inpatient mental health hospital unit in the Midwestern U.S.A. The NGASR is unique in that it describes evidence-based variables known to increase a person’s suicide risk, does not coerce mental health clients, and attempts to help nurses improve their clinical judgment when assessing suicide risk (Cutcliffe & Barker, 2004). The EBPI model was used to guide this DNP project, which asked “Among nurses working with mental health clients in the inpatient mental health hospital setting (P) how does the use of the NGASR (I) compared to the use of a no-suicide contract (C) impact nursing practice and the quality of care for mental health clients (O) over a four-month period of time? (T).” Twenty-two out of sixty nurses (36.7%) completed the survey at the end of the DNP project. Nurses felt the process of changing to the NGASR was neutral (31.82%) to negative (50%) with statements that the NGASR was more “time-consuming” and screened more mental health clients as a “high risk” for suicide than the no-suicide contract. Nurses provided neutral (13.64%) to positive (59.09%) responses when asked how the NGASR changed their practice and mental health client care. Nurses reported the NGASR increased critical thinking, awareness, and insight into the mental health clients’ problems and history. During the two months of NGASR incorporation the use and cost of safety sitters decreased by 44.44% and vi 76.76% ($10,897.71) respectively. It is recommended the NGASR undergo further incorporation and study on inpatient mental health hospital units to determine if utilizing the EHR is able to mitigate nurses’ perception of increased workload

Integrating artificial intelligence into an ophthalmologist’s workflow: obstacles and opportunities

Introduction: Demand in clinical services within the field of ophthalmology is predicted to rise over the future years. Artificial intelligence, in particular, machine learning-based systems, have demonstrated significant potential in optimizing medical diagnostics, predictive analysis, and management of clinical conditions. Ophthalmology has been at the forefront of this digital revolution, setting precedents for integration of these systems into clinical workflows. Areas covered: This review discusses integration of machine learning tools within ophthalmology clinical practices. We discuss key issues around ethical consideration, regulation, and clinical governance. We also highlight challenges associated with clinical adoption, sustainability, and discuss the importance of interoperability. Expert opinion: Clinical integration is considered one of the most challenging stages within the implementation process. Successful integration necessitates a collaborative approach from multiple stakeholders around a structured governance framework, with emphasis on standardization across healthcare providers and equipment and software developers

Interpretive analysis of 85 systematic reviews suggests narrative syntheses and meta-analyses are incommensurate in argumentation

Introduction. Using Toulmin’s argumentation theory, we analysed the texts of systematic reviews in the area of workplace health promotion to explore differences in the modes of reasoning embedded in reports of narrative synthesis as compared to reports of meta-analysis. Methods. We used framework synthesis, grounded theory and cross-case analysis methods to analyse 85 systematic reviews addressing intervention effectiveness in workplace health promotion. Results. Two core categories, or ‘modes of reasoning’, emerged to frame the contrast between narrative synthesis and meta-analysis: practical-configurational reasoning in narrative synthesis (‘what is going on here? what picture emerges?’) and inferential-predictive reasoning in meta-analysis (‘does it work, and how well? will it work again?’). Modes of reasoning examined quality and consistency of the included evidence differently. Meta-analyses clearly distinguished between warrant and claim, whereas narrative syntheses often presented joint warrant-claims. Conclusion. Narrative syntheses and meta-analyses represent different modes of reasoning. Systematic reviewers are likely to be addressing research questions in different ways with each method. It is important to consider narrative synthesis in its own right as a method and to develop specific quality criteria and understandings of how it is done, not merely as a complement to, or second-best option for, meta-analysis

ARIA 2016 : Care pathways implementing emerging technologies for predictive medicine in rhinitis and asthma across the life cycle

European Innovation Partnership on Active and Healthy Ageing Reference Site MACVIA-France, EU Structural and Development Fund Languedoc-Roussillon, ARIA.Peer reviewedPublisher PD

The reliability and validity of multiple mini interviews (MMIs) in values based recruitment to nursing, midwifery and paramedic practice: Findings from an evaluation study

Background: Universities in the United Kingdom (UK) are required to incorporate values based recruitment (VBR) into their healthcare student selection processes. This reflects an international drive to strengthen the quality of healthcare service provision. This paper presents novel findings in relation to the reliability and predictive validity of multiple mini interviews (MMIs); one approach to VBR widely being employed by universities. Objectives: To examine the reliability (internal consistency) and predictive validity of MMIs using end of Year One practice outcomes of under-graduate pre-registration adult, child, mental health nursing, midwifery and paramedic practice students. Design: Cross-discipline evaluation study. Setting: One university in the United Kingdom. Participants: Data were collected in two streams: applicants to A) The September 2014 and 2015 Midwifery Studies programmes; B) September 2015 adult; Child and Mental Health Nursing and Paramedic Practice programmes. Fifty-seven midwifery students commenced their programme in 2014 and 69 in 2015; 47 and 54 agreed to participate and completed Year One respectively. 333 healthcare students commenced their programmes in September 2015. Of these, 281 agreed to participate and completed their first year (180 adult, 33 child and 34 mental health nursing and 34 paramedic practice students). Methods: Stream A featured a seven station four-minute model with one interviewer at each station and in Stream B a six station model was employed. Cronbach’s alpha was used to assess MMI station internal consistency and Pearson’s moment correlation co-efficient to explore associations between participants’ admission MMI score and end of Year one clinical practice outcomes (OSCE and mentor grading). Results: Stream A: Significant correlations are reported between midwifery applicant’s MMI scores and end of Year One practice outcomes. A multivariate linear regression model demonstrated that MMI score significantly predicted end of Year One practice outcomes controlling for age and academic entry level: coefficients 0.195 (p = 0.002) and 0.116 (p = 0.002) for OSCE and mentor grading respectively. In Stream B no significant correlations were found between MMI score and practice outcomes measured by mentor grading. Internal consistency for each MMI station was ‘excellent’ with values ranging from 0.966–0.974 across Streams A and B. Conclusion: This novel, cross-discipline study shows that MMIs are reliable VBR tools which have predictive validity when a seven station model is used. These data are important given the current international use of different MMI models in healthcare student selection processes