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Abstract Healthcare fraud, waste and abuse are costly problems that have huge impact on society. Traditional approaches to identify non-compliant claims rely on auditing strategies requiring trained professionals, or on machine learning methods requiring labelled data and possibly lacking interpretability. We present Clais, a collaborative artificial intelligence system for claims analysis. Clais automatically extracts human-interpretable rules from healthcare policy documents (0.72 F1-score), and it enables professionals to edit and validate the extracted rules through an intuitive user interface. Clais executes the rules on claim records to identify non-compliance: on this task Clais significantly outperforms two baseline machine learning models, and its median F1-score is 1.0 (IQR = 0.83 to 1.0) when executing the extracted rules, and 1.0 (IQR = 1.0 to 1.0) when executing the same rules after human curation. Professionals confirm through a user study the usefulness of Clais in making their workflow simpler and more effective.

Background Using reports of randomised trials of smoking cessation interventions as a test case, this study aimed to develop and evaluate machine learning (ML) algorithms for extracting information from study reports and predicting outcomes as part of the Human Behaviour-Change Project. It is the first of two linked papers, with the second paper reporting on further development of a prediction system. Methods Researchers manually annotated 70 items of information (‘entities’) in 512 reports of randomised trials of smoking cessation interventions covering intervention content and delivery, population, setting, outcome and study methodology using the Behaviour Change Intervention Ontology. These entities were used to train ML algorithms to extract the information automatically. The information extraction ML algorithm involved a named-entity recognition system using the ‘FLAIR’ framework. The manually annotated intervention, population, setting and study entities were used to develop a deep-learning algorithm using multiple layers of long-short-term-memory (LSTM) components to predict smoking cessation outcomes. Results The F1 evaluation score, derived from the false positive and false negative rates (range 0-1), for the information extraction algorithm averaged 0.42 across different types of entity (SD=0.22, range 0.05-0.88) compared with an average human annotator’s score of 0.75 (SD=0.15, range 0.38-1.00). The algorithm for assigning entities to study arms (e.g., intervention or control) was not successful. This initial ML outcome prediction algorithm did not outperform prediction based just on the mean outcome value or a linear regression model. Conclusions While some success was achieved in using ML to extract information from reports of randomised trials of smoking cessation interventions, we identified major challenges that could be addressed by greater standardisation in the way that studies are reported. Outcome prediction from smoking cessation studies may benefit from development of novel algorithms, e.g., using ontological information to inform ML (as reported in the linked paper (1)).

A series of mono- and bis-polyethylene glycol (PEG)-substituted BF2-azadipyrromethene fluorophores have been synthesized with emissions in the near-infrared region (700–800 nm) for the purpose of fluorescence guided intraoperative imaging; chiefly ureter imaging. The Bis-PEGylation of fluorophores resulted in higher aqueous fluorescence quantum yields, with PEG chain lengths of 2.9 to 4.6 kDa being optimal. Fluorescence ureter identification was possible in a rodent model with the preference for renal excretion notable through comparative fluorescence intensities from the ureters, kidneys and liver. Ureteral identification was also successfully performed in a larger animal porcine model under abdominal surgical conditions. Three tested doses of 0.5, 0.25 and 0.1 mg/kg all successfully identified fluorescent ureters within 20 min of administration which was sustained up to 120 min. 3-D emission heat map imaging allowed the spatial and temporal changes in intensity due to the distinctive peristaltic waves of urine being transferred from the kidneys to the bladder to be identified. As the emission of these fluorophores could be spectrally distinguished from the clinically-used perfusion dye indocyanine green, it is envisaged that their combined use could be a step towards intraoperative colour coding of different tissues.

Time analysis of the course of an infectious disease epidemic is a critical way to understand the dynamics of pathogen transmission and the effect of population scale interventions. Computational methods have been applied to the progression of the COVID-19 outbreak in five different countries (Ireland, Germany, UK, South Korea and Iceland) using their reported daily infection data. A Gaussian convolution smoothing function constructed a continuous epidemic line profile that was segmented into longitudinal time series of mathematically fitted individual logistic curves. The time series of fitted curves allowed comparison of disease progression with differences in decreasing daily infection numbers following the epidemic peak being of specific interest. A positive relationship between the rate of declining infections and countries with comprehensive COVID-19 testing regimes existed. Insight into different rates of decline infection numbers following the wave peak was also possible which could be a useful tool to guide the reopening of societies. In contrast, extended epidemic timeframes were recorded for those least prepared for large-scale testing and contact tracing. As many countries continue to struggle to implement population wide testing it is prudent to explore additional measures that could be employed. Comparative analysis of healthcare worker (HCW) infection data from Ireland shows it closely related to that of the entire population with respect to trends of daily infection numbers and growth rates over a 57-day period. With 31.6% of all test-confirmed infections in healthcare workers (all employees of healthcare facilities), they represent a concentrated 3% subset of the national population which if exhaustively tested (regardless of symptom status) could provide valuable information on disease progression in the entire population (or set). Mathematically, national population and HCWs can be viewed as a set and subset with significant influences on each other, with solidarity between both an essential ingredient for ending this crisis.

The Laplace mechanism is the workhorse of differential privacy, applied to many instances where numerical data is processed. However, the Laplace mechanism can return semantically impossible values, such as negative counts, due to its infinite support. There are two popular solutions to this: (i) bounding/capping the output values and (ii) bounding the mechanism support. In this paper, we show that bounding the mechanism support, while using the parameters of the standard Laplace mechanism, does not typically preserve differential privacy. We also present a robust method to compute the optimal mechanism parameters to achieve differential privacy in such a setting.

Abstract Background Behaviour change is key to addressing both the challenges facing human health and wellbeing and to promoting the uptake of research findings in health policy and practice. We need to make better use of the vast amount of accumulating evidence from behaviour change intervention (BCI) evaluations and promote the uptake of that evidence into a wide range of contexts. The scale and complexity of the task of synthesising and interpreting this evidence, and increasing evidence timeliness and accessibility, will require increased computer support. The Human Behaviour-Change Project (HBCP) will use Artificial Intelligence and Machine Learning to (i) develop and evaluate a ‘Knowledge System’ that automatically extracts, synthesises and interprets findings from BCI evaluation reports to generate new insights about behaviour change and improve prediction of intervention effectiveness and (ii) allow users, such as practitioners, policy makers and researchers, to easily and efficiently query the system to get answers to variants of the question ‘What works, compared with what, how well, with what exposure, with what behaviours (for how long), for whom, in what settings and why?’. Methods The HBCP will: a) develop an ontology of BCI evaluations and their reports linking effect sizes for given target behaviours with intervention content and delivery and mechanisms of action, as moderated by exposure, populations and settings; b) develop and train an automated feature extraction system to annotate BCI evaluation reports using this ontology; c) develop and train machine learning and reasoning algorithms to use the annotated BCI evaluation reports to predict effect sizes for particular combinations of behaviours, interventions, populations and settings; d) build user and machine interfaces for interrogating and updating the knowledge base; and e) evaluate all the above in terms of performance and utility. Discussion The HBCP aims to revolutionise our ability to synthesise, interpret and deliver evidence on behaviour change interventions that is up-to-date and tailored to user need and context. This will enhance the usefulness, and support the implementation of, that evidence.

Abstract: We present SPUD, a semantic environment for cataloging, exploring, integrating, understanding, processing and transforming urban information. A series of challenges are identified: namely, the heterogeneity of the domain and the impracticality of a common model, the volume of information and the number of data sets, the requirement for a low entry threshold to the system, the diversity of the input data, in terms of format, syntax and update frequency (streams vs static data), the complex data dependencies and the sensitivity of the information. We propose an approach for the incremental and continuous integration of static and streaming data, based on Semantic Web technologies and apply our technology to a traffic diagnosis scenario. We demonstrate our approach through a system operating on real data in Dublin and we show that semantic technologies can be used to obtain business results in an environment with hundreds of heterogeneous datasets coming from distributed data sources and spanning multiple domains. [Copyright &y& Elsevier]

Background   Using reports of randomised trials of smoking cessation interventions as a test case, this study aimed to develop and evaluate machine learning (ML) algorithms for extracting information from study reports and predicting outcomes as part of the Human Behaviour-Change Project. It is the first of two linked papers, with the second paper reporting on further development of a prediction system. Methods   Researchers manually annotated 70 items of information ('entities') in 512 reports of randomised trials of smoking cessation interventions covering intervention content and delivery, population, setting, outcome and study methodology using the Behaviour Change Intervention Ontology. These entities were used to train ML algorithms to extract the information automatically. The information extraction ML algorithm involved a named-entity recognition system using the 'FLAIR' framework. The manually annotated intervention, population, setting and study entities were used to develop a deep-learning algorithm using multiple layers of long-short-term-memory (LSTM) components to predict smoking cessation outcomes. Results   The F1 evaluation score, derived from the false positive and false negative rates (range 0-1), for the information extraction algorithm averaged 0.42 across different types of entity (SD=0.22, range 0.05-0.88) compared with an average human annotator's score of 0.75 (SD=0.15, range 0.38-1.00). The algorithm for assigning entities to study arms ( e.g. , intervention or control) was not successful. This initial ML outcome prediction algorithm did not outperform prediction based just on the mean outcome value or a linear regression model. Conclusions   While some success was achieved in using ML to extract information from reports of randomised trials of smoking cessation interventions, we identified major challenges that could be addressed by greater standardisation in the way that studies are reported. Outcome prediction from smoking cessation studies may benefit from development of novel algorithms, e.g. , using ontological information to inform ML (as reported in the linked paper 3 )., Competing Interests: Competing interests: RW and SM are unpaid directors of the Unlocking Behaviour Change Community Interest Company., (Copyright: © 2023 West R et al.)

A series of mono- and bis-polyethylene glycol (PEG)-substituted BF 2 -azadipyrromethene fluorophores have been synthesized with emissions in the near-infrared region (700-800 nm) for the purpose of fluorescence guided intraoperative imaging; chiefly ureter imaging. The Bis-PEGylation of fluorophores resulted in higher aqueous fluorescence quantum yields, with PEG chain lengths of 2.9 to 4.6 kDa being optimal. Fluorescence ureter identification was possible in a rodent model with the preference for renal excretion notable through comparative fluorescence intensities from the ureters, kidneys and liver. Ureteral identification was also successfully performed in a larger animal porcine model under abdominal surgical conditions. Three tested doses of 0.5, 0.25 and 0.1 mg/kg all successfully identified fluorescent ureters within 20 min of administration which was sustained up to 120 min. 3-D emission heat map imaging allowed the spatial and temporal changes in intensity due to the distinctive peristaltic waves of urine being transferred from the kidneys to the bladder to be identified. As the emission of these fluorophores could be spectrally distinguished from the clinically-used perfusion dye indocyanine green, it is envisaged that their combined use could be a step towards intraoperative colour coding of different tissues.

Background: Surgery is a major component of health-care provision. Operative intervention often employs minimally invasive approaches incorporating digital cameras creating a 'digital twin' of both intracorporeal appearances and operative performance. Video recordings provide richer detail than the traditional operative note and can couple with advanced computer technology to unlock new analytic capabilities capable of driving surgical advancement via quality improvement initiatives and new technology design. Surgical video is however an under-utilized technology resource, in part, because ownership along with broader issues including purpose, privacy, confidentiality, copyright and inclusion in outputs have been poorly considered using outdated categorisation., Method: A first principles perspective on operative video classification as a useful public interest resource enshrining fundamental stakeholder (patients, physicians, institutions, industry and society) rights, roles and responsibilities., Result: A facility of noble purpose, understandable to all, for fair, accountable, safe and transparent access to large volumes of anonymised surgical videos of intracorporeal operations that enables advances through cross-disciplinary research is proposed. Technology can be exploited to protect all relevant parties respecting both citizen data-rights and the special status doctor-patient relationship. Through general consensus, the capability can be understood, established and iterated to perfection., Conclusion: Overall we argue that new and specific classification of surgical video enables responsible curation and serves the public good better than the current model. Rather than being thought of as a bicycle where discrete ownership is ascribed, such data are better viewed as being more like a park, a regulated amenity we should preserve for better human life., Competing Interests: Declaration of competing interest RC receives speaker fees from Stryker Corporation, speaker fees from Stryker Corporation, Olympus Corporation and Ethicon/JNJ, consulting fees from Distalmotion, Touch Surgery and Palliare and holds research funding from Intutive Corp and Medtronic. consulting fees from Distal Motion and Touch Surgery and holds research funding from Intuitive Corp. PMA is a full-time employee of IBM Research, a division of IBM Corporation. IBM Corporation provides technical products and services world-wide to government, healthcare and life-sciences companies. RC and PMA together hold research funding from the Irish Government for work related to surgical video analysis which provides background to how this Perspective generated. NM has no conflicts of interest to declare in relation to this work., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Findings from randomized controlled trials (RCTs) of behaviour change interventions encode much of our knowledge on intervention efficacy under defined conditions. Predicting outcomes of novel interventions in novel conditions can be challenging, as can predicting differences in outcomes between different interventions or different conditions. To predict outcomes from RCTs, we propose a generic framework of combining the information from two sources - i) the instances (comprised of surrounding text and their numeric values) of relevant attributes, namely the intervention, setting and population characteristics of a study, and ii) abstract representation of the categories of these attributes themselves. We demonstrate that this way of encoding both the information about an attribute and its value when used as an embedding layer within a standard deep sequence modeling setup improves the outcome prediction effectiveness., (©2021 AMIA - All rights reserved.)

The wide availability of near infrared light sources in interventional medical imaging stacks enables non-invasive quantification of perfusion by using fluorescent dyes, typically Indocyanine Green (ICG). Due to their often leaky and chaotic vasculatures, intravenously administered ICG perfuses through cancerous tissues differently. We investigate here how a few characteristic values derived from the time series of fluorescence can be used in simple machine learning algorithms to distinguish benign lesions from cancers. These features capture the initial uptake of ICG in the colon, its peak fluorescence, and its early wash-out. By using simple, explainable algorithms we demonstrate, in clinical cases, that sensitivity (specificity) rates of over 95% (95%) for cancer classification can be achieved., (©2021 AMIA - All rights reserved.)

In this article, we provide an evidence-based primer of current tools and evolving concepts in the area of intraprocedural artificial intelligence (AI) methods in colonoscopy and laparoscopy as a 'procedure companion', with specific focus on colorectal cancer recognition and characterisation. These interventions are both likely beneficiaries from an impending rapid phase in technical and technological evolution. The domains where AI is most likely to impact are explored as well as the methodological pitfalls pertaining to AI methods. Such issues include the need for large volumes of data to train AI systems, questions surrounding false positive rates, explainability and interpretability as well as recent concerns surrounding instabilities in current deep learning (DL) models. The area of biophysics-inspired models, a potential remedy to some of these pitfalls, is explored as it could allow our understanding of the fundamental physiological differences between tissue types to be exploited in real time with the help of computer-assisted interpretation. Right now, such models can include data collected from dynamic fluorescence imaging in surgery to characterise lesions by their biology reducing the number of cases needed to build a reliable and interpretable classification system. Furthermore, instead of focussing on image-by-image analysis, such systems could analyse in a continuous fashion, more akin to how we view procedures in real life and make decisions in a manner more comparable to human decision-making. Synergistical approaches can ensure AI methods usefully embed within practice thus safeguarding against collapse of this exciting field of investigation as another 'boom and bust' cycle of AI endeavour.

Time analysis of the course of an infectious disease epidemic is a critical way to understand the dynamics of pathogen transmission and the effect of population scale interventions. Computational methods have been applied to the progression of the COVID-19 outbreak in five different countries (Ireland, Germany, UK, South Korea and Iceland) using their reported daily infection data. A Gaussian convolution smoothing function constructed a continuous epidemic line profile that was segmented into longitudinal time series of mathematically fitted individual logistic curves. The time series of fitted curves allowed comparison of disease progression with differences in decreasing daily infection numbers following the epidemic peak being of specific interest. A positive relationship between the rate of declining infections and countries with comprehensive COVID-19 testing regimes existed. Insight into different rates of decline infection numbers following the wave peak was also possible which could be a useful tool to guide the reopening of societies. In contrast, extended epidemic timeframes were recorded for those least prepared for large-scale testing and contact tracing. As many countries continue to struggle to implement population wide testing it is prudent to explore additional measures that could be employed. Comparative analysis of healthcare worker (HCW) infection data from Ireland shows it closely related to that of the entire population with respect to trends of daily infection numbers and growth rates over a 57-day period. With 31.6% of all test-confirmed infections in healthcare workers (all employees of healthcare facilities), they represent a concentrated 3% subset of the national population which if exhaustively tested (regardless of symptom status) could provide valuable information on disease progression in the entire population (or set). Mathematically, national population and HCWs can be viewed as a set and subset with significant influences on each other, with solidarity between both an essential ingredient for ending this crisis., Competing Interests: The authors have declared that no competing interests exist. Commercial affiliations of the authors do not alter adherence to PLOS ONE policies on sharing data and materials.

Background: Artificial Intelligence (AI) is transforming the process of scientific research. AI, coupled with availability of large datasets and increasing computational power, is accelerating progress in areas such as genetics, climate change and astronomy [NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning, Vancouver, Canada; Hausen R, Robertson BE. Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. Astrophys J Suppl Ser. 2020;248:20; Dias R, Torkamani A. AI in clinical and genomic diagnostics. Genome Med. 2019;11:70.]. The application of AI in behavioral science is still in its infancy and realizing the promise of AI requires adapting current practices., Purposes: By using AI to synthesize and interpret behavior change intervention evaluation report findings at a scale beyond human capability, the HBCP seeks to improve the efficiency and effectiveness of research activities. We explore challenges facing AI adoption in behavioral science through the lens of lessons learned during the Human Behaviour-Change Project (HBCP)., Methods: The project used an iterative cycle of development and testing of AI algorithms. Using a corpus of published research reports of randomized controlled trials of behavioral interventions, behavioral science experts annotated occurrences of interventions and outcomes. AI algorithms were trained to recognize natural language patterns associated with interventions and outcomes from the expert human annotations. Once trained, the AI algorithms were used to predict outcomes for interventions that were checked by behavioral scientists., Results: Intervention reports contain many items of information needing to be extracted and these are expressed in hugely variable and idiosyncratic language used in research reports to convey information makes developing algorithms to extract all the information with near perfect accuracy impractical. However, statistical matching algorithms combined with advanced machine learning approaches created reasonably accurate outcome predictions from incomplete data., Conclusions: AI holds promise for achieving the goal of predicting outcomes of behavior change interventions, based on information that is automatically extracted from intervention evaluation reports. This information can be used to train knowledge systems using machine learning and reasoning algorithms., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society of Behavioral Medicine.)

This paper describes our approach to construct a scalable system for unsupervised information extraction from the behaviour change intervention literature. Due to the many different types of attribute to be extracted, we adopt a passage retrieval based framework that provides the most likely value for an attribute. Our proposed method is capable of addressing variable length passage sizes and different validation criteria for the extracted values corresponding to each attribute to be found. We evaluate our approach by constructing a manually annotated ground-truth from a set of 50 research papers with reported studies on smoking cessation.

Patient-Centric Care requires comprehensive visibility into the strengths and vulnerabilities of individuals and populations. The systems involved in Patient-Centric Care are numerous and heterogeneous, span medical, behavioral and social domains and must be coordinated across government and NGO stakeholders in Health Care, Social Care and more. We present a system, based on Linked Data technologies, taking first steps in making this cross-domain information accessible and fit-for-use, using minimal structure and open vocabularies. We evaluate our system through user studies.