Semi-Automated evidence synthesis in health psychology: current methods and future prospects

Automation tools to support undertaking scoping reviews

OBJECTIVE

This paper describes several automation tools and software that can be considered during evidence synthesis projects and provides guidance for their integration in the conduct of scoping reviews.

STUDY DESIGN AND SETTING

The guidance presented in this work is adapted from the results of a scoping review and consultations with the JBI Scoping Review Methodology group.

RESULTS

This paper describes several reliable, validated automation tools and software that can be used to enhance the conduct of scoping reviews. Developments in the automation of systematic reviews, and more recently scoping reviews, are continuously evolving. We detail several helpful tools in order of the key steps recommended by the JBI's methodological guidance for undertaking scoping reviews including team establishment, protocol development, searching, de-duplication, screening titles and abstracts, data extraction, data charting, and report writing. While we include several reliable tools and software that can be used for the automation of scoping reviews, there are some limitations to the tools mentioned. For example, some are available in English only and their lack of integration with other tools results in limited interoperability.

CONCLUSION

This paper highlighted several useful automation tools and software programs to use in undertaking each step of a scoping review. This guidance has the potential to inform collaborative efforts aiming at the development of evidence informed, integrated automation tools and software packages for enhancing the conduct of high-quality scoping reviews.

Applying the Non-adoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) framework to evaluate automated evidence synthesis in health behaviour change

Automated tools to speed up the process of evidence synthesis are increasingly apparent within health behaviour research. This brief review explores the potential of the Non-adoption, Abandonment, Scale-up, Spread and Sustainability framework for supporting automated evidence synthesis in health behaviour change by applying it to the ongoing Human Behaviour-Change Project, which aims to revolutionize evidence synthesis within behaviour change intervention research. To increase the relevance of NASSS for health behaviour change, we recommend i) terminology changes ('condition' to 'behaviour' and 'patient' to 'end user') and ii) that it is used prospectively address complexities iteratively. We draw conclusions about i) the need to specify the organizations that will use the technology, ii) identifying what to do if interdependencies fail and iii) even though we have focused on automated evidence synthesis, NASSS would arguably be beneficial for technology developments in health behaviour change more generally, particularly for invention development.

Label-based meta-analysis of functional brain dysconnectivity across mood and psychotic disorders

BACKGROUND

Resting-state functional magnetic resonance imaging (rsfMRI) studies have revealed patterns of functional brain dysconnectivity in psychiatric disorders such as major depression disorder (MDD), bipolar disorder (BD) and schizophrenia (SZ). Although these disorders have been mostly studied in isolation, there is mounting evidence of shared neurobiological alterations across them.

METHODS

To uncover the nature of the relatedness between these psychiatric disorders, we conducted an innovative meta-analysis of dysconnectivity findings reported separately in MDD, BD and SZ. Rather than relying on a classical voxel level coordinate-based approach, our procedure extracted relevant neuroanatomical labels from text data and examined findings at the whole brain network level. Data were drawn from 428 rsfMRI studies investigating MDD (158 studies, 7429 patients/7414 controls), BD (81 studies, 3330 patients/4096 patients) and/or SZ (223 studies, 11,168 patients/11,754 controls). Permutation testing revealed commonalities and differences in hypoconnectivity and hyperconnectivity patterns across disorders.

RESULTS

Hypoconnectivity and hyperconnectivity patterns of higher-order cognitive (default-mode, fronto-parietal, cingulo-opercular) networks were similarly observed across the three disorders. By contrast, dysconnectivity of lower-order (somatomotor, visual, auditory) networks in some cases differed between disorders, notably dissociating SZ from BD and MDD.

CONCLUSIONS

Findings suggest that functional brain dysconnectivity of higher-order cognitive networks is largely transdiagnostic in nature while that of lower-order networks may best discriminate between mood and psychotic disorders, thus emphasizing the relevance of motor and sensory networks to psychiatric neuroscience.

Auto-STEED: A data mining tool for automated extraction of experimental parameters and risk of bias items from in vivo publications

Background

Systematic reviews, i.e., research summaries that address focused questions in a structured and reproducible manner, are a cornerstone of evidence-based medicine and research. However, certain systematic review steps such as data extraction are labour-intensive which hampers their applicability, not least with the rapidly expanding body of biomedical literature.

Objective

To bridge this gap, we aimed at developing a data mining tool in the R programming environment to automate data extraction from neuroscience in vivo publications. The function was trained on a literature corpus (n=45 publications) of animal motor neuron disease studies and tested in two validation corpora (motor neuron diseases, n=31 publications; multiple sclerosis, n=244 publications).

Results

Our data mining tool Auto-STEED (Automated and STructured Extraction of Experimental Data) was able to extract key experimental parameters such as animal models and species as well as risk of bias items such as randomization or blinding from in vivo studies. Sensitivity and specificity were over 85 and 80%, respectively, for most items in both validation corpora. Accuracy and F-scores were above 90% and 0.9 for most items in the validation corpora. Time savings were above 99%.

Conclusions

Our developed text mining tool Auto-STEED is able to extract key experimental parameters and risk of bias items from the neuroscience in vivo literature. With this, the tool can be deployed to probe a field in a research improvement context or to replace one human reader during data extraction resulting in substantial time-savings and contribute towards automation of systematic reviews. The function is available on Github.

Modelado de tópicos aplicado al análisis del papel del aprendizaje automático en revisiones sistemáticas

El objetivo de la investigación fue analizar el papel del aprendizaje automático de datos en las revisiones sistemáticas de literatura. Se aplicó la técnica de Procesamiento de Lenguaje Natural denominada modelado de tópicos, a un conjunto de títulos y resúmenes recopilados de la base de datos Scopus. Especificamente se utilizó la técnica de Asignación Latente de Dirichlet (LDA), a partir de la cual se lograron descubrir y comprender las temáticas subyacentes en la colección de documentos. Los resultados mostraron la utilidad de la técnica utilizada en la revisión exploratoria de literatura, al permitir agrupar los resultados por temáticas. Igualmente, se pudo identificar las áreas y actividades específicas donde más se ha aplicado el aprendizaje automático, en lo referente a revisiones de literatura. Se concluye que la técnica LDA es una estrategia fácil de utilizar y cuyos resultados permiten abordar una amplia colección de documentos de manera sistemática y coherente, reduciendo notablemente el tiempo de la revisión.

Tools to support the automation of systematic reviews: A scoping review

OBJECTIVE

The objectives of this scoping review are to identify the reliability and validity of the available tools, their limitations and any recommendations to further improve the use of these tools.

STUDY DESIGN

A scoping review methodology was followed to map the literature published on the challenges and solutions of conducting evidence synthesis using the JBI scoping review methodology.

RESULTS

A total of 47 publications were included in the review. The current scoping review identified that LitSuggest, Rayyan, Abstractr, BIBOT, R software, RobotAnalyst, DistillerSR, ExaCT and NetMetaXL have potential to be used for the automation of systematic reviews. However, they are not without limitations. The review also identified other studies that employed algorithms that have not yet been developed into user friendly tools. Some of these algorithms showed high validity and reliability but their use is conditional on user knowledge of computer science and algorithms.

CONCLUSION

Abstract screening has reached maturity; data extraction is still an active area. Developing methods to semi-automate different steps of evidence synthesis via machine learning remains an important research direction. Also, it is important to move from the research prototypes currently available to professionally maintained platforms.

Ensuring Prevention Science Research is Synthesis-Ready for Immediate and Lasting Scientific Impact

When seeking to inform and improve prevention efforts and policy, it is important to be able to robustly synthesize all available evidence. But evidence sources are often large and heterogeneous, so understanding what works, for whom, and in what contexts can only be achieved through a systematic and comprehensive synthesis of evidence. Many barriers impede comprehensive evidence synthesis, which leads to uncertainty about the generalizability of intervention effectiveness, including inaccurate titles/abstracts/keywords terminology (hampering literature search efforts), ambiguous reporting of study methods (resulting in inaccurate assessments of study rigor), and poorly reported participant characteristics, outcomes, and key variables (obstructing the calculation of an overall effect or the examination of effect modifiers). To address these issues and improve the reach of primary studies through their inclusion in evidence syntheses, we provide a set of practical guidelines to help prevention scientists prepare synthesis-ready research. We use a recent mindfulness trial as an empirical example to ground the discussion and demonstrate ways to ensure the following: (1) primary studies are discoverable; (2) the types of data needed for synthesis are present; and (3) these data are readily synthesizable. We highlight several tools and practices that can aid authors in these efforts, such as using a data-driven approach for crafting titles, abstracts, and keywords or by creating a repository for each project to host all study-related data files. We also provide step-by-step guidance and software suggestions for standardizing data design and public archiving to facilitate synthesis-ready research.

Artificial intelligence methods for a Bayesian epistemology-powered evidence evaluation

RATIONALE, AIMS AND OBJECTIVES

The diversity of types of evidence (eg, case reports, animal studies and observational studies) makes the assessment of a drug's safety profile into a formidable challenge. While frequentist uncertain inference struggles in aggregating these signals, the more flexible Bayesian approaches seem better suited for this quest. Artificial Intelligence (AI) offers great promise to these approaches for information retrieval, decision support, and learning probabilities from data.

METHODS

E-Synthesis is a Bayesian framework for drug safety assessments built on philosophical principles and considerations. It aims to aggregate all the available information, in order to provide a Bayesian probability of a drug causing an adverse reaction. AI systems are being developed for evidence aggregation in medicine, which increasingly are automated.

RESULTS

We find that AI can help E-Synthesis with information retrieval, usability (graphical decision-making aids), learning Bayes factors from historical data, assessing quality of information and determining conditional probabilities for the so-called 'indicators' of causation for E-Synthesis. Vice versa, E-Synthesis offers a solid methodological basis for (semi-)automated evidence aggregation with AI systems.

CONCLUSIONS

Properly applied, AI can help the transition of philosophical principles and considerations concerning evidence aggregation for drug safety to a tool that can be used in practice.

Artificial Intelligence and Behavioral Science Through the Looking Glass: Challenges for Real-World Application

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.

Perspective: Towards Automated Tracking of Content and Evidence Appraisal of Nutrition Research

Robust recommendations for healthy diets and nutrition require careful synthesis of available evidence. Given the increasing volume of research articles generated, the retrieval and synthesis of evidence are increasingly becoming laborious and time-consuming. Information technology could help to reduce workload for humans. To guide supervised learning however, human identification of key study characteristics is necessary. Reporting guidelines recommend that authors include essential content in articles and could generate manually labeled training data for automated evidence retrieval and synthesis. Here, we present a semiautomated approach to annotate, link, and track the content of nutrition research manuscripts. We used the STROBE extension for nutritional epidemiology (STROBE-nut) reporting guidelines to manually annotate a sample of 15 articles and converted the semantic information into linked data in a Neo4j graph database through an automated process. Six summary statistics were computed to estimate the reporting completeness of the articles. The content structure, presence of essential study characteristics as well as the reporting completeness of the articles are visualized automatically from the graph database. The archived linked data are interoperable through their annotations and relations. A graph database with linked data on essential study characteristics can enable Natural Language Processing in nutrition.