Artificial Intelligence and Behavioral Science Through the Looking Glass: Challenges for Real-World Application
| Autor: | Susan Michie, Pol Mac Aonghusa |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial intelligence
Computer science Process (engineering) Health Behavior Psychological intervention Behavioural sciences AcademicSubjects/SCI02170 03 medical and health sciences Knowledge-based systems 0302 clinical medicine Behavior change Behavior Therapy Machine learning Humans 030212 general & internal medicine Prediction algorithms Interventions General Psychology 030304 developmental biology 0303 health sciences business.industry Brief Report Deep learning Psychiatry and Mental health Outcome and Process Assessment Health Care Evidence synthesis Scale (social sciences) AcademicSubjects/MED00010 business Behavioral Sciences Natural language |
| Zdroj: | Annals of Behavioral Medicine: A Publication of the Society of Behavioral Medicine |
| ISSN: | 1532-4796 0883-6612 |
| DOI: | 10.1093/abm/kaaa095 |
| Popis: | 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. Artificial intelligence holds promise for achieving the goal of predicting outcomes of behavior change interventions, using information automatically extracted from intervention evaluation reports. |
| Databáze: | OpenAIRE |
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