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 Ch...
| Publicado en: | Annals of Behavioral Medicine Vol. 54; no. 12; pp. 942 - 948 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Dec2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148188574&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148188574 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08836612 H5U jtl: Annals of Behavioral Medicine issn: 08836612 maglogo: N pubinfo: dt: Dec2020 vid: 54 iid: 12 pid: 622 pub: Oxford University Press / USA artinfo: ui: 148188574 148188574 NLM33416835 148188574 10.1093/abm/kaaa095 NLM33416835 148188574 ppf: 942 ppct: 6 formats: tig: atl: Artificial Intelligence and Behavioral Science Through the Looking Glass: Challenges for Real-World Application. aug: au: Aonghusa, Pol Mac Michie, Susan Mac Aonghusa, Pol affil: Health and Social Care Research Group, IBM Research , Dublin, Ireland sug: subj: Behavioral Sciences Methods Behavioral Sciences Statistics and Numerical Data Behavior Therapy Methods Behavior Therapy Statistics and Numerical Data Health Behavior Artificial Intelligence Human Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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