1
|
Jacucci G, Barral O, Daee P, Wenzel M, Serim B, Ruotsalo T, Pluchino P, Freeman J, Gamberini L, Kaski S, Blankertz B. Integrating neurophysiologic relevance feedback in intent modeling for information retrieval. J Assoc Inf Sci Technol 2019; 70:917-930. [PMID: 31763361 PMCID: PMC6853416 DOI: 10.1002/asi.24161] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 04/20/2018] [Accepted: 10/17/2018] [Indexed: 11/11/2022]
Abstract
The use of implicit relevance feedback from neurophysiology could deliver effortless information retrieval. However, both computing neurophysiologic responses and retrieving documents are characterized by uncertainty because of noisy signals and incomplete or inconsistent representations of the data. We present the first-of-its-kind, fully integrated information retrieval system that makes use of online implicit relevance feedback generated from brain activity as measured through electroencephalography (EEG), and eye movements. The findings of the evaluation experiment (N = 16) show that we are able to compute online neurophysiology-based relevance feedback with performance significantly better than chance in complex data domains and realistic search tasks. We contribute by demonstrating how to integrate in interactive intent modeling this inherently noisy implicit relevance feedback combined with scarce explicit feedback. Although experimental measures of task performance did not allow us to demonstrate how the classification outcomes translated into search task performance, the experiment proved that our approach is able to generate relevance feedback from brain signals and eye movements in a realistic scenario, thus providing promising implications for future work in neuroadaptive information retrieval (IR).
Collapse
Affiliation(s)
- Giulio Jacucci
- Helsinki Institute for Information Technology HIIT, Department of Computer Science University of Helsinki P.O. Box 68, (Pietari Kalmin katu 5), Helsinki FI-00014 Finland
| | - Oswald Barral
- Helsinki Institute for Information Technology HIIT, Department of Computer Science University of Helsinki P.O. Box 68, (Pietari Kalmin katu 5), Helsinki FI-00014 Finland
| | - Pedram Daee
- Helsinki Institute for Information Technology HIIT, Department of Computer Science Aalto University P.O.Box 15400, Aalto FI-00076 Finland
| | - Markus Wenzel
- Neurotechnology Group Technische Universität Berlin Berlin 10587 Germany
| | - Baris Serim
- Helsinki Institute for Information Technology HIIT, Department of Computer Science University of Helsinki P.O. Box 68, (Pietari Kalmin katu 5), Helsinki FI-00014 Finland
| | - Tuukka Ruotsalo
- Helsinki Institute for Information Technology HIIT, Department of Computer Science University of Helsinki P.O. Box 68, (Pietari Kalmin katu 5), Helsinki FI-00014 Finland
| | - Patrik Pluchino
- Human Inspired Technology Research Centre University of Padova Via Luzzatti 4, Padova 35121 Italy
| | | | - Luciano Gamberini
- Human Inspired Technology Research Centre University of Padova Via Luzzatti 4, Padova 35121 Italy
| | - Samuel Kaski
- Helsinki Institute for Information Technology HIIT, Department of Computer Science Aalto University P.O.Box 15400, Aalto FI-00076 Finland
| | - Benjamin Blankertz
- Neurotechnology Group Technische Universität Berlin Berlin 10587 Germany
| |
Collapse
|
2
|
Sundin I, Peltola T, Micallef L, Afrabandpey H, Soare M, Mamun Majumder M, Daee P, He C, Serim B, Havulinna A, Heckman C, Jacucci G, Marttinen P, Kaski S. Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge. Bioinformatics 2018; 34:i395-i403. [PMID: 29949984 PMCID: PMC6022689 DOI: 10.1093/bioinformatics/bty257] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Motivation Precision medicine requires the ability to predict the efficacies of different treatments for a given individual using high-dimensional genomic measurements. However, identifying predictive features remains a challenge when the sample size is small. Incorporating expert knowledge offers a promising approach to improve predictions, but collecting such knowledge is laborious if the number of candidate features is very large. Results We introduce a probabilistic framework to incorporate expert feedback about the impact of genomic measurements on the outcome of interest and present a novel approach to collect the feedback efficiently, based on Bayesian experimental design. The new approach outperformed other recent alternatives in two medical applications: prediction of metabolic traits and prediction of sensitivity of cancer cells to different drugs, both using genomic features as predictors. Furthermore, the intelligent approach to collect feedback reduced the workload of the expert to approximately 11%, compared to a baseline approach. Availability and implementation Source code implementing the introduced computational methods is freely available at https://github.com/AaltoPML/knowledge-elicitation-for-precision-medicine. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Iiris Sundin
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland
| | - Tomi Peltola
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland
| | - Luana Micallef
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland
| | - Homayun Afrabandpey
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland
| | - Marta Soare
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland
| | - Muntasir Mamun Majumder
- Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Pedram Daee
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland
| | - Chen He
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland
| | - Baris Serim
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland
| | - Aki Havulinna
- Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.,National Institute for Health and Welfare THL, Helsinki, Finland
| | - Caroline Heckman
- Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Giulio Jacucci
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland
| | - Pekka Marttinen
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland
| | - Samuel Kaski
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland
| |
Collapse
|
3
|
Daee P, Mirian MS, Ahmadabadi MN. Reward maximization justifies the transition from sensory selection at childhood to sensory integration at adulthood. PLoS One 2014; 9:e103143. [PMID: 25058591 PMCID: PMC4110011 DOI: 10.1371/journal.pone.0103143] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2014] [Accepted: 06/27/2014] [Indexed: 11/19/2022] Open
Abstract
In a multisensory task, human adults integrate information from different sensory modalities -behaviorally in an optimal Bayesian fashion- while children mostly rely on a single sensor modality for decision making. The reason behind this change of behavior over age and the process behind learning the required statistics for optimal integration are still unclear and have not been justified by the conventional Bayesian modeling. We propose an interactive multisensory learning framework without making any prior assumptions about the sensory models. In this framework, learning in every modality and in their joint space is done in parallel using a single-step reinforcement learning method. A simple statistical test on confidence intervals on the mean of reward distributions is used to select the most informative source of information among the individual modalities and the joint space. Analyses of the method and the simulation results on a multimodal localization task show that the learning system autonomously starts with sensory selection and gradually switches to sensory integration. This is because, relying more on modalities -i.e. selection- at early learning steps (childhood) is more rewarding than favoring decisions learned in the joint space since, smaller state-space in modalities results in faster learning in every individual modality. In contrast, after gaining sufficient experiences (adulthood), the quality of learning in the joint space matures while learning in modalities suffers from insufficient accuracy due to perceptual aliasing. It results in tighter confidence interval for the joint space and consequently causes a smooth shift from selection to integration. It suggests that sensory selection and integration are emergent behavior and both are outputs of a single reward maximization process; i.e. the transition is not a preprogrammed phenomenon.
Collapse
Affiliation(s)
- Pedram Daee
- Cognitive Robotics Laboratory, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- * E-mail:
| | - Maryam S. Mirian
- Cognitive Robotics Laboratory, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Majid Nili Ahmadabadi
- Cognitive Robotics Laboratory, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| |
Collapse
|