Improving Mild Cognitive Impairment Prediction via Reinforcement Learning and Dialogue Simulation Article Swipe
Fengyi Tang
,
Kaixiang Lin
,
Ikechukwu Uchendu
,
Hiroko H. Dodge
,
Jiayu Zhou
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1802.06428
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1802.06428
Mild cognitive impairment (MCI) is a prodromal phase in the progression from normal aging to dementia, especially Alzheimers disease. Even though there is mild cognitive decline in MCI patients, they have normal overall cognition and thus is challenging to distinguish from normal aging. Using transcribed data obtained from recorded conversational interactions between participants and trained interviewers, and applying supervised learning models to these data, a recent clinical trial has shown a promising result in differentiating MCI from normal aging. However, the substantial amount of interactions with medical staff can still incur significant medical care expenses in practice. In this paper, we propose a novel reinforcement learning (RL) framework to train an efficient dialogue agent on existing transcripts from clinical trials. Specifically, the agent is trained to sketch disease-specific lexical probability distribution, and thus to converse in a way that maximizes the diagnosis accuracy and minimizes the number of conversation turns. We evaluate the performance of the proposed reinforcement learning framework on the MCI diagnosis from a real clinical trial. The results show that while using only a few turns of conversation, our framework can significantly outperform state-of-the-art supervised learning approaches.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1802.06428
- https://arxiv.org/pdf/1802.06428
- OA Status
- green
- Cited By
- 6
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2788965295
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Raw OpenAlex JSON
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https://openalex.org/W2788965295Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1802.06428Digital Object Identifier
- Title
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Improving Mild Cognitive Impairment Prediction via Reinforcement Learning and Dialogue SimulationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-02-18Full publication date if available
- Authors
-
Fengyi Tang, Kaixiang Lin, Ikechukwu Uchendu, Hiroko H. Dodge, Jiayu ZhouList of authors in order
- Landing page
-
https://arxiv.org/abs/1802.06428Publisher landing page
- PDF URL
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https://arxiv.org/pdf/1802.06428Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1802.06428Direct OA link when available
- Concepts
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Reinforcement learning, Reinforcement, Cognitive impairment, Cognition, Computer science, Cognitive psychology, Artificial intelligence, Psychology, Machine learning, Social psychology, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2023: 2, 2022: 1, 2019: 2Per-year citation counts (last 5 years)
- References (count)
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33Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.that | 138, 172 |
| abstract_inverted_index.they | 29 |
| abstract_inverted_index.this | 98 |
| abstract_inverted_index.thus | 35, 132 |
| abstract_inverted_index.with | 85 |
| abstract_inverted_index.(MCI) | 3 |
| abstract_inverted_index.Using | 43 |
| abstract_inverted_index.agent | 113, 122 |
| abstract_inverted_index.aging | 13 |
| abstract_inverted_index.data, | 63 |
| abstract_inverted_index.incur | 90 |
| abstract_inverted_index.novel | 103 |
| abstract_inverted_index.phase | 7 |
| abstract_inverted_index.shown | 69 |
| abstract_inverted_index.staff | 87 |
| abstract_inverted_index.still | 89 |
| abstract_inverted_index.there | 21 |
| abstract_inverted_index.these | 62 |
| abstract_inverted_index.train | 109 |
| abstract_inverted_index.trial | 67 |
| abstract_inverted_index.turns | 178 |
| abstract_inverted_index.using | 174 |
| abstract_inverted_index.while | 173 |
| abstract_inverted_index.aging. | 42, 78 |
| abstract_inverted_index.amount | 82 |
| abstract_inverted_index.models | 60 |
| abstract_inverted_index.normal | 12, 31, 41, 77 |
| abstract_inverted_index.number | 146 |
| abstract_inverted_index.paper, | 99 |
| abstract_inverted_index.recent | 65 |
| abstract_inverted_index.result | 72 |
| abstract_inverted_index.sketch | 126 |
| abstract_inverted_index.though | 20 |
| abstract_inverted_index.trial. | 168 |
| abstract_inverted_index.turns. | 149 |
| abstract_inverted_index.between | 51 |
| abstract_inverted_index.decline | 25 |
| abstract_inverted_index.lexical | 128 |
| abstract_inverted_index.medical | 86, 92 |
| abstract_inverted_index.overall | 32 |
| abstract_inverted_index.propose | 101 |
| abstract_inverted_index.results | 170 |
| abstract_inverted_index.trained | 54, 124 |
| abstract_inverted_index.trials. | 119 |
| abstract_inverted_index.However, | 79 |
| abstract_inverted_index.accuracy | 142 |
| abstract_inverted_index.applying | 57 |
| abstract_inverted_index.clinical | 66, 118, 167 |
| abstract_inverted_index.converse | 134 |
| abstract_inverted_index.dialogue | 112 |
| abstract_inverted_index.disease. | 18 |
| abstract_inverted_index.evaluate | 151 |
| abstract_inverted_index.existing | 115 |
| abstract_inverted_index.expenses | 94 |
| abstract_inverted_index.learning | 59, 105, 158, 188 |
| abstract_inverted_index.obtained | 46 |
| abstract_inverted_index.proposed | 156 |
| abstract_inverted_index.recorded | 48 |
| abstract_inverted_index.cognition | 33 |
| abstract_inverted_index.cognitive | 1, 24 |
| abstract_inverted_index.dementia, | 15 |
| abstract_inverted_index.diagnosis | 141, 163 |
| abstract_inverted_index.efficient | 111 |
| abstract_inverted_index.framework | 107, 159, 182 |
| abstract_inverted_index.maximizes | 139 |
| abstract_inverted_index.minimizes | 144 |
| abstract_inverted_index.patients, | 28 |
| abstract_inverted_index.practice. | 96 |
| abstract_inverted_index.prodromal | 6 |
| abstract_inverted_index.promising | 71 |
| abstract_inverted_index.Alzheimers | 17 |
| abstract_inverted_index.especially | 16 |
| abstract_inverted_index.impairment | 2 |
| abstract_inverted_index.outperform | 185 |
| abstract_inverted_index.supervised | 58, 187 |
| abstract_inverted_index.approaches. | 189 |
| abstract_inverted_index.challenging | 37 |
| abstract_inverted_index.distinguish | 39 |
| abstract_inverted_index.performance | 153 |
| abstract_inverted_index.probability | 129 |
| abstract_inverted_index.progression | 10 |
| abstract_inverted_index.significant | 91 |
| abstract_inverted_index.substantial | 81 |
| abstract_inverted_index.transcribed | 44 |
| abstract_inverted_index.transcripts | 116 |
| abstract_inverted_index.conversation | 148 |
| abstract_inverted_index.interactions | 50, 84 |
| abstract_inverted_index.participants | 52 |
| abstract_inverted_index.Specifically, | 120 |
| abstract_inverted_index.conversation, | 180 |
| abstract_inverted_index.distribution, | 130 |
| abstract_inverted_index.interviewers, | 55 |
| abstract_inverted_index.reinforcement | 104, 157 |
| abstract_inverted_index.significantly | 184 |
| abstract_inverted_index.conversational | 49 |
| abstract_inverted_index.differentiating | 74 |
| abstract_inverted_index.disease-specific | 127 |
| abstract_inverted_index.state-of-the-art | 186 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.5600000023841858 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |