Summary of Linguistic-based Mild Cognitive Impairment Detection Using Informative Loss, by Ali Pourramezan Fard et al.
Linguistic-Based Mild Cognitive Impairment Detection Using Informative Loss
by Ali Pourramezan Fard, Mohammad H. Mahoor, Muath Alsuhaibani, Hiroko H. Dodgec
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed deep learning method uses Natural Language Processing (NLP) techniques to differentiate between Mild Cognitive Impairment (MCI) and Normal Cognitive (NC) conditions in older adults. A framework is presented that analyzes transcripts from video interviews collected within the I-CONECT study project, a randomized controlled trial aimed at improving cognitive functions through video chats. The framework consists of two Transformer-based modules: Sentence Embedding (SE) and Sentence Cross Attention (SCA). The SE module captures contextual relationships between words within each sentence, while the SCA module extracts temporal features from a sequence of sentences. A Multi-Layer Perceptron (MLP) is then used for classification into MCI or NC. To enhance classification accuracy, a novel loss function called InfoLoss is proposed that considers reduction in entropy by observing each sequence of sentences. The results show that the framework can distinguish between MCI and NC with an average area under the curve of 84.75% on the I-CONECT dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses computer programs to understand conversations and figure out if older adults are experiencing Mild Cognitive Impairment or just having normal thinking abilities. The researchers used video interviews to train a special kind of AI model that can analyze sentences and pick up on patterns. They developed two main parts for this model: one that looks at words in each sentence and another that looks at how sentences relate to each other over time. This information is then used to decide whether someone has MCI or not. The model was tested using a special dataset and showed it can accurately make these decisions about 85% of the time. |
Keywords
* Artificial intelligence * Classification * Cross attention * Deep learning * Embedding * Loss function * Natural language processing * Nlp * Transformer
