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Found 15 papers, 3 papers with code
Impact of ASR on Alzheimer’s Disease Detection: All Errors are Equal, but Deletions are More Equal than Others
We find that deletion errors affect detection performance the most, due to their impact on the features of syntactic complexity and discourse representation in speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
The organization of the research roundtables at the conference involved 17 Senior Chairs and 19 Junior Chairs across 11 tables.
Event-Based Contrastive Learning for Medical Time Series
In clinical practice, one often needs to identify whether a patient is at high risk of adverse outcomes after some key medical event; for example, the short-term risk of death after an admission for heart failure.
The Role of Relevance in Fair Ranking
Online platforms mediate access to opportunity: relevance-based rankings create and constrain options by allocating exposure to job openings and job candidates in hiring platforms, or sellers in a marketplace.
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations
Across two different blackbox model architectures and four popular explainability methods, we find that the approximation quality of explanation models, also known as the fidelity, differs significantly between subgroups.
Quantifying the Task-Specific Information in Text-Based Classifications
This framework allows us to compare across datasets, saying that, apart from a set of ``shortcut features'', classifying each sample in the Multi-NLI task involves around 0. 4 nats more TSI than in the Quora Question Pair.
Comparing Acoustic-based Approaches for Alzheimer's Disease Detection
Robust strategies for Alzheimer's disease (AD) detection are important, given the high prevalence of AD.
Augmenting BERT Carefully with Underrepresented Linguistic Features
Fine-tuned Bidirectional Encoder Representations from Transformers (BERT)-based sequence classification models have proven to be effective for detecting Alzheimer's Disease (AD) from transcripts of human speech.
Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided Approach
Despite the widely reported success of embedding-based machine learning methods on natural language processing tasks, the use of more easily interpreted engineered features remains common in fields such as cognitive impairment (CI) detection.
To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection
Research related to automatically detecting Alzheimer's disease (AD) is important, given the high prevalence of AD and the high cost of traditional methods.
Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation
We learn mappings from other languages to English and detect aphasia from linguistic characteristics of speech, and show that OT domain adaptation improves aphasia detection over unilingual baselines for French (6% increased F1) and Mandarin (5% increased F1).
Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power
Understanding the vulnerability of linguistic features extracted from noisy text is important for both developing better health text classification models and for interpreting vulnerabilities of natural language models.
Impact of ASR on Alzheimer's Disease Detection: All Errors are Equal, but Deletions are More Equal than Others
We find that deletion errors affect detection performance the most, due to their impact on the features of syntactic complexity and discourse representation in speech.
Alzheimer's Disease Detection
Automatic Speech Recognition
+2
The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech
We analyze the impact of age of the added samples and if they affect fairness in classification.
ReGAN: RE[LAX|BAR|INFORCE] based Sequence Generation using GANs
Although GANs have had a lot of success in producing more realistic images than other approaches, they have only seen limited use for text sequences.
