An important issue impacting healthcare is a lack of available experts.
Machine learning (ML) models could resolve this by aiding in diagnosing
patients. However, creating datasets large enough to train these models is
expensive. We evaluated large language models (LLMs) for data creation. Using
Autism Spectrum Disorders (ASD), we prompted ChatGPT and GPT-Premium to
generate 4,200 synthetic observations to augment existing medical data. Our
goal is to label behaviors corresponding to autism criteria and improve model
accuracy with synthetic training data. We used a BERT classifier pre-trained on
biomedical literature to assess differences in performance between models. A
random sample (N=140) from the LLM-generated data was evaluated by a clinician
and found to contain 83% correct example-label pairs. Augmenting data increased
recall by 13% but decreased precision by 16%, correlating with higher quality
and lower accuracy across pairs. Future work will analyze how different
synthetic data traits affect ML outcomes.

利用大型语言模型（LLMs）生成的合成观察数据，用于改善机器学习模型对于自闭症谱系障碍（ASD）诊断的准确性。通过评估 ChatGPT 和 GPT-Premium 生成的 4,200 个合成观察数据，并使用预训练于生物医学文献的 BERT 分类器比较模型之间的性能差异，发现数据增加了召回率 13%，但降低了精确度 16%。未来的研究将分析不同合成数据特征对机器学习结果的影响。

利用大型语言模型生成合成数据以提高基于 BERT 的神经网络的性能

Utilizing Large Language Models to Generate Synthetic Data to Increase  the Performance of BERT-Based Neural Networks

When suggesting Points of Interest (PoIs) to people with autism spectrum
disorders, we must take into account that they have idiosyncratic sensory
aversions to noise, brightness and other features that influence the way they
perceive places. Therefore, recommender systems must deal with these aspects.
However, the retrieval of sensory data about PoIs is a real challenge because
most geographical information servers fail to provide this data. Moreover,
ad-hoc crowdsourcing campaigns do not guarantee to cover large geographical
areas and lack sustainability. Thus, we investigate the extraction of sensory
data about places from the consumer feedback collected by location-based
services, on which people spontaneously post reviews from all over the world.
Specifically, we propose a model for the extraction of sensory data from the
reviews about PoIs, and its integration in recommender systems to predict item
ratings by considering both user preferences and compatibility information. We
tested our approach with autistic and neurotypical people by integrating it
into diverse recommendation algorithms. For the test, we used a dataset built
in a crowdsourcing campaign and another one extracted from TripAdvisor reviews.
The results show that the algorithms obtain the highest accuracy and ranking
capability when using TripAdvisor data. Moreover, by jointly using these two
datasets, the algorithms further improve their performance. These results
encourage the use of consumer feedback as a reliable source of information
about places in the development of inclusive recommender systems.

本研究旨在从地理信息服务中收集大量来自世界各地的消费者反馈中提取感官数据，并集成到推荐算法中，以生成更包容性的推荐系统，具有较高的准确性和排名性能。

利用基于位置的服务的用户反馈来为自闭症者设计 POI 推荐系统

Using consumer feedback from location-based services in PoI recommender systems for people with autism

Autism Spectrum Disorders (ASDs) are often associated with specific atypical
postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) have
a specific visibility. While the identification and the quantification of SMM
patterns remain complex, its automation would provide support to accurate
tuning of the intervention in the therapy of autism. Therefore, it is essential
to develop automatic SMM detection systems in a real world setting, taking care
of strong inter-subject and intra-subject variability. Wireless accelerometer
sensing technology can provide a valid infrastructure for real-time SMM
detection, however such variability remains a problem also for machine learning
methods, in particular whenever handcrafted features extracted from
accelerometer signal are considered. Here, we propose to employ the deep
learning paradigm in order to learn discriminating features from multi-sensor
accelerometer signals. Our results provide preliminary evidence that feature
learning and transfer learning embedded in the deep architecture achieve higher
accurate SMM detectors in longitudinal scenarios.

采用深度学习范式从多传感器加速度计信号中学习的特征和转移学习可以实现更精确的 SMM 检测器，从而为自闭症治疗的准确调整提供支持。