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Breast cancer is a leading cause of mortality among women, disproportionately affecting Hispanic populations in the U.S., particularly those with limited health literacy and language access. To address these disparities, we present a bilingual, web-based educational platform tailored to low-literacy Hispanic users. The platform supports full navigation in English and Spanish, with seamless language switching and both written and spoken input options. It incorporates automatic speech recognition (ASR) capable of handling code-switching, enhancing accessibility for bilingual users. Educational content is delivered through culturally sensitive videos organized into four categories: prevention, detection, diagnosis, and treatment. Each video includes embedded and post-video assessment questions aligned with Bloom’s Taxonomy to foster active learning. Users can monitor their progress and quiz performance via a personalized dashboard. An integrated chatbot, powered by large language models (LLMs), allows users to ask foundational breast cancer questions in natural language. The platform also recommends relevant resources, including nearby treatment centers, and support groups. LLMs are further used for ASR, question generation, and semantic response evaluation. Combining language technologies and LLMs reduces disparities in cancer education and supports informed decision-making among underserved populations, playing a pivotal role in reducing information gaps and promoting informed healthcare decisions. 

This research project aims to develop a resource management framework for efficient allocation of 5G network resources to IoT (Internet of Things) devices. As 5G technology is increasingly integrated with IoT applications, the diverse demands and use-cases of IoT devices necessitate dynamic resource management. The focus of this study is to develop an IoT device environment utilizing reinforcement learning (RL) for resource adjustment. The environment observes IoT device parameters including the current BER (bit-error-rate), allocated bandwidth, and current signal power levels. Actions that can be taken by the RL agent on the environment include adjustments to the bandwidth and the signal power level of an IoT device. One implementation of the environment is currently tested with PPO (Proximal Policy Optimization), and DDPG (Deep Deterministic Policy Gradient) RL algorithms using a continuous action space. Initial results show that PPO models train at a faster rate, while DDPG models explore a wider range of states, leading to better model predictions. Another version is tested with PPO and DQN (Deep Q-Networks) using a discrete action space. DQN demonstrates slightly better results than the PPO, possibly due to its value-based approach and that it is better suited for discrete action spaces. 

The recent rapid development in Natural Language Processing (NLP) has greatly en- hanced the effectiveness of Intelligent Tutoring Systems (ITS) as tools for healthcare education. These systems hold the potential to improve health-related quality of life (HRQoL) outcomes, especially for populations with limited English reading and writing skills. However, despite the progress in pre-trained multilingual NLP models, there exists a noticeable research gap when it comes to code-switching within the medical context. Code-switching is a prevalent phenomenon in multilingual communities where individuals seamlessly transition between languages during conversations. This presents a distinctive challenge for healthcare ITS aimed at serving multilin- gual communities, as it demands a thorough understanding of and accurate adaptation to code- switching, which has thus far received limited attention in research. The hypothesis of our work asserts that the development of an ITS for healthcare education, culturally appropriate to the Hispanic population with frequent code-switching practices, is both achievable and pragmatic. Given that text classification is a core problem to many tasks in ITS, like sentiment analysis, topic classification, and smart replies, we target text classification as the application domain to validate our hypothesis. Our model relies on pre-trained word embeddings to offer rich representations for understand- ing code-switching medical contexts. However, training such word embeddings, especially within the medical domain, poses a significant challenge due to limited training corpora. In our approach to address this challenge, we identify distinct English and Spanish embeddings, each trained on medical corpora, and subsequently merge them into a unified vector space via space transforma- tion. In our study, we demonstrate that singular value decomposition (SVD) can be used to learn a linear transformation (a matrix), which aligns monolingual vectors from two languages in a single meta-embedding. As an example, we assessed the similarity between the words “cat” and “gato” both before and after alignment, utilizing the cosine similarity metric. Prior to alignment, these words exhibited a similarity score of 0.52, whereas after alignment, the similarity score increased to 0.64. This example illustrates that aligning the word vectors in a meta-embedding enhances the similarity between these words, which share the same meaning in their respective languages. To assess the quality of the representations in our meta-embedding in the context of code-switching, we employed a neural network to conduct text classification tasks on code-switching datasets. Our results demonstrate that, compared to pre-trained multilingual models, our model can achieve high performance in text classification tasks while utilizing significantly fewer parameters. 

Little quantitative research has explored which clinician skills and behaviors facilitate communication. Mutual understanding is especially challenging when patients have limited health literacy (HL). Two strategies hypothesized to improve communication include matching the complexity of language to patients’ HL (“universal tailoring”); or always using simple language (“universal precautions”). Through computational linguistic analysis of 237,126 email exchanges between dyads of 1094 physicians and 4331 English-speaking patients, we assessed matching (concordance/discordance) between physicians’ linguistic complexity and patients’ HL, and classified physicians’ communication strategies. Among low HL patients, discordance was associated with poor understanding ( P = 0.046). Physicians’ “universal tailoring” strategy was associated with better understanding for all patients ( P = 0.01), while “universal precautions” was not. There was an interaction between concordance and communication strategy ( P = 0.021): The combination of dyadic concordance and “universal tailoring” eliminated HL-related disparities. Physicians’ ability to adapt communication to match their patients’ HL promotes shared understanding and equity. The ‘Precision Medicine’ construct should be expanded to include the domain of ‘Precision Communication.’