Using Case-Level Context to Classify Cancer Pathology Reports

Individual electronic health records (EHRs) and clinical reports are often part of a larger sequence-for example, a single patient may generate multiple reports over the trajectory of a disease. In applications such as cancer pathology reports, it is necessary not only to extract information from individual reports, but also to capture aggregate information regarding the entire cancer case based off case-level context from all reports in the sequence. In this paper, we introduce a simple modular add-on for capturing case-level context that is designed to be compatible with most existing deep learning architectures for text classification on individual reports. We test our approach on a corpus of 431,433 cancer pathology reports, and we show that incorporating case-level context significantly boosts classification accuracy across six classification tasks-site, subsite, laterality, histology, behavior, and grade. We expect that with minimal modifications, our add-on can be applied towards a wide range of other clinical text-based tasks

A Survey of Multimodal Information Fusion for Smart Healthcare: Mapping the Journey from Data to Wisdom

Multimodal medical data fusion has emerged as a transformative approach in smart healthcare, enabling a comprehensive understanding of patient health and personalized treatment plans. In this paper, a journey from data to information to knowledge to wisdom (DIKW) is explored through multimodal fusion for smart healthcare. We present a comprehensive review of multimodal medical data fusion focused on the integration of various data modalities. The review explores different approaches such as feature selection, rule-based systems, machine learning, deep learning, and natural language processing, for fusing and analyzing multimodal data. This paper also highlights the challenges associated with multimodal fusion in healthcare. By synthesizing the reviewed frameworks and theories, it proposes a generic framework for multimodal medical data fusion that aligns with the DIKW model. Moreover, it discusses future directions related to the four pillars of healthcare: Predictive, Preventive, Personalized, and Participatory approaches. The components of the comprehensive survey presented in this paper form the foundation for more successful implementation of multimodal fusion in smart healthcare. Our findings can guide researchers and practitioners in leveraging the power of multimodal fusion with the state-of-the-art approaches to revolutionize healthcare and improve patient outcomes.Comment: This work has been submitted to the ELSEVIER for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Program: Graduate Research Achievement Day 2017

Full program for 2017 Graduate Research Achievement Day.https://digitalcommons.odu.edu/graduateschool_achievementday2017-18_programs/1001/thumbnail.jp

ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model

The ChatGPT, a lite and conversational variant of Generative Pretrained Transformer 4 (GPT-4) developed by OpenAI, is one of the milestone Large Language Models (LLMs) with billions of parameters. LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks, which profoundly impact various fields. This paper mainly discusses the future applications of LLMs in dentistry. We introduce two primary LLM deployment methods in dentistry, including automated dental diagnosis and cross-modal dental diagnosis, and examine their potential applications. Especially, equipped with a cross-modal encoder, a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations. We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application. While LLMs offer significant potential benefits, the challenges, such as data privacy, data quality, and model bias, need further study. Overall, LLMs have the potential to revolutionize dental diagnosis and treatment, which indicates a promising avenue for clinical application and research in dentistry

Unveiling the frontiers of deep learning: innovations shaping diverse domains

Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

Health Care Equity Through Intelligent Edge Computing and Augmented Reality/Virtual Reality: A Systematic Review

Intellectual capital is a scarce resource in the healthcare industry. Making the most of this resource is the first step toward achieving a completely intelligent healthcare system. However, most existing centralized and deep learning-based systems are unable to adapt to the growing volume of global health records and face application issues. To balance the scarcity of healthcare resources, the emerging trend of IoMT (Internet of Medical Things) and edge computing will be very practical and cost-effective. A full examination of the transformational role of intelligent edge computing in the IoMT era to attain health care equity is offered in this research. Intelligent edge computing-aided distribution and collaborative information management is a possible approach for a long-term digital healthcare system. Furthermore, IEC (Intelligent Edge Computing) encourages digital health data to be processed only at the edge, minimizing the amount of information exchanged with central servers/the internet. This significantly increases the privacy of digital health data. Another critical component of a sustainable healthcare system is affordability in digital healthcare. Affordability in digital healthcare is another key component of a sustainable healthcare system. Despite its importance, it has received little attention due to its complexity. In isolated and rural areas where expensive equipment is unavailable, IEC with AR / VR, also known as edge device shadow, can play a significant role in the inexpensive data collection process. Healthcare equity becomes a reality by combining intelligent edge device shadows and edge computing

Electronic Medical Records and Machine Learning in Approaches to Drug Development

Electronic medical records (EMRs) were primarily introduced as a digital health tool in hospitals to improve patient care, but over the past decade, research works have implemented EMR data in clinical trials and omics studies to increase translational potential in drug development. EMRs could help discover phenotype-genotype associations, enhance clinical trial protocols, automate adverse drug event detection and prevention, and accelerate precision medicine research. Although feasible, data mining in EMRs still faces challenges. Existing machine learning tools may help overcome these bottlenecks in EMR mining to unlock new approaches in drug development. This chapter will explore the role of EMRs in drug development while evaluating the viability and bottlenecks of their uses in data mining. This will include discussions on EMR usage in drug development while highlighting successful outcomes in oncology and exploring ML tools to complement and enhance EMR as a widely accepted drug-research source, a section on current clinical applications of EMRs, and a conclusion to summarize and imagine what a future drug research pipeline from EMR to patient treatment may look like

Disruptive innovation in the healthcare sector : the advent of AI chatbots

Over the last several decades, the healthcare sector has faced many challenges. These include a shortage of doctors, especially in rural areas, high clinical costs, and an increasing number of diseases needing to be treated. This thesis focuses on the potential and the limitations of an innovative way to solve problems in healthcare – use of AI chatbots. We highlight the user’s perspective concerning AI healthcare chatbot technology. Based on qualitative and quantitative research, we conclude that this novel technology offers new opportunities for diagnostics, enables work to be carried out more efficiently, and gives the patient the power to “self-diagnose”. AI chatbots have not yet reached their full potential due to legal restrictions, insufficient data, and the lack of capacity to integrate them into different systems. Even though the number of AI chatbot users is increasing, people trust chatbots less than doctors. To enhance user engagement and create a higher level of trust, credible entities such as doctors and the government could recommend the use of AI chatbots. The general acceptance of chatbots has to be analyzed per country since it is explained by socio-economic factors (education, age, income), personality-related factors (attitude to new things, curiosity) and communication behavior factors.Nas últimas décadas, o setor da saúde enfrentou muitos desafios. Nestes podem destacar-se a escassez de médicos, especialmente nas zonas rurais, custos de tratamento elevados e um número crescente de doenças a precisarem de ser tratadas. Esta tese foca-se no potencial e nas limitações de uma forma revolucionária de resolver problemas na área da saúde – o uso de chatbots de IA. Destacamos a perspetiva do utilizador em relação à assistência médica através da tecnologia de chatbot de IA. Com base em pesquisas qualitativas e quantitativas, concluímos que esta tecnologia inovadora oferece novas oportunidades para diagnósticos, permite que o trabalho seja realizado com mais eficiência e oferece ao paciente a capacidade de se autodiagnosticar. Os chatbots de IA ainda não atingiram todo o seu potencial devido a restrições legais, dados insuficientes e à falta de capacidade de integrá-los em diferentes sistemas. Ainda que o número de utilizadores de chatbot de IA esteja a aumentar, as pessoas confiam menos nos chatbots do que nos médicos. Para encorajar um maior envolvimento do utilizador e criar um nível mais alto de confiança, entidades credíveis como médicos e o governo podem recomendar o uso de chatbots de IA. A aceitação generalizada dos chatbots deve ser analisada por país, uma vez que é explicada por fatores socioeconómicos (educação, idade, rendimento), fatores relacionados com a personalidade (atitude perante coisas novas, curiosidade) e fatores de comportamento na comunicação