1,653 research outputs found
Mining User-generated Content of Mobile Patient Portal: Dimensions of User Experience
Patient portals are positioned as a central component of patient engagement through the potential to change the physician-patient relationship and enable chronic disease self-management. The incorporation of patient portals provides the promise to deliver excellent quality, at optimized costs, while improving the health of the population. This study extends the existing literature by extracting dimensions related to the Mobile Patient Portal Use. We use a topic modeling approach to systematically analyze users’ feedback from the actual use of a common mobile patient portal, Epic’s MyChart. Comparing results of Latent Dirichlet Allocation analysis with those of human analysis validated the extracted topics. Practically, the results provide insights into adopting mobile patient portals, revealing opportunities for improvement and to enhance the design of current basic portals. Theoretically, the findings inform the social-technical systems and Task-Technology Fit theories in the healthcare field and emphasize important healthcare structural and social aspects. Further, findings inform the humanization of healthcare framework, support the results of existing studies, and introduce new important design dimensions (i.e., aspects) that influence patient satisfaction and adherence to patient portal
Deepr: A Convolutional Net for Medical Records
Feature engineering remains a major bottleneck when creating predictive
systems from electronic medical records. At present, an important missing
element is detecting predictive regular clinical motifs from irregular episodic
records. We present Deepr (short for Deep record), a new end-to-end deep
learning system that learns to extract features from medical records and
predicts future risk automatically. Deepr transforms a record into a sequence
of discrete elements separated by coded time gaps and hospital transfers. On
top of the sequence is a convolutional neural net that detects and combines
predictive local clinical motifs to stratify the risk. Deepr permits
transparent inspection and visualization of its inner working. We validate
Deepr on hospital data to predict unplanned readmission after discharge. Deepr
achieves superior accuracy compared to traditional techniques, detects
meaningful clinical motifs, and uncovers the underlying structure of the
disease and intervention space
The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRs
The successes of foundation models such as ChatGPT and AlphaFold have spurred
significant interest in building similar models for electronic medical records
(EMRs) to improve patient care and hospital operations. However, recent hype
has obscured critical gaps in our understanding of these models' capabilities.
We review over 80 foundation models trained on non-imaging EMR data (i.e.
clinical text and/or structured data) and create a taxonomy delineating their
architectures, training data, and potential use cases. We find that most models
are trained on small, narrowly-scoped clinical datasets (e.g. MIMIC-III) or
broad, public biomedical corpora (e.g. PubMed) and are evaluated on tasks that
do not provide meaningful insights on their usefulness to health systems. In
light of these findings, we propose an improved evaluation framework for
measuring the benefits of clinical foundation models that is more closely
grounded to metrics that matter in healthcare.Comment: Reformatted figures, updated contribution
Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processing
Large language models have revolutionized the field of artificial
intelligence and have been used in various applications. Among these models,
ChatGPT (Chat Generative Pre-trained Transformer) has been developed by OpenAI,
it stands out as a powerful tool that has been widely adopted. ChatGPT has been
successfully applied in numerous areas, including chatbots, content generation,
language translation, personalized recommendations, and even medical diagnosis
and treatment. Its success in these applications can be attributed to its
ability to generate human-like responses, understand natural language, and
adapt to different contexts. Its versatility and accuracy make it a powerful
tool for natural language processing (NLP). However, there are also limitations
to ChatGPT, such as its tendency to produce biased responses and its potential
to perpetuate harmful language patterns. This article provides a comprehensive
overview of ChatGPT, its applications, advantages, and limitations.
Additionally, the paper emphasizes the importance of ethical considerations
when using this robust tool in real-world scenarios. Finally, This paper
contributes to ongoing discussions surrounding artificial intelligence and its
impact on vision and NLP domains by providing insights into prompt engineering
techniques
The Significance of Machine Learning in Clinical Disease Diagnosis: A Review
The global need for effective disease diagnosis remains substantial, given
the complexities of various disease mechanisms and diverse patient symptoms. To
tackle these challenges, researchers, physicians, and patients are turning to
machine learning (ML), an artificial intelligence (AI) discipline, to develop
solutions. By leveraging sophisticated ML and AI methods, healthcare
stakeholders gain enhanced diagnostic and treatment capabilities. However,
there is a scarcity of research focused on ML algorithms for enhancing the
accuracy and computational efficiency. This research investigates the capacity
of machine learning algorithms to improve the transmission of heart rate data
in time series healthcare metrics, concentrating particularly on optimizing
accuracy and efficiency. By exploring various ML algorithms used in healthcare
applications, the review presents the latest trends and approaches in ML-based
disease diagnosis (MLBDD). The factors under consideration include the
algorithm utilized, the types of diseases targeted, the data types employed,
the applications, and the evaluation metrics. This review aims to shed light on
the prospects of ML in healthcare, particularly in disease diagnosis. By
analyzing the current literature, the study provides insights into
state-of-the-art methodologies and their performance metrics.Comment: 8 page
Mining User-generated Content of Mobile Patient Portal: Dimensions of User Experience
Patient portals are positioned as a central component of patient engagement through the potential to change the physician-patient relationship and enable chronic disease self-management. The incorporation of patient portals provides the promise to deliver excellent quality, at optimized costs, while improving the health of the population. This study extends the existing literature by extracting dimensions related to the Mobile Patient Portal Use. We use a topic modeling approach to systematically analyze users’ feedback from the actual use of a common mobile patient portal, Epic\u27s MyChart. Comparing results of Latent Dirichlet Allocation analysis with those of human analysis validated the extracted topics. Practically, the results provide insights into adopting mobile patient portals, revealing opportunities for improvement and to enhance the design of current basic portals. Theoretically, the findings inform the social-technical systems and Task-Technology Fit theories in the healthcare field and emphasize important healthcare structural and social aspects. Further, findings inform the humanization of healthcare framework, support the results of existing studies, and introduce new important design dimensions (i.e., aspects) that influence patient satisfaction and adherence to patient portal
A privacy-preserving data storage and service framework based on deep learning and blockchain for construction workers' wearable IoT sensors
Classifying brain signals collected by wearable Internet of Things (IoT)
sensors, especially brain-computer interfaces (BCIs), is one of the
fastest-growing areas of research. However, research has mostly ignored the
secure storage and privacy protection issues of collected personal
neurophysiological data. Therefore, in this article, we try to bridge this gap
and propose a secure privacy-preserving protocol for implementing BCI
applications. We first transformed brain signals into images and used
generative adversarial network to generate synthetic signals to protect data
privacy. Subsequently, we applied the paradigm of transfer learning for signal
classification. The proposed method was evaluated by a case study and results
indicate that real electroencephalogram data augmented with artificially
generated samples provide superior classification performance. In addition, we
proposed a blockchain-based scheme and developed a prototype on Ethereum, which
aims to make storing, querying and sharing personal neurophysiological data and
analysis reports secure and privacy-aware. The rights of three main transaction
bodies - construction workers, BCI service providers and project managers - are
described and the advantages of the proposed system are discussed. We believe
this paper provides a well-rounded solution to safeguard private data against
cyber-attacks, level the playing field for BCI application developers, and to
the end improve professional well-being in the industry
- …