2,172 research outputs found
Integrative AI-Driven Strategies for Advancing Precision Medicine in Infectious Diseases and Beyond: A Novel Multidisciplinary Approach
Precision medicine, tailored to individual patients based on their genetics,
environment, and lifestyle, shows promise in managing complex diseases like
infections. Integrating artificial intelligence (AI) into precision medicine
can revolutionize disease management. This paper introduces a novel approach
using AI to advance precision medicine in infectious diseases and beyond. It
integrates diverse fields, analyzing patients' profiles using genomics,
proteomics, microbiomics, and clinical data. AI algorithms process vast data,
providing insights for precise diagnosis, treatment, and prognosis. AI-driven
predictive modeling empowers healthcare providers to make personalized and
effective interventions. Collaboration among experts from different domains
refines AI models and ensures ethical and robust applications. Beyond
infections, this AI-driven approach can benefit other complex diseases.
Precision medicine powered by AI has the potential to transform healthcare into
a proactive, patient-centric model. Research is needed to address privacy,
regulations, and AI integration into clinical workflows. Collaboration among
researchers, healthcare institutions, and policymakers is crucial in harnessing
AI-driven strategies for advancing precision medicine and improving patient
outcomes
Review of innovative immersive technologies for healthcare applications
Immersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), can connect people using enhanced data visualizations to better involve stakeholders as integral members of the process. Immersive technologies have started to change the research on multidimensional genomic data analysis for disease diagnostics and treatments. Immersive technologies
are highlighted in some research for health and clinical needs, especially for precision medicine innovation. The use of immersive technology for genomic data analysis has recently received attention from the research community. Genomic data analytics research seeks to integrate immersive technologies to build more natural human-computer interactions that allow better perception engagements. Immersive technologies, especially VR, help humans perceive the digital world as real and give learning output with lower performance errors and higher accuracy. However, there are limited reviews about immersive technologies used in healthcare and genomic data analysis with specific digital health applications. This paper contributes a comprehensive review of using immersive technologies for digital health applications, including patient-centric applications, medical domain education, and data analysis, especially genomic data visual analytics. We highlight the evolution of a visual analysis using VR as a case study for how
immersive technologies step, can by step, move into the genomic data analysis domain. The discussion and conclusion summarize the current immersive technology applications’ usability, innovation, and future work in the healthcare domain, and digital health data visual analytics
Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice
Routinely collected data in hospital Electronic Medical Records (EMR) is rich and abundant but often not linked or analysed for purposes other than direct patient care. We have created a methodology to integrate patient-centric data from different EMR systems into clinical pathways that represent the history of all patient interactions with the hospital during the course of a disease and beyond. In this paper, the literature in the area of data visualisation in healthcare is reviewed and a method for visualising the journeys that patients take through care is discussed. Examples of the hidden knowledge that could be discovered using this approach are explored and the main application areas of visualisation tools are identified. This paper also highlights the challenges of collecting and analysing such data and making the visualisations extensively used in the medical domain. This paper starts by presenting the state-of-the-art in visualisation of clinical and other health related data. Then, it describes an example clinical problem and discusses the visualisation tools and techniques created for the utilisation of these data by clinicians and researchers. Finally, we look at the open problems in this area of research and discuss future challenges
Intelligent decision support System for precision medicine (IDSS 4 PM)
Availability of healthcare big data and limited human cognitive to
decide timely, from one hand and unsuccessful business model of traditional
Medical Decision Making(MDM), on the other hand, have challenged the
healthcare/medicine landscape to pioneer Precision Medicine (PM). This study
aims to propose the conceptual framework of the Intelligent Decision Support
system for Precision Medicine (IDSS4 PM), by highlighting the fundamental role
of key technologies.FCT – Fundação para a Ciência e Tecnologia within
the Projects Scope: DSAIPA/DS/0084/201
Results from the Clarify Study
Centro de Matemática e Aplicações, UID (MAT/00297/2020), Portuguese Foundation of Science and Technology.
Publisher Copyright:
© 2022 by the authors.Background: Artificial intelligence (AI) has contributed substantially in recent years to the resolution of different biomedical problems, including cancer. However, AI tools with significant and widespread impact in oncology remain scarce. The goal of this study is to present an AI-based solution tool for cancer patients data analysis that assists clinicians in identifying the clinical factors associated with poor prognosis, relapse and survival, and to develop a prognostic model that stratifies patients by risk. Materials and Methods: We used clinical data from 5275 patients diagnosed with non-small cell lung cancer, breast cancer, and non-Hodgkin lymphoma at Hospital Universitario Puerta de Hierro-Majadahonda. Accessible clinical parameters measured with a wearable device and quality of life questionnaires data were also collected. Results: Using an AI-tool, data from 5275 cancer patients were analyzed, integrating clinical data, questionnaires data, and data collected from wearable devices. Descriptive analyses were performed in order to explore the patients’ characteristics, survival probabilities were calculated, and a prognostic model identified low and high-risk profile patients. Conclusion: Overall, the reconstruction of the population’s risk profile for the cancer-specific predictive model was achieved and proved useful in clinical practice using artificial intelligence. It has potential application in clinical settings to improve risk stratification, early detection, and surveillance management of cancer patients.publishersversionpublishe
The use of clinical, behavioral, and social determinants of health to improve identification of patients in need of advanced care for depression
Indiana University-Purdue University Indianapolis (IUPUI)Depression is the most commonly occurring mental illness the world over. It poses
a significant health and economic burden across the individual and community. Not all
occurrences of depression require the same level of treatment. However, identifying
patients in need of advanced care has been challenging and presents a significant bottleneck
in providing care. We developed a knowledge-driven depression taxonomy comprised of
features representing clinical, behavioral, and social determinants of health (SDH) that
inform the onset, progression, and outcome of depression. We leveraged the depression
taxonomy to build decision models that predicted need for referrals across: (a) the overall
patient population and (b) various high-risk populations. Decision models were built using
longitudinal, clinical, and behavioral data extracted from a population of 84,317 patients
seeking care at Eskenazi Health of Indianapolis, Indiana. Each decision model yielded
significantly high predictive performance. However, models predicting need of treatment
across high-risk populations (ROC’s of 86.31% to 94.42%) outperformed models
representing the overall patient population (ROC of 78.87%). Next, we assessed the value
of adding SDH into each model. For each patient population under study, we built
additional decision models that incorporated a wide range of patient and aggregate-level
SDH and compared their performance against the original models. Models that
incorporated SDH yielded high predictive performance. However, use of SDH did not yield
statistically significant performance improvements. Our efforts present significant
potential to identify patients in need of advanced care using a limited number of clinical
and behavioral features. However, we found no benefit to incorporating additional SDH
into these models. Our methods can also be applied across other datasets in response to a
wide variety of healthcare challenges
An Analytic and Systemic View of the Digital Transformation of Healthcare
Industry 4.0 represents a digital revolution that is driven by technologies that blur the lines between the physical and digital worlds. Industry 4.0, the latest industrial revolution, is poised to have a profound impact on all aspects of society. In order to understand how the healthcare industry is being transformed by the convergence of the physical and digital realms, a systems perspective is taken in this study. Two research questions are addressed regarding the opportunities and interventions that can be provided by both analytical and systems conceptions of digital transformation. I use a systemic literature review approach to address the research questions. A sample of studies between 2000 and 2022 is analyzed. Existing studies mostly examine the effects of new digital technologies on healthcare providers. However, digital transformation also presents significant challenges, such as data privacy, ethical concerns related to AI-based automated decision-making, and equity issues related to e-health. Solutions to major challenges at both micro and macro levels can be derived from the existing theories and tools of systems thinking. For instance, systems thinking\u27s continuous learning and adaptation capabilities can be useful for healthcare organizations to develop the required digital capabilities. Furthermore, the interconnectedness of subsystems and stakeholders in systems thinking can be combined with digital twin technology to investigate the dynamic interactions among key stakeholders, leading to the development of new regulatory policies
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