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Components of an integrated precision medicine workflow illustrating existing HER and novel PM infrastructure-(QC: Quality Control)
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Precision medicine (PM) is an emerging approach that appears with the impression of changing the existing paradigm of medical practice. Recent advances in technological innovations and genetics and the growing availability of health data have set a new pace of the research and impose a set of new requirements on the different stakeholders. Some stu...
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Context 1
... authors therein emphasized about the integration of a PM program into a medical institution's clinical system to facilitate the billing and the reimbursement. The proposed PM infrastructure integration with an existing electronic health record infrastructure is shown in Figure 7. The existing EHR infrastructure depicted on the left is integrated with the PM infrastructure on the right by passing the patient specimen information to the laboratory information management system (LIMS) in order to process, sequence, and analyze the specimen data. ...
Citations
... This task remains challenging due to the complexity and volume of physician notes. High-throughput phenotyping-the automated mapping of patient symptoms to standardized ontology concepts-is crucial for this endeavor [1][2][3][4][5]. Although traditional and some advanced Natural Language Processing (NLP) methods have progressed toward this goal, their limitations underscore the need for more efficient methods. ...
High-throughput phenotyping, the automated mapping of patient signs and symptoms to standardized ontology concepts, is essential to gaining value from electronic health records (EHR) in the support of precision medicine. Despite technological advances, high-throughput phenotyping remains a challenge. This study compares three computational approaches to high-throughput phenotyping: a Large Language Model (LLM) incorporating generative AI, a Natural Language Processing (NLP) approach utilizing deep learning for span categorization, and a hybrid approach combining word vectors with machine learning. The approach that implemented GPT-4 (a Large Language Model) demonstrated superior performance, suggesting that Large Language Models are poised to be the preferred method for high-throughput phenotyping of physician notes.
... Healthcare stakeholders can improve prevention efforts, optimize therapy pathways, and encourage sustained recoveries among people who suffer from substance use disorders. To fully utilize health informatics in the fight against the growing problem of opioid and other drug addictions, however, issues of confidentiality, connectivity, and fairness must be resolved [67]. ...
... Future initiatives should concentrate on collaboration among healthcare sectors, government bodies, technology developers, and community members to create inventive informatics in order to maximize the impact of health informatics on drug addiction management [39]. Furthermore, it is crucial to carry out thorough investigative research to determine the efficacy of health information technology in the treatment of drug addiction [67]. Drug addiction treatment can undergo a revolution if data analytics and machine learning algorithms are used to design therapies based on an individual patient's unique risk profile, treatment preferences, and socioeconomic background. ...
Drug addiction is a rising concern globally that has deeply attracted the attention of the healthcare sector. The United States is not an exception, and the drug addiction crisis there is even more serious, with 10% of adults having faced substance use disorder, while around 75% of this number has been reported as not having received any treatment. Surprisingly, there are annually over 70,000 deaths reported as being due to drug overdose. Researchers are continually searching for solutions, as the current strategies have been ineffective. Health informatics platforms like electronic health records, telemedicine, and the clinical decision support system have great potential in tracking the healthcare data of patients on an individual basis and provide precise medical support in a private space. Such technologies have been found to be useful in identifying the risk factors of drug addiction among people and mitigating them. Moreover, the platforms can be used to check prescriptions of addictive drugs such as opioids and caution healthcare providers. Programs such as the Prescription Drug Monitoring Program (PDMP) and the Drug and Alcohol Services Information Systems (DASIS) are already in action in the US, but the situation demands more in-depth studies in order to mitigate substance use disorders. Artificial intelligence (AI), when combined with health informatics, can aid in the analysis of large amounts of patient data and aid in classifying nature of addiction to assist in the provision of personalized care.
... With isolated gene testing, progressing to genomics and subsequently a holistic integrated precision medicine framework is the call of the day. Mainstreaming gene testing and genome sequencing within the major tertiarylevel public hospitals could initiate an era of precision medicine geared toward managing hereditary diseases that are currently misdiagnosed and mistreated (Afzal et al., 2020). ...
... 43 The validation of the habits and lifestyle being the most useful types of PGHD for HCPs relates to the principle of precision medicine, where care is personalized based on individual variability, including factors such as the patient's environment and lifestyle. 44 These factors can provide context to clinical data and facilitate communication and care. ...
Background Patient-generated health data (PGHD) are data collected through technologies such as mobile devices and health apps. The integration of PGHD into health care workflows can support the care of chronic conditions such as multiple sclerosis (MS). Patients are often willing to share data with health care professionals (HCPs) in their care team; however, the benefits of PGHD can be limited if HCPs do not find it useful, leading patients to discontinue data tracking and sharing eventually. Therefore, understanding the usefulness of mobile health (mHealth) solutions, which provide PGHD and serve as enablers of the HCPs' involvement in participatory care, could motivate them to continue using these technologies.
Objective The objective of this study is to explore the perceived utility of different types of PGHD from mHealth solutions which could serve as tools for HCPs to support participatory care in MS.
Method A mixed-methods approach was used, combining qualitative research and participatory design. This study includes three sequential phases: data collection, assessment of PGHD utility, and design of data visualizations. In the first phase, 16 HCPs were interviewed. The second and third phases were carried out through participatory workshops, where PGHD types were conceptualized in terms of utility.
Results The study found that HCPs are optimistic about PGHD in MS care. The most useful types of PGHD for HCPs in MS care are patients' habits, lifestyles, and fatigue-inducing activities. Although these subjective data seem more useful for HCPs, it is more challenging to visualize them in a useful and actionable way.
Conclusion HCPs are optimistic about mHealth and PGHD as tools to further understand their patients' needs and support care in MS. HCPs from different disciplines have different perceptions of what types of PGHD are useful; however, subjective types of PGHD seem potentially more useful for MS care.
... Consider research related to wearable devices [1][2][3]: The paper [1] discusses consumer trends in wearable electronics, commercial and new devices, as well as production methods. In [2], it is proposed to look at skin-like electronics by reviewing several recent reports on various strategies for using materials and methodologies for integrating stretchable conductive and semiconductor nanomaterials that are used as electrodes and active layers in stretchable sensors, transistors, multiplexed matrices and integrated circuits. ...
... In [2], it is proposed to look at skin-like electronics by reviewing several recent reports on various strategies for using materials and methodologies for integrating stretchable conductive and semiconductor nanomaterials that are used as electrodes and active layers in stretchable sensors, transistors, multiplexed matrices and integrated circuits. The first part of the review [3] briefly discusses issues related to the use of smart wearable devices, including technologies, users, technology-related activities and the consequences of using technologies. The second part of this review is devoted to the risks of using smart wearable devices. ...
... Japón cuenta además con una metodología de evaluación de tecnologías sanitarias específica para la MPP, pero no con un mecanismo de la evaluación de su implantación. La financiación para la MPP en Japón tiene procedencia pública 42,76,[83][84][85][86][87][88][89] . ...
Introducción: La medicina personalizada de precisión (MPP) es un proceso continuo y fundamental que pretende mejorar la eficacia y la eficiencia de la práctica clínica. Esta mejora se consigue con la comprensión de cómo las características biológicas únicas de los individuos y sus contextos sociales y ambientales contribuyen a su salud y enfermedad. La creciente personalización de la medicina se ve facilitada por el desarrollo de la tecnología, el conocimiento y la comprensión científica. Las tecnologías que son sinónimo de medicina personalizada, como la genómica, merecen una consideración especial en comparación con otras tecnologías que también están contribuyendo a la personalización de la medicina. Esta especial consideración se deriva del impacto que se prevé que tengan en los resultados de los pacientes y a la complejidad de los retos que su aplicación plantea a los sistemas sanitarios. Objetivo Este informe presenta una síntesis de evidencia independiente para informar al Ministerio de Sanidad del estado de la adopción de la MPP en la asistencia sanitaria pública de otros entornos internacionales. Esto permitirá, además, llevar a cabo un análisis de situación de la incorporación de la cartera de servicios de genómicas en otros sistemas sanitarios, así como sus metodologías de evaluación, y la generación de un marco de implementación en España. Método: Revisión sistemática de la literatura desde enero de 2016 a febrero de 2022 respondiendo al objetivo previamente planteado y siguiendo las recomendaciones de la declaración Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), utilizando bases de datos electrónicas: Medline, Embase, Web of Science. Además, se llevó a cabo una búsqueda secundaria que consistió en la exploración de los sitios web de los Ministerios de Salud, los documentos políticos publicados, las directrices nacionales, así como los sitios web de las principales agencias gubernamentales y organizaciones de investigación involucradas en la evaluación de las pruebas genéticas. Por último, se realizó una encuesta a los miembros de The International Network of Agencies for Health Technology Assessment (INAHTA) sobre la implementación de la cartera de servicios genómicos en el contexto de la MPP en sus sistemas nacionales de salud. Resultados: Se han obtenido un total de 42 dominios para determinar el estado de la implementación de la MPP. Estos dominios, a su vez, se han sido dividido en 12 categorías con opciones de múltiple respuesta. Se ha analizado el estado de implementación de la MPP en un total de 31 países divididos en 6 áreas geográficas (Europa central, Europa meridional, países de origen anglosajón, países nórdicos, países asiáticos y otros países) y en 26 iniciativas internacionales. Este análisis muestra un grado de implementación desigual y heterogéneo entre los diferentes países. Cabe destacar que las estrategias de implementación de MPP más desarrolladas, dentro de un sistema público de salud, son las de Australia y Reino Unido, siendo las que presentan una cartera de servicios genómicos con un mayor grado de integración en sus sistemas sanitarios. Conclusiones: Los países analizados que presentan una adopción de la MPP más desarrollada en sus sistemas sanitarios, tienen una serie de características comunes, ya implementadas, en curso o previstas. Estos aspectos suelen estar recogidos en una estrategia de MPP o en su defecto, de medicina genómica (MG), ya sea en forma de un plan nacional específico o como parte de los planes nacionales de salud en línea con los marcos internacionales existentes.
... Compared with traditional medicine, it can provide patients with more effective, cheaper and more timely medical services. Since it was proposed in 2015, it has been the key to global healthcare and one of the important goals of many sustainable development plans around the world [116,117]. The concept of precision medicine opens up new ideas for human health and healthcare [118,119]. ...
Due to its automatic feature learning ability and high performance, deep learning has gradually become the mainstream of artificial intelligence in recent years, playing a role in many fields. Especially in the medical field, the accuracy rate of deep learning even exceeds that of doctors. This paper introduces several deep learning algorithms: Artificial Neural Network (NN), FM-Deep Learning, Convolutional NN and Recurrent NN, and expounds their theory, development history and applications in disease prediction; we analyze the defects in the current disease prediction field and give some current solutions; our paper expounds the two major trends in the future disease prediction and medical field—integrating Digital Twins and promoting precision medicine. This study can better inspire relevant researchers, so that they can use this article to understand related disease prediction algorithms and then make better related research.
... In addition, in the field of surgery, AI 66 is also widely used, such as the application of da Vinci robot to efficiently perform fine surgery and minimally invasive surgery. 67 The application of tumor AI technology is also reflected in nursing, rehabilitation, and other aspects, and virtual reality technology is of great help for better rehabilitation 66 and decision-making of future treatment plans. 68 ...
Precision medicine for lung cancer theranostics is an advanced model combining prevention, diagnosis, and treatment for individual or specific population diseases to match individual patient differences. It involves collection and integration of genome, transcriptome, proteome, and metabolome features of lung cancer patients, combined with clinical characteristics. Subsequently, large data and artificial intelligence (AI) analysis have emerged to identify the most suitable therapeutic targets and personal treatment strategies for treatment of patients with lung cancer. We review the development and challenges associated with diagnosis and therapy of lung cancer from traditional technology, including immunotherapy prediction markers, liquid biopsy, surgery, and tumor immune microenvironment and patient-derived xenograft models, to AI in the era of precision medicine. AI has improved precision medicine and the predictive ability and accuracy of patient outcomes. Finally, we discuss some opportunities and challenges for lung cancer theranostics. Precision medicine in lung cancer can help us find the optimum treatment dose and time for a specific patient, which can advance the development of lung cancer therapeutics.
... AI has emerged with particular strength in the medical domain over the recent years, where there are prolific proofs of its advances, strengths and opportunities in prognosis, diagnosis, healthcare or preventive medicine [49][50][51][52][53]. Besides, AI can be applied in the public health landscape by leveraging social network and Web 2.0 media data, which in turn can be exploited to control drug abuse [54,55], toxic substance consumption [56], sexual and reproductive health [57], as well as healthy life habits [58][59][60]. ...
The17 Sustainable Development Goals (SDGs) established by the United Nations Agenda 2030 constitute a global blueprint agenda and instrument for peace and prosperity worldwide. Artificial intelligence and other digital technologies that have emerged in the last years, are being currently applied in virtually every area of society, economy and the environment. Hence, it is unsurprising that their current role in the pursuance or hampering of the SDGs has become critical. This study aims at providing a snapshot and comprehensive view of the progress made and prospects in the relationship between artificial intelligence technologies and the SDGs. A comprehensive review of existing literature has been firstly conducted, after which a series SWOT (Strengths, Weaknesses, Opportunities and Threats) analyses have been undertaken to identify the strengths, weaknesses, opportunities and threats inherent to artificial intelligence-driven technologies as facilitators or barriers to each of the SDGs. Based on the results of these analyses, a subsequent broader analysis is provided, from a position vantage, to (i) identify the efforts made in applying AI technologies in SDGs, (ii) pinpoint opportunities for further progress along the current decade, and (iii) distill ongoing challenges and target areas for important advances. The analysis is organized into six categories or perspectives of human needs: life, economic and technological development, social development, equality, resources and natural environment. Finally, a closing discussion is provided about the prospects, key guidelines and lessons learnt that should be adopted for guaranteeing a positive shift of artificial intelligence developments and applications towards fully supporting the SDGs attainment by 2030.
... Several studies have highlighted implementation challenges encountered in precision medicine solutions [155,156]. These challenges include data preprocessing, unstructured clinical text processing, medical data processing and storage, and environmental data collections. ...
... Apart from these challenges, the major challenge might be the redesigning of clinical decision support systems so that they can incorporate molecular, omics, and environmental aspects of precision medicine. A comprehensive support system is desirable to facilitate the curation of data from different sources and multiple scales and to promote the interaction between bioin-formatics and clinical informatics [155]. Building such a system requires solving many integration and standardization issues. ...
Resistance to therapy remains a major cause of cancer treatment failures, resulting in many cancer-related deaths. Resistance can occur at any time during the treatment, even at the beginning. The current treatment plan is dependent mainly on cancer subtypes and the presence of genetic mutations. Evidently, the presence of a genetic mutation does not always predict the therapeutic response and can vary for different cancer subtypes. Therefore, there is an unmet need for predictive models to match a cancer patient with a specific drug or drug combination. Recent advancements in predictive models using artificial intelligence have shown great promise in preclinical settings. However, despite massive improvements in computational power, building clinically useable models remains challenging due to a lack of clinically meaningful pharmacogenomic data. In this review, we provide an overview of recent advancements in therapeutic response prediction using machine learning, which is the most widely used branch of artificial intelligence. We describe the basics of machine learning algorithms, illustrate their use, and highlight the current challenges in therapy response prediction for clinical practice.
