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Machine learning has emerged as a significant tool to augment the medical decision-making process. Studies have steadily accrued detailing algorithms and models designed using machine learning to predict and anticipate pathologic states. The cardiac intensive care unit is an area where anticipation is crucial in the division between life and death. In this paper, we aim to review important studies describing the utility of machine learning algorithms to describe the future of artificial intelligence in the cardiac intensive care unit, especially in regards to the prediction of successful ventilatory weaning, acute respiratory distress syndrome, arrhythmia, and acute kidney injury.

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Health problems in older adults are often concomitant with multiple comorbidities and geriatric syndromes that involve the psychological and social domains. Traditional models of disease care address the health problems of older adults inadequately. Therefore, we applied a case management framework (assess, plan, act, coordinate, evaluate and interact) to discuss how to implement an elderly-centered approach to integrated care that integrates comprehensive, multidisciplinary, and continuous care. The Geriatrics Formulated by Outcome Related Care & Empowerment (Geri-FORCE) was developed by the Formosan Association of Care and Education for the Seniors to help establish a geriatric case management system grounded in precision health care. We propose developing an informatics technology system for older adults that integrates the Geri-FORCE model with case management. This system should accurately identify the main health problems in older adults and provide a care plan that is patient-tailored, integrated, and continuous. We expect that the developed Geri-FORCE case management system will improve quality of care and promote health while reducing care burdens and costs.

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BACKGROUND: Pharmacogenomic (PGx) testing has the potential to provide information on specific drug-metabolizing enzymes that may lead to an absence, reduction, or increase in medication effect in patients. There is a paucity of prospective studies examining PGx testing in the intensive care unit (ICU) setting. RESEARCH AIMS: To (1) obtain a PGx panel in a sample of cardiovascular (CV) surgical patients with a planned ICU stay and identify phenotypes, and (2) identify PGx variants that may inform treatment regimens and may warrant prescribing adjustments. DESIGN AND METHODS: Descriptive, single cohort cross-sectional design. Adult (≥18 years) CV patients with an anticipated postoperative ICU stay were enrolled from a large Midwestern tertiary academic medical center. Eligible patients provided informed consent at the time of their CV clinic appointment; PGx testing was then ordered. Pharmacogenomic testing consisted of the Focused Pharmacogenomics panel which included 10 genes and 55 medications. RESULTS: Of the 272 patients screened, 100 (68% male) patients completed PGx testing (mean age 66.2 ± 9.6 years, mean Acute Physiology, Age and Chronic Health Evaluation III score 76.1 ± standard deviation). Pharmacogenomic results were available in the medical record within a median of 52.4 hours (interquartile range: 33.4-80.3). Pharmacogenomic testing results identified 5 CYP2C19 poor metabolizers, 26 CYP2C19 rapid metabolizers, 5 CYP2C19 ultrarapid metabolizers, 6 CYP2D6 poor metabolizers, 5 CYP2D6 poor to intermediate metabolizers, and 2 CYP2D6 rapid metabolizers identified. Overall, 98% of patients had actionable or potentially actionable PGx results, including 82% for warfarin, 65% for propafenone, 65% for tramadol, 46% for oxycodone, 45% for metoprolol, 33% for clopidogrel, 32% for proton pump inhibitors, 25% for statins, and 12% for haloperidol. CONCLUSIONS: A significant portion of patients had identified genetic variants that may warrant changes in medication management during and after CV-ICU stay. It remains to be seen if PGx testing leads to improvements in ICU patient outcomes.

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Precision health care plays a crucial role in an elderly society by providing personalized health care plans for improving an individual's health conditions and preventing disease. To realize precision health care, data science is key; it allows for analyses of health-related big data. In this article, an actual analysis of time-series health check-up data is presented and as is a discussion of how personalized simulation models of health conditions are constructed and used to modify individual behavior. Future directions for precision health care based on the integration of genetic variations and the microbiome are also discussed.

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BACKGROUND: Personal health record (PHR) security, correctness, and protection are essential for health and medical services. Blockchain architecture can provide efficient data retrieval and security requirements. Exchangeable PHRs and the self-management of patient health can offer many benefits to traditional medical services by allowing people to manage their own health records for disease prevention, prediction, and control while reducing resource burdens on the health care infrastructure and improving population health and quality of life. OBJECTIVE: This study aimed to build a blockchain-based architecture for an international health record exchange platform to ensure health record confidentiality, integrity, and availability for health management and used Health Level 7 Fast Healthcare Interoperability Resource international standards as the data format that could allow international, cross-institutional, and patient/doctor exchanges of PHRs. METHODS: The PHR architecture in this study comprised 2 main components. The first component was the PHR management platform, on which users could upload PHRs, view their record content, authorize PHR exchanges with doctors or other medical health care providers, and check their block information. When a PHR was uploaded, the hash value of the PHR would be calculated by the SHA-256 algorithm and the PHR would be encrypted by the Rivest-Shamir-Adleman encryption mechanism before being transferred to a secure database. The second component was the blockchain exchange architecture, which was based on Ethereum to create a private chain. Proof of authority, which delivers transactions through a consensus mechanism based on identity, was used for consensus. The hash value was calculated based on the previous hash value, block content, and timestamp by a hash function. RESULTS: The PHR blockchain architecture constructed in this study is an effective method for the management and utilization of PHRs. The platform has been deployed in Southeast Asian countries via the Asia eHealth Information Network (AeHIN) and has become the first PHR management platform for cross-region medical data exchange. CONCLUSIONS: Some systems have shown that blockchain technology has great potential for electronic health record applications. This study combined different types of data storage modes to effectively solve the problems of PHR data security, storage, and transmission and proposed a hybrid blockchain and data security approach to enable effective international PHR exchange. By partnering with the AeHIN and making use of the network's regional reach and expert pool, the platform could be deployed and promoted successfully. In the future, the PHR platform could be utilized for the purpose of precision and individual medicine in a cross-country manner because of the platform's provision of a secure and efficient PHR sharing and management architecture, making it a reasonable base for future data collection sources and the data analytics needed for precision medicine.

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BACKGROUND: Synthetic data may provide a solution to researchers who wish to generate and share data in support of precision healthcare. Recent advances in data synthesis enable the creation and analysis of synthetic derivatives as if they were the original data; this process has significant advantages over data deidentification. OBJECTIVES: To assess a big-data platform with data-synthesizing capabilities (MDClone Ltd., Beer Sheva, Israel) for its ability to produce data that can be used for research purposes while obviating privacy and confidentiality concerns. METHODS: We explored three use cases and tested the robustness of synthetic data by comparing the results of analyses using synthetic derivatives to analyses using the original data using traditional statistics, machine learning approaches, and spatial representations of the data. We designed these use cases with the purpose of conducting analyses at the observation level (Use Case 1), patient cohorts (Use Case 2), and population-level data (Use Case 3). RESULTS: For each use case, the results of the analyses were sufficiently statistically similar (P > 0.05) between the synthetic derivative and the real data to draw the same conclusions. DISCUSSION AND CONCLUSION: This article presents the results of each use case and outlines key considerations for the use of synthetic data, examining their role in clinical research for faster insights and improved data sharing in support of precision healthcare.