Differential laboratory diagnosis of hypercalcemia

Artificial intelligence and machine learning in precision and genomic medicine

The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

Artificial intelligence and machine learning in precision and genomic medicine

The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine

Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare.

Severe metabolic abnormalities after allogeneic hematopoietic cell transplantation

Severe metabolic abnormalities occurring within 100 days after allogeneic hematopoietic cell transplantation (HCT) were investigated in 311 patients. The metabolic abnormalities included hyper- and hypocalcemia, hypophosphatemia, hyper- and hypokalemia, hyper- and hyponatremia, hyper- and hypomagnesemia, hypercholesterolemia, hyper- and hypoglycemia, and hyperuricemia. Severe abnormalities, defined as grades III-V by NCI CTCAE v3.0, occurred in 269 patients (86.5%). Multivariate analysis revealed that patients with moderate-to-severe hepatic veno-occlusive disease had significantly higher risk for the occurrence of severe metabolic abnormalities. Grades III-IV acute graft-versus-host disease was the most frequently associated with individual metabolic abnormalities. Patients with at least one severe metabolic abnormality had significantly higher day 100 nonrelapse mortality (P=0.015) and lower 5-year overall survival (P=0.002) than those without severe abnormalities. The number of metabolic abnormalities also stratified the patients with different clinical outcomes. In conclusion, severe metabolic abnormalities occurring within 100 days after allogeneic HCT were common, and their occurrence was significantly associated with inferior clinical outcomes. These results indicate that metabolic parameters should be monitored in patients undergoing allogeneic HCT and that the occurrence of severe metabolic abnormalities should be considered an important toxicity parameter in prospective clinical trials regarding allogeneic HCT.

Maximizing Efficacy of Endocrine Tests: Importance of Decision-focused Testing Strategies and Appropriate Patient Preparation

The efficacy of endocrine tests depends on the choice of tests, the preparation of the patients, the integrity of the specimens, the quality of the measurements, and the validity of the reference data. Close dialogue among the clinicians, the laboratory, and the patients is a key factor for optimal patient care. The characteristics of urine and plasma samples and the advantages and limitations of paired test measurements are presented. The importance of test sequence strategies, provocative or inhibitory procedures, and elimination of drug interferences is illustrated with four cases involving Cushing syndrome, pheochromocytoma, primary aldosteronism, and hypercalcemia. For each of these scenarios, key clinical issues are highlighted, along with discussions of the best test strategies, including which medications are likely to interfere. The importance of targeting laboratory tests to answer well-focused clinical decisions is emphasized. The roles of some time-honored provocative procedures are questioned in light of more sensitive and specific analytic methods. The importance of decision-focused analytical tolerance limits is emphasized by demonstrating the impact of analytic bias on downstream medical resource utilization. User-friendly support systems to facilitate the implementation of test strategies and postanalytic tracking of patient outcomes are presented as essential requirements for quality medical practice.