A modified classification tree method for personalized medicine decisions

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.

Predictors and moderators of treatment efficacy in reducing custodial grandmothers' psychological distress

It is increasingly recommended that hypothesis-generating studies be conducted after initial RCTs in order to identify moderators of differential treatment efficacy on individual outcomes. Such analyses are important because they help clarify the best inclusion and exclusion criteria or choice of stratification for maximizing power in subsequent RCTs, reduce the chances of discarding interventions that may appear to lack efficacy when only average treatment effects are taken into consideration, and facilitate the matching of individual clients to treatment alternatives. We identified predictors and moderators of treatment-related change in psychological distress among custodial grandmothers (n = 343) assigned within a prior RCT to behavior parent training (BPT), cognitive behavior therapy (CBT), or information only control (IOC) conditions. Latent change scores in psychological distress were estimated for each grandmother across pre-test to post-test and pre-test to six months, as indicated by self-reported and clinical ratings of depression and anxiety symptoms. These estimates served as outcomes in classification and regression tree analyses conducted separately within the CBT and BPT conditions to identify predictors of treatment efficacy. Matched groups based upon identified predictors were then formed across all RCT conditions, and Predictor × RCT Condition interactions were computed to test for moderation of differential treatment efficacy. Grandmother age was the only predictor and moderator of BPT efficacy at both measurement points, whereas multifaceted predictors and moderators emerged for CBT which varied by time since treatment.

The Relationship between Timing of Pretransplant Kidney Biopsy, Graft Loss, and Survival in Kidney Transplantation: An Italian Cohort Study

INTRODUCTION

Kidney biopsy is performed to assess if an extended criteria graft can be used for transplantation. It may be performed before or after cross-clamping during organ procurement. This study aims to evaluate whether the timing of biopsy may modify cold ischemia times (CIT) and/or graft outcomes.

METHODS

Kidney transplants performed in our center from January 2007 to December 2017 were analyzed. Grafts with preimplantation kidney biopsy were included. Biopsies were performed during surgical back table (ex situ kidney biopsy [ESKB]) until 2012 and since then before the aortic cross-clamping (in situ kidney biopsy [ISKB]). To overcome biases owing to different distributions, a propensity score model was developed. The study population consists in 322 patients, 115 ESKB, and 207 ISKB.

RESULTS

CIT was significantly lower for ISKB (730 min ISKB vs. 840 min ESKB, p value = 0.001). In both crude (OR 0.27; 95% confidence interval, 95% CI 0.12-0.60; p value = 0.002) and adjusted analyses (OR 0.37; 95% CI 0.14-0.94; p value = 0.039), ISKB was associated with a reduced odd of graft loss when compared to ESKB.

DISCUSSION/CONCLUSION

Performing preimplantation kidney biopsy during the recovery, prior to the aortic cross-clamping, may be a strategy to reduce CIT and improve transplant outcomes.

A decision tree to guide long term venous access placement in children and adolescents undergoing surgery for renal tumors

BACKGROUND/PURPOSE

While many children with renal tumors require long term venous access (VA) for adjuvant chemotherapy, certainly not all do. This study develops and tests a VA decision tree (DT) to direct the placement of VA in patients with renal tumors.

METHODS

Utilizing data readily available at surgery a VADT was developed. The VADT was tested retrospectively by 2 independent reviewers on a historic cohort. The ability of the VADT to appropriately select which patients would benefit from VA placement was tested.

RESULTS

160 patients underwent renal tumor surgery between 2005 and 2018. 70 (43.8%) patients met study criteria with median age of 45.1 months (range 1.1-224); 73% required VA. Using the VADT, VA placement was "needed" in 67.1% of patients and "deferred" in 32.9%. Interrater reliability was very high (kappa = 0.97, 95% CI 0.91-1, p < 0.001). The sensitivity and specificity of the VADT to correctly decide on VA placement were 0.92 (0.8-0.98) and 1 (0.79-1). Using the VADT, no patient would have undergone unnecessary VA placement. In reality, 4.3% of patients had an unnecessary VA placed which required a subsequent removal.

CONCLUSIONS

These preliminary data support the continued study of this VADT to guide intraoperative decisions regarding VA placement in patients with renal tumors.

LEVEL OF EVIDENCE

III - Study of diagnostic test.

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.