AIMC Topic: Precision Medicine

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Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.

PloS one
Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal...

Recapitulation of Ayurveda constitution types by machine learning of phenotypic traits.

PloS one
In Ayurveda system of medicine individuals are classified into seven constitution types, "Prakriti", for assessing disease susceptibility and drug responsiveness. Prakriti evaluation involves clinical examination including questions about physiologic...

Estimation and evaluation of linear individualized treatment rules to guarantee performance.

Biometrics
In clinical practice, an informative and practically useful treatment rule should be simple and transparent. However, because simple rules are likely to be far from optimal, effective methods to construct such rules must guarantee performance, in ter...

The rise of artificial intelligence and the uncertain future for physicians.

European journal of internal medicine
Physicians in everyday clinical practice are under pressure to innovate faster than ever because of the rapid, exponential growth in healthcare data. "Big data" refers to extremely large data sets that cannot be analyzed or interpreted using traditio...

Precision Medicine: Genomic Profiles to Individualize Therapy.

Otolaryngologic clinics of North America
Precision medicine is the application of genotypic and Omics biomarkers to determine the most appropriate, outcome-driven therapy for individual patients. To determine the best choice of therapy, institutions use significant information technology-en...

Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework.

Scientific reports
Precision medicine approaches rely on obtaining precise knowledge of the true state of health of an individual patient, which results from a combination of their genetic risks and environmental exposures. This approach is currently limited by the lac...

Constrained binary classification using ensemble learning: an application to cost-efficient targeted PrEP strategies.

Statistics in medicine
Binary classification problems are ubiquitous in health and social sciences. In many cases, one wishes to balance two competing optimality considerations for a binary classifier. For instance, in resource-limited settings, an human immunodeficiency v...

CATTLE (CAncer treatment treasury with linked evidence): An integrated knowledge base for personalized oncology research and practice.

CPT: pharmacometrics & systems pharmacology
Despite the existence of various databases cataloging cancer drugs, there is an emerging need to support the development and application of personalized therapies, where an integrated understanding of the clinical factors and drug mechanism of action...

Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients.

Scientific reports
Tacrolimus has a narrow therapeutic window and considerable variability in clinical use. Our goal was to compare the performance of multiple linear regression (MLR) and eight machine learning techniques in pharmacogenetic algorithm-based prediction o...