AI Medical Compendium Journal:
Clinical pharmacology and therapeutics

Showing 31 to 40 of 59 articles

Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology.

Clinical pharmacology and therapeutics
The amount of "big" data generated in clinical oncology, whether from molecular, imaging, pharmacological, or biological origin, brings novel challenges. To mine efficiently this source of information, mathematical models able to produce predictive a...

Strategies for Testing Intervention Matching Schemes in Cancer.

Clinical pharmacology and therapeutics
Personalized medicine, or the tailoring of health interventions to an individual's nuanced and often unique genetic, biochemical, physiological, behavioral, and/or exposure profile, is seen by many as a biological necessity given the great heterogene...

Leading a Digital Transformation in the Pharmaceutical Industry: Reimagining the Way We Work in Global Drug Development.

Clinical pharmacology and therapeutics
We are experiencing seminal times in computing that seem to define a fourth industrial revolution. This may fundamentally change the way we live, work, and relate to one another. Embracing data and digital information is a top priority for most indus...

Predicting Inpatient Medication Orders From Electronic Health Record Data.

Clinical pharmacology and therapeutics
In a general inpatient population, we predicted patient-specific medication orders based on structured information in the electronic health record (EHR). Data on over three million medication orders from an academic medical center were used to train ...

An Introduction to Machine Learning.

Clinical pharmacology and therapeutics
In the last few years, machine learning (ML) and artificial intelligence have seen a new wave of publicity fueled by the huge and ever-increasing amount of data and computational power as well as the discovery of improved learning algorithms. However...

Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology?

Clinical pharmacology and therapeutics
As the field of artificial intelligence and machine learning (AI/ML) for drug discovery is rapidly advancing, we address the question "What is the impact of recent AI/ML trends in the area of Clinical Pharmacology?" We address difficulties and AI/ML ...

Broad-Spectrum Profiling of Drug Safety via Learning Complex Network.

Clinical pharmacology and therapeutics
Drug safety is a severe clinical pharmacology and toxicology problem that has caused immense medical and social burdens every year. Regretfully, a reproducible method to assess drug safety systematically and quantitatively is still missing. In this s...

An Application of Machine Learning in Pharmacovigilance: Estimating Likely Patient Genotype From Phenotypical Manifestations of Fluoropyrimidine Toxicity.

Clinical pharmacology and therapeutics
Dihydropyrimidine dehydrogenase (DPD)-deficient patients might only become aware of their genotype after exposure to dihydropyrimidines, if testing is performed. Case reports to pharmacovigilance databases might only contain phenotypical manifestatio...

Model-Informed Artificial Intelligence: Reinforcement Learning for Precision Dosing.

Clinical pharmacology and therapeutics
The availability of multidimensional data together with the development of modern techniques for data analysis represent an exceptional opportunity for clinical pharmacology. Data science-defined in this special issue as the novel approaches to the c...

Pharmacometrics and Machine Learning Partner to Advance Clinical Data Analysis.

Clinical pharmacology and therapeutics
Clinical pharmacology is a multidisciplinary data sciences field that utilizes mathematical and statistical methods to generate maximal knowledge from data. Pharmacometrics (PMX) is a well-recognized tool to characterize disease progression, pharmaco...