AIMC Topic: Drug Therapy

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Assessing the accuracy and quality of artificial intelligence (AI) chatbot-generated responses in making patient-specific drug-therapy and healthcare-related decisions.

BMC medical informatics and decision making Dec 24, 2024
BACKGROUND: Interactive artificial intelligence tools such as ChatGPT have gained popularity, yet little is known about their reliability as a reference tool for healthcare-related information for healthcare providers and trainees. The objective of t...
Delphi Technique Drug Therapy Humans Artificial Intelligence Clinical Decision-Making Reproducibility of Results
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Evaluation of artificial intelligence-generated drug therapy communication skill competencies in medical education.

British journal of clinical pharmacology Jul 2, 2024
AIMS: This study compared three artificial intelligence (AI) platforms' potential to identify drug therapy communication competencies expected of a graduating medical doctor.
Humans Patient-Centered Care Education, Medical Drug Therapy Artificial Intelligence Communication Clinical Competence
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Local-Global Memory Neural Network for Medication Prediction.

IEEE transactions on neural networks and learning systems Apr 2, 2021
Electronic medical records (EMRs) play an important role in medical data mining and sequential data learning. In this article, we propose to use a sequential neural network with dynamic content-based memories to predict future medications, given EMRs...
Electronic Health Records Models, Theoretical Pharmaceutical Preparations Data Mining Drug Therapy Neural Networks, Computer Machine Learning Algorithms Humans
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Artificial Intelligence in Pharmacovigilance: Scoping Points to Consider.

Clinical therapeutics Jan 18, 2021
Artificial intelligence (AI), a highly interdisciplinary science, is an increasing presence in pharmacovigilance (PV). A better understanding of the scope of artificial intelligence in pharmacovigilance (AIPV) may be advantageous to more sharply defi...
Artificial Intelligence Machine Learning Pharmacovigilance Humans Datasets as Topic Drug Therapy
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A novel approach for personalized response model: deep learning with individual dropout feature ranking.

Journal of pharmacokinetics and pharmacodynamics Oct 26, 2020
Deep learning is the fastest growing field in artificial intelligence and has led to many transformative innovations in various domains. However, lack of interpretability sometimes hinders its application in hypothesis-driven domains such as biology ...
Treatment Outcome Models, Biological Datasets as Topic Computer Simulation Precision Medicine Deep Learning Drug Therapy Biological Variation, Population Humans
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Emerging Pharmacotherapy and Health Care Needs of Patients in the Age of Artificial Intelligence and Digitalization.

The Annals of pharmacotherapy May 28, 2020
Advances in the application of artificial intelligence, digitization, technology, iCloud computing, and wearable devices in health care predict an exciting future for health care professionals and our patients. Projections suggest an older, generally...
Male Decision Support Systems, Clinical Telemedicine Digital Technology Drug Therapy Female Aged Aged, 80 and over Humans Artificial Intelligence Delivery of Health Care
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Multigene signatures of responses to chemotherapy derived by biochemically-inspired machine learning.

Molecular genetics and metabolism Aug 19, 2019
Pharmacogenomic responses to chemotherapy drugs can be modeled by supervised machine learning of expression and copy number of relevant gene combinations. Such biochemical evidence can form the basis of derived gene signatures using cell line data, w...
Cell Line, Tumor Urinary Bladder Neoplasms Metabolic Networks and Pathways Supervised Machine Learning Gene Dosage Drug Therapy Breast Neoplasms Gene Expression Profiling Transcriptome Humans Female
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Methodological variations in lagged regression for detecting physiologic drug effects in EHR data.

Journal of biomedical informatics Aug 30, 2018
We studied how lagged linear regression can be used to detect the physiologic effects of drugs from data in the electronic health record (EHR). We systematically examined the effect of methodological variations ((i) time series construction, (ii) tem...
Area Under Curve Pharmaceutical Preparations Data Collection Academic Medical Centers Linear Models New York City Time Factors ROC Curve Regression Analysis Databases, Factual Electronic Health Records Reproducibility of Results Humans Drug Therapy Machine Learning
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Automated ontology generation framework powered by linked biomedical ontologies for disease-drug domain.

Computer methods and programs in biomedicine Aug 16, 2018
OBJECTIVE AND BACKGROUND: The exponential growth of the unstructured data available in biomedical literature, and Electronic Health Record (EHR), requires powerful novel technologies and architectures to unlock the information hidden in the unstructu...
Decision Support Systems, Clinical Knowledge Bases Data Mining Humans Machine Learning Drug Therapy Algorithms Semantics Biological Ontologies Disease
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Drug Repositioning by Integrating Known Disease-Gene and Drug-Target Associations in a Semi-supervised Learning Model.

Acta biotheoretica Apr 26, 2018
Computational drug repositioning has been proven as a promising and efficient strategy for discovering new uses from existing drugs. To achieve this goal, a number of computational methods have been proposed, which are based on different data sources...
Computational Biology Humans Area Under Curve Models, Statistical Pharmacology Software Pharmaceutical Preparations Drug Repositioning Drug Therapy Reproducibility of Results Supervised Machine Learning Pharmacy
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