AIMC Topic: Patient Selection

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Optimizing clinical trials recruitment via deep learning.

Journal of the American Medical Informatics Association : JAMIA Nov 1, 2019
OBJECTIVE: Clinical trials, prospective research studies on human participants carried out by a distributed team of clinical investigators, play a crucial role in the development of new treatments in health care. This is a complex and expensive proce...
Data Mining Humans Research Personnel Databases, Factual Clinical Trials as Topic Insurance Claim Reporting Electronic Health Records Deep Learning Patient Selection
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Use of Artificial Intelligence to Represent Emergent Systems and Augment Surgical Decision-making.

JAMA surgery Sep 1, 2019
Humans Patient Selection Artificial Intelligence Surgical Procedures, Operative Risk Assessment Intensive Care Units Surgery Department, Hospital Emergencies Clinical Decision-Making United States Female Male
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Impact of Artificial Intelligence on Interventional Cardiology: From Decision-Making Aid to Advanced Interventional Procedure Assistance.

JACC. Cardiovascular interventions Jul 22, 2019
Access to big data analyzed by supercomputers using advanced mathematical algorithms (i.e., deep machine learning) has allowed for enhancement of cognitive output (i.e., visual imaging interpretation) to previously unseen levels and promises to funda...
Clinical Decision-Making Cardiovascular Diseases Diagnosis, Computer-Assisted Deep Learning Humans Machine Learning Decision Support Techniques Therapy, Computer-Assisted Cardiac Catheterization Workflow Unsupervised Machine Learning Supervised Machine Learning Predictive Value of Tests Patient Selection Robotics Diffusion of Innovation
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Criteria2Query: a natural language interface to clinical databases for cohort definition.

Journal of the American Medical Informatics Association : JAMIA Apr 1, 2019
OBJECTIVE: Cohort definition is a bottleneck for conducting clinical research and depends on subjective decisions by domain experts. Data-driven cohort definition is appealing but requires substantial knowledge of terminologies and clinical data mode...
Patient Selection Databases as Topic Information Storage and Retrieval Clinical Trials as Topic Machine Learning User-Computer Interface Natural Language Processing Eligibility Determination Humans
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Machine Learning Algorithm Helps Identify Non-Diagnosed Prodromal Alzheimer's Disease Patients in the General Population.

The journal of prevention of Alzheimer's disease Jan 1, 2019
BACKGROUND: Recruiting patients for clinical trials of potential therapies for Alzheimer's disease (AD) remains a major challenge, with demand for trial participants at an all-time high. The AD treatment R and D pipeline includes around 112 agents. I...
Male Middle Aged Diagnosis, Computer-Assisted Patient Selection Alzheimer Disease Early Diagnosis Prodromal Symptoms Algorithms Female Aged Aged, 80 and over Humans Machine Learning
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Does global oncology need artificial intelligence?

The Lancet. Oncology May 1, 2018
Humans Artificial Intelligence Decision Support Techniques Clinical Decision-Making Patient Selection Global Health Neoplasms Medical Oncology Decision Support Systems, Clinical Therapy, Computer-Assisted Delivery of Health Care, Integrated
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SemEHR: A general-purpose semantic search system to surface semantic data from clinical notes for tailored care, trial recruitment, and clinical research.

Journal of the American Medical Informatics Association : JAMIA May 1, 2018
OBJECTIVE: Unlocking the data contained within both structured and unstructured components of electronic health records (EHRs) has the potential to provide a step change in data available for secondary research use, generation of actionable medical i...
Clinical Trials as Topic Humans Electronic Health Records Natural Language Processing Patient Selection United Kingdom Information Storage and Retrieval Semantics State Medicine
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EliIE: An open-source information extraction system for clinical trial eligibility criteria.

Journal of the American Medical Informatics Association : JAMIA Nov 1, 2017
OBJECTIVE: To develop an open-source information extraction system called Eligibility Criteria Information Extraction (EliIE) for parsing and formalizing free-text clinical research eligibility criteria (EC) following Observational Medical Outcomes P...
Natural Language Processing Patient Selection Clinical Trials as Topic Eligibility Determination Humans Machine Learning
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Automated classification of eligibility criteria in clinical trials to facilitate patient-trial matching for specific patient populations.

Journal of the American Medical Informatics Association : JAMIA Jul 1, 2017
OBJECTIVE: To develop automated classification methods for eligibility criteria in ClinicalTrials.gov to facilitate patient-trial matching for specific populations such as persons living with HIV or pregnant women.
Eligibility Determination HIV Seropositivity Patient Selection Datasets as Topic Clinical Trials as Topic Humans Machine Learning Neoplasms Natural Language Processing
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Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation.

Transplantation Apr 1, 2017
BACKGROUND: The ability to predict graft failure or primary nonfunction at liver transplant decision time assists utilization of scarce resource of donor livers, while ensuring that patients who are urgently requiring a liver transplant are prioritiz...
Graft Survival Predictive Value of Tests Area Under Curve Liver Transplantation Time Factors Risk Factors ROC Curve Aged Tissue Donors Young Adult Decision Support Techniques Patient Selection Adult Male Databases, Factual Treatment Failure Humans Middle Aged Female Donor Selection Machine Learning Risk Assessment Reproducibility of Results Victoria Algorithms Adolescent
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