AIMC Topic: Humans

Research on Humans in artificial intelligence and machine learning in medicine, selected for practicing healthcare professionals. Page 6428.

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Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study.

The Lancet. Oncology
BACKGROUND: Whether machine-learning algorithms can diagnose all pigmented skin lesions as accurately as human experts is unclear. The aim of this study was to compare the diagnostic accuracy of state-of-the-art machine-learning algorithms with human...

Predictive Analysis of First Abbreviated New Drug Application Submission for New Chemical Entities Based on Machine Learning Methodology.

Clinical pharmacology and therapeutics
Generic drug products are approved by the US Food and Drug Administration (FDA) through Abbreviated New Drug Applications (ANDAs). The ANDA review and approval involves multiple offices across the FDA. Forecasting ANDA submissions can critically info...

Isokinetic Robotic Device to Improve Test-Retest and Inter-Rater Reliability for Stretch Reflex Measurements in Stroke Patients with Spasticity.

Journal of visualized experiments : JoVE
Measuring spasticity is important in treatment planning and determining efficacy after treatment. However, the current tool used in clinical settings has been shown to be limited in inter-rater reliability. One factor in this poor inter-rater reliabi...

A Multi-Sensor System for Silkworm Cocoon Gender Classification via Image Processing and Support Vector Machine.

Sensors (Basel, Switzerland)
Sericulture is traditionally a labor-intensive rural-based industry. In modern contexts, the development of process automation faces new challenges related to quality and efficiency. During the silkworm farming life cycle, a common issue is represent...

Consensus Development of a Modern Ontology of Emergency Department Presenting Problems-The Hierarchical Presenting Problem Ontology (HaPPy).

Applied clinical informatics
OBJECTIVE: Numerous attempts have been made to create a standardized "presenting problem" or "chief complaint" list to characterize the nature of an emergency department visit. Previous attempts have failed to gain widespread adoption as they were no...

On the impact of Citizen Science-derived data quality on deep learning based classification in marine images.

PloS one
The evaluation of large amounts of digital image data is of growing importance for biology, including for the exploration and monitoring of marine habitats. However, only a tiny percentage of the image data collected is evaluated by marine biologists...

Adjusting the dose in paediatric care: dispersing four different aspirin tablets and taking a proportion.

European journal of hospital pharmacy : science and practice
OBJECTIVES: When caring for children in a hospital setting, tablets are often manipulated at the ward to obtain the right dose. One example is manipulation of tablets containing the slightly water-soluble substance aspirin, used in paediatric care as...

Time to Move On: The Impending Need for a New Disease-specific Comorbidity Index for Bladder Cancer Patients Undergoing Robot-assisted Radical Cystectomy.

European urology focus
The Charlson comorbidity index is an outdated comorbidity assessment tool which is not disease specific and is not applicable to contemporary BCa patients.

Physicochemical stability of nefopam and nefopam/droperidol solutions in polypropylene syringes for intensive care units.

European journal of hospital pharmacy : science and practice
INTRODUCTION: Nefopam has been reported to be effective in postoperative pain control with an opioid-sparing effect, but the use of nefopam can lead to nausea and vomiting. To prevent these side effects, droperidol can be mixed with nefopam. In inten...

A Machine Learning Approach to Classifying Self-Reported Health Status in a Cohort of Patients With Heart Disease Using Activity Tracker Data.

IEEE journal of biomedical and health informatics
Constructing statistical models using personal sensor data could allow for tracking health status over time, thereby enabling the possibility of early intervention. The goal of this study was to use machine learning algorithms to classify patient-rep...