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Results: 1 to 5 of 5

Publication Record


Benefits and Risks of Machine Learning Decision Support Systems.
Lasko TA, Walsh CG, Malin B
(2017) JAMA 318: 2355
MeSH Terms: Decision Support Systems, Clinical, Expert Systems, Humans, Machine Learning, Risk Assessment
Added March 14, 2018
0 Communities
2 Members
0 Resources
5 MeSH Terms
Automated quantification of pancreatic β-cell mass.
Golson ML, Bush WS, Brissova M
(2014) Am J Physiol Endocrinol Metab 306: E1460-7
MeSH Terms: Animals, Artificial Intelligence, Automation, Laboratory, Cell Size, Computational Biology, Diabetes Mellitus, Experimental, Expert Systems, Hyperplasia, Image Processing, Computer-Assisted, Insulin-Secreting Cells, Mice, Mice, Inbred C57BL, Mice, Inbred Strains, Mice, Obese, Microtomy, Models, Biological, Obesity, Pancreas, Reproducibility of Results, Software
Show Abstract · Added July 15, 2015
β-Cell mass is a parameter commonly measured in studies of islet biology and diabetes. However, the rigorous quantification of pancreatic β-cell mass using conventional histological methods is a time-consuming process. Rapidly evolving virtual slide technology with high-resolution slide scanners and newly developed image analysis tools has the potential to transform β-cell mass measurement. To test the effectiveness and accuracy of this new approach, we assessed pancreata from normal C57Bl/6J mice and from mouse models of β-cell ablation (streptozotocin-treated mice) and β-cell hyperplasia (leptin-deficient mice), using a standardized systematic sampling of pancreatic specimens. Our data indicate that automated analysis of virtual pancreatic slides is highly reliable and yields results consistent with those obtained by conventional morphometric analysis. This new methodology will allow investigators to dramatically reduce the time required for β-cell mass measurement by automating high-resolution image capture and analysis of entire pancreatic sections.
1 Communities
1 Members
1 Resources
20 MeSH Terms
Detecting adverse events for patient safety research: a review of current methodologies.
Murff HJ, Patel VL, Hripcsak G, Bates DW
(2003) J Biomed Inform 36: 131-43
MeSH Terms: Database Management Systems, Databases, Factual, Decision Support Techniques, Documentation, Expert Systems, Information Storage and Retrieval, Medical Audit, Medical Errors, Medical Records Systems, Computerized, Models, Statistical, Patient Care Management, Quality Assurance, Health Care, Risk Assessment, Risk Management, Safety Management, Statistics as Topic
Show Abstract · Added March 5, 2014
Promoting patient safety is a national priority. To evaluate interventions for reducing medical errors and adverse event, effective methods for detecting such events are required. This paper reviews the current methodologies for detection of adverse events and discusses their relative advantages and limitations. It also presents a cognitive framework for error monitoring and detection. While manual chart review has been considered the "gold-standard" for identifying adverse events in many patient safety studies, this methodology is expensive and imperfect. Investigators have developed or are currently evaluating, several electronic methods that can detect adverse events using coded data, free-text clinical narratives, or a combination of techniques. Advances in these systems will greatly facilitate our ability to monitor adverse events and promote patient safety research. But these systems will perform optimally only if we improve our understanding of the fundamental nature of errors and the ways in which the human mind can naturally, but erroneously, contribute to the problems that we observe.
0 Communities
1 Members
0 Resources
16 MeSH Terms
Medical informatics and pediatrics. Decision-support systems.
Johnson KB, Feldman MJ
(1995) Arch Pediatr Adolesc Med 149: 1371-80
MeSH Terms: Attitude to Computers, Databases, Factual, Decision Making, Computer-Assisted, Decision Support Techniques, Diagnosis, Computer-Assisted, Expert Systems, Humans, Motivation, Pediatrics, Physicians, Practice Guidelines as Topic, Reminder Systems
Show Abstract · Added February 12, 2015
Decision support is an important area of medical informatics research. Computer-based decision-support tools facilitate diagnosis and the management of patients after a diagnosis has been established. Diagnostic decision-support tools, such as Meditel, Quick Medical Reference, DXplain, Iliad, and PEM-DXP are potentially useful "expert systems." Other management-support tools, such as systems that use clinical practice guidelines to create reminders and alerts, also have been developed and evaluated. We do the following: (1) to provide an overview of diagnostic and management decision-support systems; (2) explore the background of and motivation behind these systems; (3) survey the uses of decision-support technology in office-based and inpatient pediatric practices; and (4) discuss the virtues and problems associated with some of these tools, and current controversies and future goals for computer-based decision support.
0 Communities
1 Members
0 Resources
12 MeSH Terms
Knowledge-based approach to sleep EEG analysis--a feasibility study.
Jansen BH, Dawant BM
(1989) IEEE Trans Biomed Eng 36: 510-8
MeSH Terms: Electroencephalography, Expert Systems, Feasibility Studies, Signal Processing, Computer-Assisted, Sleep Stages
Show Abstract · Added April 10, 2018
A knowledge-based approach to automated sleep EEG (electroencephalogram) analysis is described. In this system, an object-oriented approach is followed in which specific waveforms and sleep stages ("objects") are represented in terms of frames. The latter capture the morphological and spatio-temporal information for each object. An object detection module ("frame matcher"), operating on the frames, is employed to identify what features need to be extracted from the EEG and to trigger the appropriate "specialist"--specialized signal processing modules--to obtain values for these features. This leads to an opportunistic approach to EEG interpretation with quantitative information being extracted from the signal only when needed by the reasoning processes. The system has been tested on the detection of K complexes and sleep spindles. Its performance indicates that the approach followed is feasible and can become a powerful tool for automated EEG interpretation.
0 Communities
1 Members
0 Resources
MeSH Terms