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A Medical Informatics Perspective on Decision Support

P. Ruch
2009 IMIA Yearbook of Medical Informatics  
to process large patient sets; 2. narrow clinical applications focused on in-depth real-time signal processing for a specific population or medical specialty.  ...  Altogether these papers support the idea that more elaborated computer tools, likely to combine together textual and highly structured data, including real-time data contents, are needed.  ...  I also would like to thank Casimir Kulikowski and Antoine Geissbuhler for the inspired discussions we had during the selection process.  ... 
doi:10.1055/s-0038-1638645 fatcat:evnpaip6gjderkurxs6bw56oza

A Decision Support System For Predicting Hospitalization Of Hemodialysis Patients

Jinn-Yi Yeh, Tai-Hsi Wu
2009 Zenodo  
In this study we combined temporal abstraction with data mining techniques for analyzing the dialysis patients' biochemical data to develop a decision support system.  ...  The mined temporal patterns are helpful for clinicians to predict hospitalization of hemodialysis patients and to suggest them some treatments immediately to avoid hospitalization.  ...  ACKNOWLEDGMENT We are grateful to the National Science Council for the grants program (NSC 96-2221-E-415-005), and to the Hemodialysis Center of Chiayi Yang Ming Hospital for their professional consultations  ... 
doi:10.5281/zenodo.1331128 fatcat:b7cdiau5wrehplmqz7v7bo6vr4

Application of K-Means Technique in Data Mining to Cluster Hemodialysis Patients

Reza Ghodsi
2017 International Robotics & Automation Journal  
The results obtained from the data mining model in this research can be useful for the decision makers and doctors.  ...  In the next stage, the data was processed and prepared to be used as the input data for the implementation of data mining techniques.  ...  The goal of this research is to use data mining techniques to help create such a decision support tool for medical doctors dealing with ESRD patients in future.  ... 
doi:10.15406/iratj.2017.02.00013 fatcat:fapgeuihxbgbfk7u5njoy767ua

Predicting the Appearance of Hypotension during Hemodialysis Sessions Using Machine Learning Classifiers

Juan A. Gómez-Pulido, José M. Gómez-Pulido, Diego Rodríguez-Puyol, María-Luz Polo-Luque, Miguel Vargas-Lombardo
2021 International Journal of Environmental Research and Public Health  
However, the analytical information is not always available to the healthcare personnel, or it is far in time, so the clinical parameters monitored during the session become key to the prevention of hypotension  ...  The prediction model takes into account up to 22 clinical parameters measured five times during the session, as well as the gender and age of the patient.  ...  Its technology is particularly suited for analyzing medical data and making decisions in medical diagnostic problems [26] .  ... 
doi:10.3390/ijerph18052364 pmid:33671029 fatcat:3qc7pf4hkjdulaxxdhjrqvrfzy

Data mining in predicting survival of kidney dialysis patients

Shital Shah, Andrew Kusiak, Brad Dixon, Lawrence S. Bass, Nikiforos Kollias, Reza S. Malek, Abraham Katzir, Udayan K. Shah, Brian J. F. Wong, Eugene A. Trowers, Timothy A. Woodward, Werner T. W. de Riese (+4 others)
2003 Lasers in Surgery: Advanced Characterization, Therapeutics, and Systems XIII  
Two different data mining algorithms are employed for extracting knowledge in the form of decision rules.  ...  In this research, a data mining approach is used to elicit knowledge about the interaction between these variables and patient survival.  ...  ACKNOWLEDGEMENT Our special thanks to Katherine Ruth Coates, Sara Ann Dolny, Katherine Gaunt, and Katie Louise Kruse for preparation different versions of data sets.  ... 
doi:10.1117/12.476387 fatcat:k262lwh36rbxxnudq5tmjnggom

Development, implementation and user experience of the Veterans Health Administration (VHA) dialysis dashboard

Michael J. Fischer, Wissam M. Kourany, Karen Sovern, Kurt Forrester, Cassandra Griffin, Nancy Lightner, Shawn Loftus, Katherine Murphy, Greg Roth, Paul M. Palevsky, Susan T. Crowley
2020 BMC Nephrology  
Data on user experience and perceptions were collected via an e-mail questionnaire to dialysis medical directors and nurse managers at these facilities.  ...  Respondents indicated that they used the dialysis dashboard for clinical reporting (71%), quality assessment/performance improvement (QAPI) (62%), and decision-making (23%).  ...  While clinical dashboards provide timely clinical data to clinicians so that they can make informed daily decisions about patient care, quality dashboards display information on performance measures at  ... 
doi:10.1186/s12882-020-01798-6 pmid:32299383 fatcat:5ex6lv2kqrad7ox3hivsukefpy

Guest Editorial Special Section on New and Emerging Technologies in Bioinformatics and Bioengineering

Konstantina S. Nikita, Dimitrios I. Fotiadis
2010 IEEE Transactions on Information Technology in Biomedicine  
Computational Intelligence and Data Mining in Support of Decision Making in Biomedicine Machine learning and data mining facilitates biomedical data exploration using data analysis methods with sophisticated  ...  9] - [11] to imaging [12] , [13] and decision making [7] , [14] in the life sciences domain.  ... 
doi:10.1109/titb.2010.2048660 pmid:20684050 fatcat:gwerjlnpxzh7nnlmadzvzjigue

Integrating Real Time Data to Improve Outcomes in Acute Kidney Injury

Jamie S. Hirsch, Sumit Mohan
2015 Nephron  
This connection facilitates automated data capture of many variables -including delivered dose, ultrafiltration rate and pressure measurements -which in turn can be leveraged for data mining, quality improvement  ...  One major barrier to effective care of these patients is the traditional dissociation of dialysis device data from other clinical information systems, notably the electronic health record (EHR).  ...  coordination with other clinical variables and precludes improving clinical outcomes using automated data driven clinical decision-making [26] .  ... 
doi:10.1159/000441981 pmid:26575177 fatcat:ruxbgow25faj3atfib4tnxw5ci

Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy

Miguel Hueso, Alfredo Vellido, Nuria Montero, Carlo Barbieri, Rosa Ramos, Manuel Angoso, Josep Maria Cruzado, Anders Jonsson
2018 Kidney Diseases  
Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient's quality of life.  ...  These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the  ...  to decision making.  ... 
doi:10.1159/000486394 pmid:29594137 pmcid:PMC5848485 fatcat:x24qgxt2bnh7xnzwafc4o2vnvy

An architecture of Multiagent System (MAS) for healthcare Intelligent Decision Support System (IDSS)

N Mahiddin, Z.A. Othman, A.A. Bakar
2018 Journal of Fundamental and Applied Sciences  
Currently, the healthcare domain is very complex with a growing of medical knowledge, a growing size of medical data, a dynamic data and process, a distributed environment and a concurring decision making  ...  Consequently, in [8] suggested a healthcare decision making process framework to clarify those healthcare decision making processes.  ...  Currently, DSS development also showed a wide use of data mining technique. This is because the efficacy of using the data mining approach or known as intelligent technique in the DSS development.  ... 
doi:10.4314/jfas.v9i5s.12 fatcat:2uqh2ygmlbak3ipcpkwjhnc5pa

Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review

Alexandru Burlacu, Adrian Iftene, Daniel Jugrin, Iolanda Valentina Popa, Paula Madalina Lupu, Cristiana Vlad, Adrian Covic
2020 BioMed Research International  
We identified 43 trials in the first group, 8 in the second, and 18 in the third. Then, studies were classified according to the type of algorithm. Results.  ...  We searched four electronic databases or studies that used AI/ML in hemodialysis (HD), peritoneal dialysis (PD), and kidney transplantation (KT).  ...  Supplemental Table 2 : short description and results of the studies involving AI in PD.  ... 
doi:10.1155/2020/9867872 pmid:32596403 pmcid:PMC7303737 fatcat:xtqwjydqnzaezicg56zrwekl4q

Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

Matthew J. Wills, Surendra Sarnikar, Omar F. El-Gayar, Amit V. Deokar
2010 Communications of the Association for Information Systems  
In this paper, we focus on clinical knowledge management systems (CKMS) research.  ...  The objectives of the paper are to evaluate the current state of knowledge management systems diffusion in the clinical setting, assess the present status and focus of CKMS research efforts, and identify  ...  One system was noted to facilitate knowledge sharing with respect to ethical decision-making in the clinical setting [Frize et al., 2005] .  ... 
doi:10.17705/1cais.02626 fatcat:gv5a4f3byrczff6ffc3ptk3mzi

Predictive data mining in clinical medicine: a focus on selected methods and applications

Riccardo Bellazzi, Fulvia Ferrazzi, Lucia Sacchi
2011 Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery  
Predictive data mining in clinical medicine deals with learning models to predict patients' health. The models can be devoted to support clinicians in diagnostic, therapeutic, or monitoring tasks.  ...  Data mining methods are usually applied in clinical contexts to analyze retrospective data, thus giving healthcare professionals the opportunity to exploit large amounts of data routinely collected during  ...  In a clinical scenario, such patterns can be conveniently used as a decisional support tool to predict future healthcare events.  ... 
doi:10.1002/widm.23 fatcat:n6juaunarbcclkfur3ojwb6zpq

A comparison between physicians and computer algorithms for form CMS-2728 data reporting

Mohammed Said Malas, Jay Wish, Ranjani Moorthi, Shaun Grannis, Paul Dexter, Jon Duke, Sharon Moe
2016 Hemodialysis International  
We used all available procedure, billing, laboratory, and prescription data to generate predictive models of physician annotations.  ...  We aimed to examine the predictability of provider annotations for 8 clinical conditions.  ...  Finally, the use of machine learning techniques can be a valuable tool in understanding annotations patterns in order to guide development of decision support systems. Disclosures  ... 
doi:10.1111/hdi.12445 pmid:27353890 fatcat:qcl2o6y7qndvrewbdlleq4ku6i

Neuro-Fuzzy Technology as a Predictor of Parathyroid Hormone Level in Hemodialysis Patients

Chiou-An Chen, Yu-Chuan Li, Yuh-Feng Lin, Fu-Chiu Yu, Wei-Hsin Huang, Jainn-Shiun Chiu
2007 Tohoku journal of experimental medicine  
Neuro-fuzzy technology is increasingly used for data analysis and decision-making purposes in clinical medicine (Hanai and Honda 2004; Ramesh et al. 2004 ).  ...  Our reason is that for any forecasting model to be useful in making clinical decisions, it should use only parameters that are readily available to the clinician at the time of triage (Chiu et al. 2005  ... 
doi:10.1620/tjem.211.81 pmid:17202775 fatcat:yd4asb5kzzcrxk5ezcgy7dysui
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