Access the full text.
Sign up today, get DeepDyve free for 14 days.
M Gomez, C Bielza, JAF Pozo, S Rios-Insua (2007)
A graphical decision-theoretic model for neonatal jaundiceMed Decis Mak, 27
MW Temple, CU Lehmann, D Fabbri (2016)
Natural language processing for cohort discovery in a discharge prediction model for the neonatal ICUAppl Clin Inform, 7
E Thomas, A Temko, G Lightbody, W Marnane, G Boylan (2010)
Gaussian mixture models for classification of neonatal seizures using EEGPhysiol Meas, 31
P Nevalainen, V Marchi, M Metsaranta, T Lonnqvist, S Toiviainen-Salo, S Vanhatalo, L Lauronen (2017)
Evoked potentials recorded during routine EEG predict outcome after perinatal asphyxiaClin Neurophysiol, 128
R Safdari, M Kadivar, M Langarizadeh, AF Nejad, F Kermani (2016)
Developing a fuzzy expert system to predict the risk of neonatal deathActa Inform Med, 24
A Temko, W Marnane, G Boylan, G Lightbody (2015)
Clinical implementation of a neonatal seizure detection algorithmDecis Support Syst, 70
A Compte, JG Chase, G Russell, A Lynn, C Hann, G Shaw, J Lin (2011)
Modeling the glucose regulatory system in extreme preterm infantsComput Methods Programs Biomed, 102
JP Campbell, E Ataer-Cansizoglu, V Bolon-Canedo, A Bozkurt, D Erdogmus, J Kalpathy-Cramer, MF Chiang (2016)
Expert diagnosis of plus disease in retinopathy of prematurity from computer-based image analysisJAMA Ophthalmol, 134
LFC Nascimento, PMSR Rizol, LB Abiuzi (2009)
Establishing the risk of neonatal mortality using a fuzzy predictive modelCadernos de saude publica, 25
R Ahmed, A Temko, W Marnane, G Lightbody, G Boylan (2016)
Grading hypoxic–ischemic encephalopathy severity in neonatal EEG using GMM supervectors and the support vector machineClin Neurophysiol, 127
RR Hoffman, B Crandall, N Shadbolt (1998)
Use of the critical decision method to elicit expert knowledge: a case study in the methodology of cognitive task analysisHum Factors J Hum Factors Ergon Soc, 40
LM Saadah, FD Chedid, MR Sohail, YM Nazzal, MR Al Kaabi, AY Rahmani (2014)
Palivizumab prophylaxis during nosocomial outbreaks of respiratory syncytial virus in a neonatal intensive care unit: predicting effectiveness with an artificial neural network modelPharmacother J Hum Pharmacol Drug Ther, 34
N Koolen, L Oberdorfer, Z Rona, V Giordano, T Werther, K Klebermass-Schrehof, S Vanhatalo (2017)
Automated classification of neonatal sleep states using EEGClin Neurophysiol, 128
W Deburchgraeve, P Cherian, M Vos, R Swarte, J Blok, GH Visser, S Huffel (2008)
Automated neonatal seizure detection mimicking a human observer reading EEGClin Neurophysiol, 119
A Temko, G Boylan, W Marnane, G Lightbody (2013)
Robust neonatal EEG seizure detection through adaptive background modelingInt J Neural Syst, 23
A Temko, O Doyle, D Murray, G Lightbody, G Boylan, W Marnane (2015)
Multimodal predictor of neurodevelopmental outcome in newborns with hypoxic-ischaemic encephalopathyComput Biol Med, 63
FT Tehrani, S Abbasi (2009)
Evaluation of a computerized system for mechanical ventilation of infantsJ Clin Monit Comput, 23
H Khazaei, C McGregor, JM Eklund, K El-Khatib (2015)
Real-time and retrospective health-analytics-as-a-service: a novel frameworkJMIR Med Inform, 3
GD Baxter, AF Monk, K Tan, PR Dear, SJ Newell (2005)
Using cognitive task analysis to facilitate the integration of decision support systems into the neonatal intensive care unitArtif Intell Med, 35
B Sheehan, D Kaufman, S Bakken, L Currie (2012)
Cognitive analysis of decision support for antibiotic ordering in a neonatal intensive care unitAppl Clin Inform, 3
A Temko, E Thomas, W Marnane, G Lightbody, G Boylan (2011)
EEG-based neonatal seizure detection with support vector machinesClin Neurophysiol, 122
LE Chaves, LF Nascimento (2014)
Estimating outcomes in newborn infants using fuzzy logicRev Paul Pediatr, 32
VL Patel, JF Arocha, M Diermeier, RA Greenes, EH Shortliffe (2001)
Methods of cognitive analysis to support the design and evaluation of biomedical systems: the case of clinical practice guidelinesJ Biomed Inform, 34
W Chen, Y Wang, G Cao, G Chen, Q Gu (2014)
A random forest model based classification scheme for neonatal amplitude-integrated EEGBiomed Eng Online, 13
JM Araújo, JMP Menezes, AA Moura de Albuquerque, O Mota Almeida, FM Ugulino de Araújo (2013)
Assessment and certification of neonatal incubator sensors through an inferential neural networkSensors, 13
R Ahmed, A Temko, WP Marnane, G Boylan, G Lightbody (2017)
Exploring temporal information in neonatal seizures using a dynamic time warping based SVM kernelComput Biol Med, 82
E Grönvall, L Piccini, A Pollini, A Rullo, G Andreoni (2007)
Assemblies of heterogeneous technologies at the neonatal intensive care unit Ambient intelligence
AH Ansari, PJ Cherian, A Dereymaeker, V Matic, K Jansen, L Wispelaere, S Huffel (2016)
Improved multi-stage neonatal seizure detection using a heuristic classifier and a data-driven post-processorClin Neurophysiol, 127
V Monasterio, F Burgess, GD Clifford (2012)
Robust classification of neonatal apnoea-related desaturationsPhysiol Meas, 33
M Frize, CM Ennett, M Stevenson, HC Trigg (2001)
Clinical decision support systems for intensive care units: using artificial neural networksMed Eng Phys, 23
Y Liu, M Traskin, SA Lorch, EI George, D Small (2015)
Ensemble of trees approaches to risk adjustment for evaluating a hospital’s performanceHealth Care Manag Sci, 18
S Bhattacharyya, A Biswas, J Mukherjee, AK Majumdar, B Majumdar, S Mukherjee, AK Singh (2013)
Detection of artifacts from high energy bursts in neonatal EEGComput Biol Med, 43
FT Tehrani, S Abbasi (2012)
A model-based decision support system for critiquing mechanical ventilation treatmentsJ Clin Monit Comput, 26
R Ahmed, A Temko, W Marnane, G Boylan, G Lighbody (2012)
Dynamic time warping based neonatal seizure detection systemConf Proc IEEE Eng Med Biol Soc, 2012
LFC Nascimento, NRS Ortega (2002)
Fuzzy linguistic model for evaluating the risk of neonatal deathRevista de Saúde Pública, 36
M Frize, L Yang, RC Walker, AM Connor (2005)
Conceptual framework of knowledge management for ethical decision-making support in neonatal intensive careIEEE Trans Inf Technol Biomed, 9
CR Walker, M Frize (2004)
Are artificial neural networks “ready to use” for decision making in the neonatal intensive care unit?: commentary on the article by Mueller et al. and page 11Pediatr Res, 56
S Mani, A Ozdas, C Aliferis, HA Varol, Q Chen, R Carnevale, J-H Weitkamp (2014)
Medical decision support using machine learning for early detection of late-onset neonatal sepsisJ Am Med Inform Assoc, 21
M Frize, R Walker (2000)
Clinical decision-support systems for intensive care units using case-based reasoningMed Eng Phys, 22
H Khazaei, N Mench-Bressan, C McGregor, JE Pugh (2015)
Health informatics for neonatal intensive care units: an analytical modeling perspectiveIEEE J Transl Eng Health Med, 3
S Brahnam, C-F Chuang, RS Sexton, FY Shih (2007)
Machine assessment of neonatal facial expressions of acute painDecis Support Syst, 43
M Mueller, CL Wagner, DJ Annibale, TC Hulsey, RG Knapp, JS Almeida (2004)
Predicting extubation outcome in preterm newborns: a comparison of neural networks with clinical expertise and statistical modelingPediatr Res, 56
FR Cerqueira, TG Ferreira, A Paiva Oliveira, DA Augusto, E Krempser, HJ Corrêa Barbosa, R Siqueira-Batista (2014)
NICeSim: an open-source simulator based on machine learning techniques to support medical research on prenatal and perinatal care decision makingArtif Intell Med, 62
L Butruille, R Marcilly, C Boog, SB Costa, T Rakza, L Storme, R Logier (2015)
Development of a pain monitoring device focused on newborn infant applications: the NeoDoloris projectIRBM, 36
A Temko, AK Sarkar, GB Boylan, S Mathieson, WP Marnane, G Lightbody (2017)
Toward a Personalized real-time diagnosis in neonatal seizure detectionIEEE J Transl Eng Health Med, 5
K Shimomura, H Shono, M Kohara, A Uchiyama, Y Ito, H Sugimori (1994)
Neonatal assessment using the Apgar fuzzy expert systemComput Biol Med, 24
UR Kotagal, PH Perlstein, HD Atherton, EF Donovan (1995)
Acuity scores as predictors of cost-related outcomes of neonatal intensive careJ Pediatr, 126
A Temko, E Thomas, W Marnane, G Lightbody, G Boylan (2011)
Performance assessment for EEG-based neonatal seizure detectorsClin Neurophysiol, 122
A Temko, G Lightbody (2016)
Detecting neonatal seizures with computer algorithmsJ Clin Neurophysiol, 33
JM O’Toole, GB Boylan, S Vanhatalo, NJ Stevenson (2016)
Estimating functional brain maturity in very and extremely preterm neonates using automated analysis of the electroencephalogramClin Neurophysiol, 127
A neonatal intensive care unit (NICU) provides critical services to preterm and high-risk infants. Over the years, many tools and techniques have been introduced to support the clinical decisions made by specialists in the NICU. This study systematically reviewed the different technologies used in neonatal decision support systems (DSS), including cognitive analysis, artificial neural networks, data mining techniques, multi-agent systems, and highlighted their role in patient diagnosis, prognosis, monitoring, and healthcare management. Articles on NICU DSS were surveyed, Searches were based on the PubMed, Science Direct, and IEEE databases and only English articles published after 1990 were included. The overall search strategy was to retrieve articles that included terms that were related to “NICU Decision Support Systems” or “Artificial Intelligence” and “Neonatal”. Different methods and artificial intelligence techniques used in NICU decision support systems were assessed and related outcomes, variables, methods and performance measures was reported and discussed. Because of the dynamic, heterogeneous, and real-time environment of the NICU, the processes and medical rules that are followed within a NICU are complicated, and the data records that are produced are complex and frequent. Therefore, a single tool or technology could not cover all the needs of a NICU. However, it is important to examine and deploy new temporal data mining approaches and system architectures, such as multi-agent systems, services, and sensors, to provide integrated real-time solutions for NICU.
Artificial Intelligence Review – Springer Journals
Published: May 22, 2018
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.