Prediction of Emergency Department Visits for Respiratory Symptoms Using an Artificial Neural Network

Bibi, Haim; Nutman, Amir; Shoseyov, David; Mendel, Shalom; Peled, Ronit; Kivity, Shmuel; Nutman, J

doi:10.1378/chest.122.5.1627

Cited by 40 publications

(26 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, artificial neural network analysis has several outstanding issues which need to be addressed. First, it provides little insight into the relative importance of the various input variables [31]. Second, it is impossible to ascertain whether predictors have positive or negative effects on output [31].…”

Section: Discussionmentioning

confidence: 99%

“…First, it provides little insight into the relative importance of the various input variables [31]. Second, it is impossible to ascertain whether predictors have positive or negative effects on output [31]. Third, the desirable value of most of the network design parameters can differ for each application and cannot be theoretically defined [18].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Temporal linear mode complexity as a surrogate measure of the effect of remifentanil on the central nervous system in healthy volunteers

Choi¹,

Shin

Noh

et al. 2011

Brit J Clinical Pharma

View full text Add to dashboard Cite

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• Remifentanil, an intravenous ultra short-acting opioid, depresses central nervous system activity with an increase in the delta band power, and causes beta activation after discontinuation, resulting in a rebound of the processed electroencephalographic parameters, including 95% spectral edge frequency, the canonical univariate parameter and electroencephalographic approximate entropy.• A sigmoid Emax model, in which the highest predicted values of processed electroencephalographic parameters are restricted to the baseline value, cannot describe a rebound of these parameters.• Electroencephalographic approximate entropy correlated well with the remifentanil blood concentration and demonstrated high baseline stability. WHAT THIS STUDY ADDS• A combined effect and tolerance model effectively characterized the time course of the remifentanil effect on the central nervous system, including the rebound which occurred during recovery from the remifentanil effect.• Temporal linear mode complexity was comparable with approximate entropy as a univariate electroencephalographic descriptor of the effect of remifentanil on the central nervous system. AIMSPreviously, electroencephalographic approximate entropy (ApEn) effectively described both depression of central nervous system (CNS) activity and rebound during and after remifentanil infusion. ApEn is heavily dependent on the record length. Linear mode complexity, which is algorithmatically independent of the record length, was investigated to characterize the effect of remifentanil on the CNS using the combined effect and tolerance, feedback and sigmoid Emax models. METHODSThe remifentanil blood concentrations and electroencephalographic data obtained in our previous study were used. With the recording of the electroencephalogram, remifentanil was infused at a rate of 1, 2, 3, 4, 5, 6, 7 or 8 mg kg -1 min -1 for 15-20 min. The areas below (AUCeffect) or above (AACrebound) the effect vs. time curve of temporal linear mode complexity (TLMC) and ApEn were calculated to quantitate the decrease of the CNS activity and rebound. The coefficients of variation (CV) of median baseline (E0), maximal (Emax), and individual median E0 minus Emax values of TLMC were compared with those of ApEn. The concentration-TLMC relationship was characterized by population analysis using non-linear mixed effects modelling. RESULTSMedian AUCeffect and AACrebound were 1016 and 5.3 (TLMC), 787 and 4.5 (ApEn). The CVs of individual median E0 minus Emax were 35.6, 32.5% (TLMC, ApEn). The combined effect and tolerance model demonstrated the lowest Akaike information criteria value and the highest positive predictive value of rebound in tolerance. CONCLUSIONSThe combined effect and tolerance model effectively characterized the time course of TLMC as a surrogate measure of the effect of remifentanil on the CNS.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Temporal linear mode complexity as a surrogate measure of the effect of remifentanil on the central nervous system in healthy volunteers

Choi¹,

Shin

Noh

et al. 2011

Brit J Clinical Pharma

View full text Add to dashboard Cite

show abstract

“…Where there is a substantial literature looking at causal effects, there is surprisingly little literature that looks at forecasting the demand for health services based on environmental predictors (exceptions include studies by Bibi et al, 21 Moustris et al, 22 and Novikov et al 23 ). Using weather, air quality and hospital asthma admissions data from 2005 to 2006 in the London area (the region bounded by the M25 motorway), two related negative binomial models were developed and compared with a naive seasonal model.…”

mentioning

confidence: 99%

Forecasting asthma-related hospital admissions in London using negative binomial models

2013

View full text Add to dashboard Cite

Health forecasting can improve health service provision and individual patient outcomes. Environmental factors are known to impact chronic respiratory conditions such as asthma, but little is known about the extent to which these factors can be used for forecasting. Using weather, air quality and hospital asthma admissions, in London (2005London ( -2006, two related negative binomial models were developed and compared with a naive seasonal model. In the first approach, predictive forecasting models were fitted with 7-day averages of each potential predictor, and then a subsequent multivariable model is constructed. In the second strategy, an exhaustive search of the best fitting models between possible combinations of lags (0-14 days) of all the environmental effects on asthma admission was conducted. Three models were considered: a base model (seasonal effects), contrasted with a 7-day average model and a selected lags model (weather and air quality effects). Season is the best predictor of asthma admissions. The 7-day average and seasonal models were trivial to implement. The selected lags model was computationally intensive, but of no real value over much more easily implemented models. Seasonal factors can predict daily hospital asthma admissions in London, and there is a little evidence that additional weather and air quality information would add to forecast accuracy.

show abstract

“…Fuzzy expert system is inferior to rules of linguistic variables or fuzzy numbers which are uncertain in nature [27].…”

Section: Fuzzy Expert Systemmentioning

confidence: 99%

A fuzzy expert system for diagnosis and treatment of musculoskeletal disorders in wrist

Amiri

Khadivar

2017

Teh. vjesn.

View full text Add to dashboard Cite

A FUZZY EXPERT SYSTEM FOR DIAGNOSIS AND TREATMENT OF MUSCULOSKELETAL DISORDERS IN WRIST Fatemeh Mohammadi Amiri, Ameneh KhadivarOriginal scientific paper Medical expert system is an application which can effectively contribute to decisions on diagnosis and treatment of diseases. A major part of the knowledge related to diagnosis of symptoms and treatment of illnesses is experimental. The problem is that how this knowledge can be extracted and made available to others.The purpose of this study is to design an expert system for diagnosis and treatment of musculoskeletal disorders in wrist in fuzzy environment.The knowledge is achieved from 15 elites in the field of musculoskeletal disorders. For data gathering related to symptoms of the disorders, a fuzzy Delphi method is used. A Delphi method is also used for gathering data related to treatments. Finally, a fuzzy expert system is developed for diagnosis and treatment of musculoskeletal disorders in wrist using MATLAB software. The designed system is able to diagnose seven disorders of the wrist. The input to the system is the magnitude of symptoms and the output is the scores given by the system to disorders; finally, the disorder with the highest score is displayed as the systemic diagnosis for which treatments are suggested.The systemic diagnosis is compared to elite diagnosis using statistical analysis conducted by SPSS software. Results show a significant correlation between two variables. By comparing the variables, it is found that 86,7 % of the systemic diagnoses were similar to elite diagnoses. In the absence of elites, diagnosis and treatment can be performed to a relatively reliable level. As results of current research, medical expert systems can be used as a scientific source by medical students. It can also help users as an auxiliary diagnostic system to diagnose and treat diseases. Keywords: fuzzy approach; knowledge acquisition; medical expert system; musculoskeletal disorders; treatment Fuzzy ekspertni sustav za dijagnozu i liječenje mišićnoskeletnih poremećaja u ručnom zglobuIzvorni znanstveni članak Medicinski ekspertni sustav je aplikacija koja može značajno pomoći pri odlučivanju o dijagnozi i liječenju bolesti. Veći dio znanja koje se odnosi na dijagnozu simptoma i liječenje bolesti je eksperimentalan. Problem je kako to znanje ekstrahirati i učiniti dostupnim drugima. Svrha ovoga rada je napraviti ekspertni sustav za dijagnozu i liječenje mišićno skeletnih poremećaja u ručnom zglobu u fuzzy okruženju. Iskustvo se steklo od 15 elitnih stručnjaka iz područja mišićnoskeletnih poremećaja. Za prikupljanje podataka o simptomima poremećaja primijenjena je fuzzy Delphi metoda. Ta se metoda primijenila i za dobivanje podataka o liječenju. Konačno, razvijen je fuzzy ekspertni sustav za dijagnosticiranje i liječenje mišićno skeletnih poremećaja u ručnom zglobu primjenom softvera MATLAB. Moguće je dijagnosticirati sedam poremećaja zgloba primjenom toga sustava. Ulaz u sustav je magnituda simptoma, a izlaz su rezultati koje je sustav pronašao za te porem...

show abstract

Prediction of Emergency Department Visits for Respiratory Symptoms Using an Artificial Neural Network

Cited by 40 publications

References 25 publications

Temporal linear mode complexity as a surrogate measure of the effect of remifentanil on the central nervous system in healthy volunteers

Temporal linear mode complexity as a surrogate measure of the effect of remifentanil on the central nervous system in healthy volunteers

Forecasting asthma-related hospital admissions in London using negative binomial models

A fuzzy expert system for diagnosis and treatment of musculoskeletal disorders in wrist

Contact Info

Product

Resources

About