Clinical decision support system (DSS) in the diagnosis of malaria: A case comparison of two soft computing methodologies

Uzoka, Faith-Michael E.; Osuji, Joseph; Obot, Okure U.

doi:10.1016/j.eswa.2010.07.068

Cited by 47 publications

(23 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…CDSS, an important category of health information systems, has been defined as an artificial intelligence tool [29] to improve clinical decision-making [28] and make an optimal diagnosis decision [25]. Uzoka, Osuji and Obot [31] point out the application of artificial intelligence or other decision support systems in the diagnostic process is about to improve practitioner performance, reduce costs, and improve patient outcomes.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Building a two-stage integrated model for outpatient appointment service

Chuang

Lin

Chen

et al. 2014

Journal of Industrial and Production Engineering

View full text Add to dashboard Cite

There are many medical departments in Taiwan's hospitals which make the patients not easily selecting the appropriate outpatient appointment. Patients often make their selection based on their own experience. This may lead incorrect appointment, delay of treatment, and waste of time. To help patients in making appropriate choice of outpatient appointment, this study has built a two-stage integrated model for decision support applications. artificial neural networks (ANN), support vector machine (SVM), and classification and regression tree (CART) are used in combination for modeling of the system. The first is to input the symptoms into the classifier and generate the category of outpatient department. The second stage is for continued classification of input symptoms and generates the recommended medical specialty/division for appointment. The results show ANN-SVM model has achieved the highest overall yield of 93.94%. ANN-CART and SVM-CART models achieved 80.75 and 78.57%, respectively. This two-stage model is a cost-effective tool in medical decision support and helps providing efficient medical service.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

“…Quick and accurate diagnosis will cut costs and reduce human suffering [31]. Previous studies have shown that CDSS can improve physicians' performance and accuracy [4,14].…”

Section: Introductionmentioning

confidence: 99%

Building a two-stage integrated model for outpatient appointment service

Chuang

Lin

Chen

et al. 2014

Journal of Industrial and Production Engineering

View full text Add to dashboard Cite

show abstract

“…Promoting disease prevention; e.g., coronary heart disease prevention [12] 3. Regarding diagnosis; e.g., malaria [13] 4. For treatment choices; e.g., adjuvant breast cancer therapies [14] …”

Section: Use Of Decision Aids In Healthcarementioning

confidence: 99%

Measuring healthcare decision aid effectiveness

Collard

Garibaldi

2012

2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

View full text Add to dashboard Cite

Knowledge on the causes, prevention, screening, diagnosis, treatment and progression of disease is increasing exponentially in both volume and complexity. Clinicians and patients need to assimilate this information to make decisions in situations of prognostic uncertainty and healthcare decision aids may help this process by providing all the information (even if conflicting) in one place and/or by aiding values clarification. Like all healthcare interventions, decision aids must be evaluated before introduction into the patient pathway to ensure they provide benefit, avoid harm, and achieve their aims. Published evaluations vary in outcome measures resulting in difficulty in comparisons and limiting progress in this field. This paper reviews the literature and proposes a framework to guide the evaluation of decision aids in healthcare.

show abstract

“…Fuzzification converts the real numbers to fuzzy sets, knowledge base includes laws which express the relationship between input and output fuzzy variables [30], inference mechanism takes input from user, selects the rules from the rule base [25], processes the input based on these rules and provides the output [30], defuzzification converts the fuzzy set to a real number based on the defuzzification process [31]. Another important concept of fuzzy expert system is knowledge acquisition which provides an efficient means of knowledge collection and storage throughout the world.…”

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

Clinical decision support system (DSS) in the diagnosis of malaria: A case comparison of two soft computing methodologies

Cited by 47 publications

References 50 publications

Building a two-stage integrated model for outpatient appointment service

Building a two-stage integrated model for outpatient appointment service

Measuring healthcare decision aid effectiveness

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

Contact Info

Product

Resources

About