10 results on '"Kentala E"'
Search Results
2. Experiences of otoneurological expert system for vertigo.
- Author
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Kentala EL, Laurikkala JP, Viikki K, Auramo Y, Juhola M, and Pyykkö IV
- Subjects
- Decision Making, Discriminant Analysis, Humans, Vertigo etiology, Expert Systems, Vertigo diagnosis
- Abstract
We have developed an OtoNeurological Expert system (ONE) to aid the diagnostics of vertigo, to assist teaching and to implement the database for research. The database contains detailed information on the patient history, signs and test results necessary for the diagnostic work with vertiginous patients. The pattern recognition method was used in the reasoning process. Questions regarding symptoms, signs and test results are weighted and scored for each disease, and the most likely disease is recognized from the defined disease profiles. Uncertainties in reasoning, caused by missing information, were solved with a method resembling fuzzy logic. We have also applied adaptive computer applications, such as genetic algorithms and decision trees, in the reasoning process. In the validation the expert system ONE proved to be a sound decision maker, by solving 65% of the cases correctly, while the physicians' mean was 69%. To improve the expert system ONE further, a follow-up should be implemented for the patients, to ease the diagnostic work of some difficult diseases. The six diseases were detected with high accuracy also with adaptive learning methods and discriminant analysis. An expert system is a practical tool in otoneurology. We aim to construct a hybrid program for the reasoning, where the best reasoning method for each disease is used.
- Published
- 2001
- Full Text
- View/download PDF
3. A novel machine learning program applied to discover otological diagnoses.
- Author
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Laurikkala JP, Kentala EL, Juhola M, and Pyvkkö IV
- Subjects
- Humans, Severity of Illness Index, Ear Diseases diagnosis, Expert Systems, Learning
- Abstract
A novel machine learning system, Galactica, has been developed for knowledge discovery from databases. This system was applied to discover diagnostic rules from a patient database containing 564 cases with vestibular schwannoma, bening paroxysmal positional vertigo, Ménière's disease, sudden deafness, traumatic vertigo and vestibular neuritis diagnoses. The rules were evaluated using an independent testing set. The accuracy of rules for these diagnoses were 91%, 96%, 81%, 95%, 92% and 98%, respectively. Besides being accurate, the rules contained the five most important diagnostic questions identified in the earlier research. The knowledge presented with rules can be easily comprehended and verified.
- Published
- 2001
- Full Text
- View/download PDF
4. Decision tree induction in the diagnosis of otoneurological diseases.
- Author
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Viikki K, Kentala E, Juhola M, and Pyykkö I
- Subjects
- Diagnosis, Differential, Hearing Loss, Sudden complications, Humans, Meniere Disease complications, Neurilemmoma complications, Neurilemmoma diagnosis, Neuroma, Acoustic complications, Neuroma, Acoustic diagnosis, Predictive Value of Tests, Vertigo etiology, Vestibular Neuronitis complications, Vestibular Neuronitis diagnosis, Vestibule, Labyrinth injuries, Vestibulocochlear Nerve Diseases complications, Algorithms, Decision Trees, Diagnosis, Computer-Assisted methods, Expert Systems, Hearing Loss, Sudden diagnosis, Meniere Disease diagnosis, Vestibulocochlear Nerve Diseases diagnosis
- Abstract
Expert systems have been applied in medicine as diagnostic aids and education tools. The construction of a knowledge base for an expert system may be a difficult task; to automate this task several machine learning methods have been developed. These methods can be also used in the refinement of knowledge bases for removing inconsistencies and redundancies, and for simplifying decision rules. In this study, decision tree induction was employed to acquire diagnostic knowledge for otoneurological diseases and to extract relevant parameters from the database of an otoneurological expert system ONE. The records of patients with benign positional vertigo, Meniere's disease, sudden deafness, traumatic vertigo, vestibular neuritis and vestibular schwannoma were retrieved from the database of ONE, and for each disease, decision trees were constructed. The study shows that decision tree induction is a useful technique for acquiring diagnostic knowledge for otoneurological diseases and for extracting relevant parameters from a large set of parameters.
- Published
- 1999
- Full Text
- View/download PDF
5. Otoneurological expert system for vertigo.
- Author
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Kentala E, Pyykkö I, Auramo Y, Laurikkala J, and Juhola M
- Subjects
- Algorithms, Artificial Intelligence, Cranial Nerve Neoplasms diagnosis, Databases as Topic, Decision Making, Follow-Up Studies, Fuzzy Logic, Hearing Loss, Sudden diagnosis, Humans, Meniere Disease diagnosis, Neurilemmoma diagnosis, Neuritis diagnosis, Pattern Recognition, Automated, Physicians, Problem Solving, Reproducibility of Results, Teaching methods, Vertigo physiopathology, Vestibular Nerve, Expert Systems, Vertigo diagnosis
- Abstract
We have developed an otoneurological expert system (ONE) to aid the diagnostics of vertigo, to assist teaching and to implement a database for research. The ONE database is set to harvest data on patient history, signs and test results necessary for diagnostic work with vertiginous patients. A method based on pattern recognition was used in the reasoning process. Questions about symptoms, signs and test results are weighted and scored for each disease and the most likely disease is recognized from defined disease profiles. Missing information and uncertainties are solved with a method resembling fuzzy logic. ONE was validated by comparing diagnoses assessed by physicians with those provided by the system. It proved to be a valid decision-maker by solving 65% of the cases correctly, while the physicians' mean was 69%. To improve ONE further, a follow-up should be implemented for the patients, since diagnosing sudden deafness and Meniere's disease during the first visit is often impossible. We aim to obtain new information on diseases involving vertigo by applying adaptive computer applications, such as genetic algorithms, to the reasoning process.
- Published
- 1999
- Full Text
- View/download PDF
6. Comparison between diagnoses of human experts and a neurotologic expert system.
- Author
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Kentala E, Auramo Y, Juhola M, and Pyykkö I
- Subjects
- Adult, Aged, Diagnostic Errors, Female, Humans, Internship and Residency, Male, Middle Aged, Central Nervous System Diseases diagnosis, Expert Systems, Otolaryngology education, Otorhinolaryngologic Diseases diagnosis
- Abstract
The decision-making ability of a recently developed neurotologic expert system was compared with the diagnoses of six physicians. Five of the physicians were residents and one was a specialist in the field of otolaryngology. The test patients were randomly selected from vertiginous patients referred to an otolaryngology clinic. The expert system and the physicians first had identical information on patient history, symptoms, and tests. During the second phase of the study the physicians were allowed to use the full medical records. The correct diagnoses were certified by an experienced specialist in neurotology. The expert system did better in decision-making when both the expert system and the physicians had identical information on patients. However, when the physicians were allowed to use patient's complete medical records, they surpassed the expert system. The expert system diagnosed 65% of the cases, while the physicians first diagnosed 54% of the cases, and then with complete information, 69% of the cases. From the patients' medical records, the physicians obtained information on the time perspective of the symptoms and the progression of the disease. These aspects will be used to further improve the expert system.
- Published
- 1998
- Full Text
- View/download PDF
7. Neural networks in neurotologic expert systems.
- Author
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Kentala E, Pyykkö I, Auramo Y, and Juhola M
- Subjects
- Diagnosis, Computer-Assisted, Humans, Expert Systems, Neural Networks, Computer, Vertigo diagnosis
- Abstract
Artificial intelligence donates new possibilities to neurotologic research. Neural networks are a computer-based reasoning method which can be applied in expert systems created for clinical decision support. Neural networks have been used in medical imaging, in medical signal processing and to analyze both clinical and laboratory data. Principally, neural networks simulate the function of the brain. They have to be taught to make correct decisions from the input data. This learning process can be either supervised or unsupervised. The decision making is based on mathematical transformations and it occurs on a hidden level. Calculations are made on parallel manner and the decision making simulates pattern recognition method. Neural networks suit well in medical problems which cannot be defined in simple rules. A drawback of neural networks is that the decisions are irrational and cannot be motivated to the user. Another problem is neural networks' difficulty to handle incomplete input data, i.e., how to define some default or expected values for unknown input parameters. In a complex medical area, which would require multilayered neural networks, the neural networks require a large amount of solved cases for the learning process. In our experience neural networks seem not suitable for diagnosing vertigo and a better choice would be either case-based reasoning or possibly genetic algorithms or a combination of these.
- Published
- 1997
- Full Text
- View/download PDF
8. Otoneurological expert system.
- Author
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Kentala E, Pyykkö I, Auramo Y, and Juhola M
- Subjects
- Diagnosis, Computer-Assisted, Humans, Meniere Disease diagnosis, Meniere Disease physiopathology, Models, Theoretical, Vertigo diagnosis, Vertigo physiopathology, Vestibule, Labyrinth physiopathology, Expert Systems, Neurology, Otolaryngology
- Abstract
An otoneurological expert system was developed to help collect data and diagnose both central and peripheral diseases causing vertigo. Patient history and otoneurological and other examination results are used in the reasoning process. The case history data can be either mandatory or supportive. Mandatory questions are used to confirm a diagnosis, and conflicting answers are used to reject an unlikely disease. Supportive questions support or suppress a diagnosis, but their presence is not obligatory. The reasoning procedure of the otoneurological expert system scores every question independently for different diagnoses, depending on how well they agree with the symptom entity of a disease. Diagnostic criteria are set for each disease. Graphic displays illustrate the linear and nonlinear correlation between the symptoms and diseases. Emphasis is placed on diminishing the possibility of a wrong decision rather than maximizing the likelihood of reaching only one right decision, so that even rare diseases can be taken into consideration.
- Published
- 1996
- Full Text
- View/download PDF
9. An essay on power of expert systems versus human expertise.
- Author
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Juhola M, Auramo Y, Kentala E, and Pyykkö I
- Subjects
- Brain Diseases diagnosis, Ear Diseases diagnosis, Humans, Neurology, Reproducibility of Results, Clinical Competence, Decision Making, Computer-Assisted, Expert Systems
- Abstract
In connection with several recent studies of medical informatics, the usefulness and use of expert systems have been both criticized and defended. We have examined the issue of the inference power of expert systems compared to that of human experts. At an abstract level we have shown that there is no doubt that expert systems could successfully complement human experts within strictly limited and well-defined specialties, and actually be of reasonable aid in diagnosis, provided that the expert systems have been correctly and effectively elaborated. Also practical experiments were conducted with our recently implemented expert system.
- Published
- 1995
- Full Text
- View/download PDF
10. Reasoning in expert system ONE for vertigo work-up.
- Author
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Kentala E, Pyykkö I, Auramo Y, and Juhola M
- Subjects
- Diagnosis, Differential, Humans, Medical Records Systems, Computerized, Software, Artificial Intelligence, Diagnosis, Computer-Assisted, Dizziness etiology, Expert Systems, Meniere Disease etiology, Vertigo etiology, Vestibular Diseases diagnosis
- Abstract
An otoneurological expert system (ONE) was developed to help collect data and diagnose the work-up of vertigo of both central and peripheral diseases causing vertigo. Patient history and otoneurological and other examination results are used in the reasoning process. The history is interactively collected and is complemented with clinical examination results. The case history data can be either mandatory or supportive. Mandatory questions are used to confirm a diagnosis, and conflicting answers are used to reject an unlikely disease. Supportive questions support or suppress a diagnosis, but their presence is not obligatory. The reasoning procedure of ONE scores every question independently for different diagnoses, depending on how well they agree with the symptom entity of a disease. Diagnostic criteria are set for each disease, in Meniere's disease, for example, the full triad is required. Graphic displays illustrate the linear and nonlinear correlation between the symptoms and diseases. For instance, both second-long Tumarkin-type attacks and attacks lasting hours give a high score while intermediately long attacks score much lower in Meniere's disease. To be able to take even rare diseases into consideration we try to diminish the possibility of a wrong decision rather than maximize the likelihood of reaching only one right decision.
- Published
- 1995
- Full Text
- View/download PDF
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