Biomedical subjects

Y Auramo

Publications and source records attributed to Y Auramo.

15 recordsLinked to original sources

PubMed2001

Experiences of otoneurological expert system for vertigo.

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.

Decision Making↗

PubMed2001

Application of artificial intelligence in audiology.

In this paper, machine learning methods based on artificial intelligence theory are applied to the computer-aided decision making of some otoneurological diseases, for example Ménière's disease. Three methods explored are decision trees, genetic algorithms and neural networks. By using such a machine learning method, the decision-making program is trained with a representative training set of cases and tested with another set. The machine learning methods are useful also for our otoneurological expert system, One, which is based on a pattern recognition approach. The methods are able to differentiate most of the cases tested between the six diseases included, provided that a sufficiently large training set is available.

Algorithms↗

PubMed2000

Database for a hearing conservation program.

We have developed a database and an analysis program (NoiseScan) for noise-induced hearing loss (NIHL). The exposure data are based on the evaluation of the noise immission level, which includes duration, frequency content, and the use of, and the attenuation performance of, hearing protectors. The input data can handle an unlimited number of exposure periods. If the noise exposure level is not known, the program lists noise levels of comparable work places, and thus provides an estimate of exposure. Confounding medical factors that may contribute to NIHL, such as elevated serum cholesterol level, hypertension, and extensive use of pain killers, are collected. Combined exposure to agents that clearly contribute to NIHL, such as hand-arm vibration, tobacco smoking, use of aminoglycosides and exposure to solvents are also assessed. An unlimited number of audiograms can be stored, and all the data can be completed and edited following collection. The program gives the predicted hearing loss according to the ISO 1999 model based on total exposure. At present, our NoiseScan program (under continuous development in an EU research program) is suitable for the data collection of various risk factors. It can be used to determine whether the hearing loss is occupational in origin and to estimate the efficiency of hearing conservation measures. NoiseScan also predicts the development of hearing loss in individuals in 5-year periods. The goal is to improve and validate the rules by which single and combined risk factors contribute to HIHL, thus leading to more precise prediction of individual hearing loss, and for the evaluation of success of the hearing conservation programs.

Audiometry↗

PubMed1999

Otoneurological expert system for vertigo.

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.

Algorithms↗

PubMed1998

Comparison between diagnoses of human experts and a neurotologic expert system.

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.

Adult↗

PubMed1997

Neural networks in neurotologic expert systems.

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.

Diagnosis, Computer-Assisted↗

PubMed1996

Modifying an expert system construction to pattern recognition solution.

Medical expert systems are a successful field of applied artificial intelligence. We constructed an otoneurological expert system in our previous research, and in this study we consider its reasoning method. The reasoning process can be described as a modified nearest neighbour solution derived from pattern recognition. The expert system was tested and functions reliably.

Ear↗

PubMed1996

Otoneurological expert system.

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.

Diagnosis, Computer-Assisted↗

PubMed1995

Graphical user interface for the analysis of noise induced hearing loss.

We have developed a Windows-based computer program which will help the user to collect data needed for calculation models of noise induced hearing loss (NIHL). The program has a graphical user interface and it includes several methods to calculate NIHL. We have tried to make our system to cover all the factors concerning NIHL and also to take into account other possible reasons of hearing loss. The system is used not only for estimating noise induced hearing loss, but also as a systematic way to collect data for future evolution of new models and for other research purposes. For this sake the program asks some questions that are not currently included to these models, but which have been shown to have some impact on noise induced hearing loss.

Computer Graphics↗

PubMed1995

Comparison of inference results of two otoneurological expert systems.

In this paper, two different otoneurological expert systems, Vertigo and One, the latter developed by us, are considered. The expert systems are evaluated as regards their correctness in reasoning diagnoses. In the light of our data collected from randomly selected test patients, One, being a newer technique, is more effective, since it could infer more cases than vertigo did. All the data was also evaluated and diagnosed by otoneurological specialists, independently of the expert systems, to guarantee objectivity in evaluation of the results of the expert systems.

Diagnosis, Differential↗

PubMed1995

Database for vertigo.

An interactive database has been developed to assist the diagnostic procedure for vertigo and to store the data. The database offers a possibility to split and reunite the collected information when needed. It contains detailed information about a patient's history, symptoms, and findings in otoneurologic, audiologic, and imaging tests. The symptoms are classified into sets of questions on vertigo (including postural instability), hearing loss and tinnitus, and provoking factors. Confounding disorders are screened. The otoneurologic tests involve saccades, smooth pursuit, posturography, and a caloric test. In addition, findings from specific antibody tests, clinical neurotologic tests, magnetic resonance imaging, brain stem audiometry, and electrocochleography are included. The input information can be applied to workups for vertigo in an expert system called ONE. The database assists its user in that the input of information is easy. If not only can be used for diagnostic purposes but is also beneficial for research, and in combination with the expert system, it provides a tutorial guide for medical students.

Anxiety↗

PubMed1995

Computer assisted data collection in vestibular disorders.

We have developed an interactive database for vertigo than can be used to assist in the diagnostic procedure and to store the data in a form of a database. The database offers the possibility to split and reunite the collected information in a desired way. The database contains detailed information about patient history, symptoms and findings in neurotological, audiological and imaging tests. The symptoms are classified into three sets of questions: vertigo (including postural instability), hearing loss and tinnitus, and provoking factors. Confounding disorders are screened. The neurotological tests involve saccades, smooth pursuit, posturography and caloric test. In addition, findings in specified antibody testing, clinical neurotological tests. MRI, brain stem audiometry and electrocochleography are included. The input information can be applied in an expert system ONE for vertigo work-up. The database is user-friendly. Besides diagnostic purposes the database is excellent for research purposes, and combined with the expert system it works as a tutorial guide for medical students.

Artificial Intelligence↗

PubMed1995

Reasoning in expert system ONE for vertigo work-up.

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.

Artificial Intelligence↗

PubMedDate not supplied

An expert system for the computer-aided diagnosis of dizziness and vertigo.

We have developed an expert system to assist in the diagnostic work-up of otoneurological cases. Our otoneurological expert system ONE takes advantage of both patient history and clinical measurement data in order to supply all possible information about the patient's symptoms and other findings. This paper presents ONE after its initial stage of development, which included tests with numerous patients.

Adult↗

PubMedDate not supplied

An essay on power of expert systems versus human expertise.

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.

Brain Diseases↗