Automated myocardial infarction diagnosis from ECG

In the present dissertation, an automated neural network-based ECG diagnosing system was designed to detect the presence of myocardial infarction based on the hypothesis that an artificial neural network-based ECG interpretation system may improve the clinical myocardial infarction. 137 patients were included. Among them 122 had myocardial infarction, but the remaining 15 were normal. The sensitivity and the specificity of present system were 92.2% and 50.7% respectively. The sensitivity was consistent with relevant research. The relatively low specificity results from the rippling of the low pass filtering. We can conclude that neural network-based system is a promising aid for the myocardial infarction diagnosis

Utilization of an artificial neural network in the prediction of heart disease

This paper intents to assess the application of artificial neural network in predicting the presence of heart disease, mainly the angina in patients.The prediction and detection of angina are significant in determining the most appropriate form of treatment for these patients.The development of the application involves three main phases.The first phase is the development of Heart Disease Management Information System (HDMIS) for data collection and patient management.Then followed by the second phase, which is the development of Neural Network Simulator (NNS) using back propagation neural network for training and testing.The final phase is the development of Prediction System (PS) for prediction on new patient’s data.The best network model produced prediction accuracy of 88.89 percent.Apart from proving the ability of neural network technology in medical diagnosis, this study also shown how the neural network could be incorporated into the hospital information system as a prediction tool.As the pilot project, the application developed could be used as the starting point in building a medical decision support system, particularly in diagnosing the heart disease

Neural Network on Mortality Prediction for the Patient Admitted with ADHF (Acute Decompensated Heart Failure)

Patient admitted with acute decompensated heart failure (ADHF) facing with high risk of mortality where 30 day mortality rates are reaching 10%. Identifying patient with high and low risk of mortality could improve clinical outcomes and hospital resources allocation. This paper proposed the use of artificial neural network to predict mortality for the patient admitted with ADHF. Results show that artificial neural network can predict mortality for ADHF patient with good prediction accuracy of 94.73% accuracy for training dataset and 91.65% for test dataset

Medical Expert Systems Survey

There is an increase interest in the area of Artificial Intelligence in general and expert systems in particular. Expert systems are rapidly growing technology. Expert system is a branch of Artificial Intelligence which is having a great impact on many fields of human life. Expert systems use human expert knowledge to solve complex problems in many fields such as Health, science, engineering, business, and weather forecasting. Organizations employing the technology of expert system have seen an increase in the efficiency and the quality. An expert system is computer program that emulates the behavior of a human expert. The expert system represents knowledge solicited from human expert as data or production rules within a computer program. These rules and data can be used to solve complex problems. In this paper, we give an overview of this technology and will discuss a survey on many papers done in health using expert system

The use of knowledge discovery databases in the identification of patients with colorectal cancer

Colorectal cancer is one of the most common forms of malignancy with 35,000 new patients diagnosed annually within the UK. Survival figures show that outcomes are less favourable within the UK when compared with the USA and Europe with 1 in 4 patients having incurable disease at presentation as of data from 2000.Epidemiologists have demonstrated that the incidence of colorectal cancer is highest on the industrialised western world with numerous contributory factors. These range from a genetic component to concurrent medical conditions and personal lifestyle. In addition, data also demonstrates that environmental changes play a significant role with immigrants rapidly reaching the incidence rates of the host country.Detection of colorectal cancer remains an important and evolving aspect of healthcare with the aim of improving outcomes by earlier diagnosis. This process was initially revolutionised within the UK in 2002 with the ACPGBI 2 week wait guidelines to facilitate referrals form primary care and has subsequently seen other schemes such as bowel cancer screening introduced to augment earlier detection rates. Whereas the national screening programme is dependent on FOBT the standard referral practice is dependent upon a number of trigger symptoms that qualify for an urgent referral to a specialist for further investigations. This process only identifies 25-30% of those with colorectal cancer and remains a labour intensive process with only 10% of those seen in the 2 week wait clinics having colorectal cancer.This thesis hypothesises whether using a patient symptom questionnaire in conjunction with knowledge discovery techniques such as data mining and artificial neural networks could identify patients at risk of colorectal cancer and therefore warrant urgent further assessment. Artificial neural networks and data mining methods are used widely in industry to detect consumer patterns by an inbuilt ability to learn from previous examples within a dataset and model often complex, non-linear patterns. Within medicine these methods have been utilised in a host of diagnostic techniques from myocardial infarcts to its use in the Papnet cervical smear programme for cervical cancer detection.A linkert based questionnaire of those attending the 2 week wait fast track colorectal clinic was used to produce a ‘symptoms’ database. This was then correlated with individual patient diagnoses upon completion of their clinical assessment. A total of 777 patients were included in the study and their diagnosis categorised into a dichotomous variable to create a selection of datasets for analysis. These data sets were then taken by the author and used to create a total of four primary databases based on all questions, 2 week wait trigger symptoms, Best knowledge questions and symptoms identified in Univariate analysis as significant. Each of these databases were entered into an artificial neural network programme, altering the number of hidden units and layers to obtain a selection of outcome models that could be further tested based on a selection of set dichotomous outcomes. Outcome models were compared for sensitivity, specificity and risk. Further experiments were carried out with data mining techniques and the WEKA package to identify the most accurate model. Both would then be compared with the accuracy of a colorectal specialist and GP.Analysis of the data identified that 24% of those referred on the 2 week wait referral pathway failed to meet referral criteria as set out by the ACPGBI. The incidence of those with colorectal cancer was 9.5% (74) which is in keeping with other studies and the main symptoms were rectal bleeding, change in bowel habit and abdominal pain. The optimal knowledge discovery database model was a back propagation ANN using all variables for outcomes cancer/not cancer with sensitivity of 0.9, specificity of 0.97 and LR 35.8. Artificial neural networks remained the more accurate modelling method for all the dichotomous outcomes.The comparison of GP’s and colorectal specialists at predicting outcome demonstrated that the colorectal specialists were the more accurate predictors of cancer/not cancer with sensitivity 0.27 and specificity 0.97, (95% CI 0.6-0.97, PPV 0.75, NPV 0.83) and LR 10.6. When compared to the KDD models for predicting the same outcome, once again the ANN models were more accurate with the optimal model having sensitivity 0.63, specificity 0.98 (95% CI 0.58-1, PPV 0.71, NPV 0.96) and LR 28.7.The results demonstrate that diagnosis colorectal cancer remains a challenging process, both for clinicians and also for computation models. KDD models have been shown to be consistently more accurate in the prediction of those with colorectal cancer than clinicians alone when used solely in conjunction with a questionnaire. It would be ill conceived to suggest that KDD models could be used as a replacement to clinician- patient interaction but they may aid in the acceleration of some patients for further investigations or ‘straight to test’ if used on those referred as routine patients

Knowledge Based System for Long-term Abdominal Pain (Stomach Pain) Diagnosis and Treatment

Abstract: Background: the abdomen is called (the belly, tummy, stomach, or midriff) establishes the part of the body between the thorax (chest) and pelvis, in humans. The abdomen contains most of the tube like organs of the digestive tract, as well as several solid organs. Hollow abdominal organs comprise the stomach, the small intestine, and the colon with its attached appendix. Organs such as the liver, its attached gallbladder, and the pancreas function in close association with the digestive tract and communicate with it via ducts. Objectives: the main goal of this expert system is to get the appropriate diagnosis of abdomen disease and the correct treatment. Methods: in this paper the design of the proposed expert system which was produced to help internist physicians in diagnosing many of the abdomen diseases such as: hiatal hernia, gastritis, ulcer or heartburn; the proposed expert system presents an overview about abdomen diseases are given, the cause of diseases are outlined and the treatment of disease whenever possible is given out. Clips expert system language was used for designing and implementing the proposed expert system. Results: the proposed abdomen diseases diagnosis expert system was evaluated by medical students and they were satisfied with its performance. Conclusions: the proposed expert system is very useful for internist physician, patients with abdomen problem and newly graduated physician

Use of neural networks in classification of heart diseases

Práce je zaměřená na návrh a využití umělých neuronových sítí jako klasifikátoru srdečních onemocnění z EKG signálu se zaměřením na ischemickou chorobu srdeční. Změny ST-T komplexů jsou významným ukazatelem ischemie v EKG signálu. Různe typy ischemické choroby srdeční se projevují zejména elevací nebo depresí ST segmentů a změnami T vlny v analyzovaném signálu. První část této práce obsahuje teoretický úvod popisující jednotlivé typy ischemické choroby srdeční a na ně vázané změny EKG signálu. Druhá část je věnována popisu předzpracování EKG signálu ke klasifikaci neuronovými sítěmi. Obsahuje filtraci EKG, QRS detekci, detekci ST-T komplexů a popis analýzy hlavních komponent a její využítí k popisu analyzovaného signálu. V poslední části práce je popsán návrh a způsob detekce možných příznaků ischemické choroby srdeční v EKG pomocí dvou typů umělých neuronových sítí: Back-propagation, SOM. Dále jsou zde uvedeny výsledky navržených algoritmů. Přílohy obsahují popis navrženého programu pro klasifikaci srdečních onemocnění, popis jednotlivých jeho funkcí, dále zde najdeme podrobný popis všech použitých neuronových sítí a tabulky obsahující detailní výsledky klasifikace EKG signálu. Samotný program byl vytvořen v programovacím prostředí Matlab R2007b.This thesis discusses the design and the utilization of the artificial neural networks as ECG classifiers and the detectors of heart diseases in ECG signal especially myocardial ischaemia. The changes of ST-T complexes are the important indicator of ischaemia in ECG signal. Different types of ischaemia are expressed particularly by depression or elevation of ST segments and changes of T wave. The first part of this thesis is orientated towards the theoretical knowledges and describes changes in the ECG signal rising close to different types of ischaemia. The second part deals with to the ECG signal pre-processing for the classification by neural network, filtration, QRS detection, ST-T detection, principal component analysis. In the last part there is described design of detector of myocardial ischaemia based on artificial neural networks with utilisation of two types of neural networks back – propagation and self-organizing map and the results of used algorithms. The appendix contains detailed description of each neural networks, description of the programme for classification of ECG signals by ANN and description of functions of programme. The programme was developed in Matlab R2007b.

Utilización de metodologías de Inteligencia Artificial y sus aplicaciones en El Salvador

El presente artículo intenta dar una pequeña perspectiva de cómo el uso de las metodologías basadas en Inteligencia Artificial (IA) podrían contribuir en la solución de problemas reales del país: como la eficiencia y eficacia en consultas médicas del Instituto del Seguro Social Salvadoreño (ISSS), toma de decisiones políticas importantes, resolución de juicios legales, evasión de impuestos, aprobación de créditos, optimización de recursos, etc. El documento describe brevemente diferentes técnicas de Inteligencia Artificial (IA) tales como Sistemas Expertos (SE), Razonamiento Basados en Casos (RBC), Redes Neuronales Artificiales (RNA) y Algoritmos Genéticos (AG) entre otras, y menciona en forma sintetizada algunas áreas críticas en las que podrían aplicarse en el país con éxito. El objetivo principal de este artículo es dar a conocer otras alternativas hasta ahora desconocidas por las instituciones del Estado para la resolución de problemas nacionales importantes

Personalised modelling with spiking neural networks integrating temporal and static information.

This paper proposes a new personalised prognostic/diagnostic system that supports classification, prediction and pattern recognition when both static and dynamic/spatiotemporal features are presented in a dataset. The system is based on a proposed clustering method (named d2WKNN) for optimal selection of neighbouring samples to an individual with respect to the integration of both static (vector-based) and temporal individual data. The most relevant samples to an individual are selected to train a Personalised Spiking Neural Network (PSNN) that learns from sets of streaming data to capture the space and time association patterns. The generated time-dependant patterns resulted in a higher accuracy of classification/prediction (80% to 93%) when compared with global modelling and conventional methods. In addition, the PSNN models can support interpretability by creating personalised profiling of an individual. This contributes to a better understanding of the interactions between features. Therefore, an end-user can comprehend what interactions in the model have led to a certain decision (outcome). The proposed PSNN model is an analytical tool, applicable to several real-life health applications, where different data domains describe a person's health condition. The system was applied to two case studies: (1) classification of spatiotemporal neuroimaging data for the investigation of individual response to treatment and (2) prediction of risk of stroke with respect to temporal environmental data. For both datasets, besides the temporal data, static health data were also available. The hyper-parameters of the proposed system, including the PSNN models and the d2WKNN clustering parameters, are optimised for each individual