Justified granulation aided noninvasive liver fibrosis classification system

According to the World Health Organization 130-150 million (according to WHO) of people globally are chronically infected with hepatitis C virus. The virus is responsible for chronic hepatitis that ultimately may cause liver cirrhosis and death. The disease is progressive, however antiviral treatment may slow down or stop its development. Therefore, it is important to estimate the severity of liver fibrosis for diagnostic, therapeutic and prognostic purposes. Liver biopsy provides a high accuracy diagnosis, however it is painful and invasive procedure. Recently, we witness an outburst of non-invasive tests (biological and physical ones) aiming to define severity of liver fibrosis, but commonly used FibroTest®, according to an independent research, in some cases may have accuracy lower than 50 %. In this paper a data mining and classification technique is proposed to determine the stage of liver fibrosis using easily accessible laboratory data. Methods: Research was carried out on archival records of routine laboratory blood tests (morphology, coagulation, biochemistry, protein electrophoresis) and histopathology records of liver biopsy as a reference value. As a result, the granular model was proposed, that contains a series of intervals representing influence of separate blood attributes on liver fibrosis stage. The model determines final diagnosis for a patient using aggregation method and voting procedure. The proposed solution is robust to missing or corrupted data. Results: The results were obtained on data from 290 patients with hepatitis C virus collected over 6 years. The model has been validated using training and test data. The overall accuracy of the solution is equal to 67.9 %. The intermediate liver fibrosis stages are hard to distinguish, due to effectiveness of biopsy itself. Additionally, the method was verified against dataset obtained from 365 patients with liver disease of various etiologies. The model proved to be robust to new data. What is worth mentioning, the error rate in misclassification of the first stage and the last stage is below 6.5 % for all analyzed datasets. Conclusions: The proposed system supports the physician and defines the stage of liver fibrosis in chronic hepatitis C. The biggest advantage of the solution is a human-centric approach using intervals, which can be verified by a specialist, before giving the final decision. Moreover, it is robust to missing data. The system can be used as a powerful support tool for diagnosis in real treatmen

Towards an event annotated corpus of Polish

Towards an event annotated corpus of PolishThe paper presents a typology of events built on the basis of TimeML specification adapted to Polish language. Some changes were introduced to the definition of the event categories and a motivation for event categorization was formulated. The event annotation task is presented on two levels – ontology level (language independent) and text mentions (language dependant). The various types of event mentions in Polish text are discussed. A procedure for annotation of event mentions in Polish texts is presented and evaluated. In the evaluation a randomly selected set of documents from the Corpus of Wrocław University of Technology (called KPWr) was annotated by two linguists and the annotator agreement was calculated. The evaluation was done in two iterations. After the first evaluation we revised and improved the annotation procedure. The second evaluation showed a significant improvement of the agreement between annotators. The current work was focused on annotation and categorisation of event mentions in text. The future work will be focused on description of event with a set of attributes, arguments and relations

Temporal Expressions in Polish Corpus KPWr

Temporal Expressions in Polish Corpus KPWrThis article presents the result of the recent research in the interpretation of Polish expressions that refer to time. These expressions are the source of information when something happens, how often something occurs or how long something lasts. Temporal information, which can be extracted from text automatically, plays significant role in many information extraction systems, such as question answering, discourse analysis, event recognition and many more. We prepared PLIMEX — a broad description of Polish temporal expressions with annotation guidelines, based on the state-of-the-art solutions for English, mainly TimeML specification. We also adapted the solution to capture the local semantics of temporal expressions, called LTIMEX. Temporal description also supports further event identification and extends event description model, focusing at anchoring events in time, ordering events and reasoning about the persistence of events. We prepared the specification, which is designed to address these issues and we annotated all documents in Polish Corpus of Wroclaw University of Technology (KPWr) using our annotation guidelines

Distribution Transformer Parameters Detection Based on Low-Frequency Noise, Machine Learning Methods, and Evolutionary Algorithm

The paper proposes a method of automatic detection of parameters of a distribution transformer (model, type, and power) from a distance, based on its low-frequency noise spectra. The spectra are registered by sensors and processed by a method based on evolutionary algorithms and machine learning. The method, as input data, uses the frequency spectra of sound pressure levels generated during operation by transformers in the real environment. The model also uses the background characteristic to take under consideration the changing working conditions of the transformers. The method searches for frequency intervals and its resolution using both a classic genetic algorithm and particle swarm optimization. The interval selection was verified using five state-of-the-art machine learning algorithms. The research was conducted on 16 different distribution transformers. As a result, a method was proposed that allows the detection of a specific transformer model, its type, and its power with an accuracy greater than 84%, 99%, and 87%, respectively. The proposed optimization process using the genetic algorithm increased the accuracy by up to 5%, at the same time reducing the input data set significantly (from 80% up to 98%). The machine learning algorithms were selected, which were proven efficient for this task

Classification of Low Frequency Signals Emitted by Power Transformers Using Sensors and Machine Learning Methods

This paper proposes a method of automatically detecting and classifying low frequency noise generated by power transformers using sensors and dedicated machine learning algorithms. The method applies the frequency spectra of sound pressure levels generated during operation by transformers in a real environment. The spectra frequency interval and its resolution are automatically optimized for the selected machine learning algorithm. Various machine learning algorithms, optimization techniques, and transformer types were researched: two indoor type transformers from Schneider Electric and two overhead type transformers manufactured by ABB. As a result, a method was proposed that provides a way in which inspections of working transformers (from background) and their type can be performed with an accuracy of over 97%, based on the generated low-frequency noise. The application of the proposed preprocessing stage increased the accuracy of this method by 10%. Additionally, machine learning algorithms were selected which offer robust solutions (with the highest accuracy) for noise classification

Towards an event annotated corpus of Polish

Towards an event annotated corpus of Polish The paper presents a typology of events built on the basis of TimeML specification adapted to Polish language. Some changes were introduced to the definition of the event categories and a motivation for event categorization was formulated. The event annotation task is presented on two levels – ontology level (language independent) and text mentions (language dependant). The various types of event mentions in Polish text are discussed. A procedure for annotation of event mentions in Polish texts is presented and evaluated. In the evaluation a randomly selected set of documents from the Corpus of Wrocław University of Technology (called KPWr) was annotated by two linguists and the annotator agreement was calculated. The evaluation was done in two iterations. After the first evaluation we revised and improved the annotation procedure. The second evaluation showed a significant improvement of the agreement between annotators. The current work was focused on annotation and categorisation of event mentions in text. The future work will be focused on description of event with a set of attributes, arguments and relations

Temporal Expressions in Polish Corpus KPWr

Temporal Expressions in Polish Corpus KPWr This article presents the result of the recent research in the interpretation of Polish expressions that refer to time. These expressions are the source of information when something happens, how often something occurs or how long something lasts. Temporal information, which can be extracted from text automatically, plays significant role in many information extraction systems, such as question answering, discourse analysis, event recognition and many more. We prepared PLIMEX — a broad description of Polish temporal expressions with annotation guidelines, based on the state-of-the-art solutions for English, mainly TimeML specification. We also adapted the solution to capture the local semantics of temporal expressions, called LTIMEX. Temporal description also supports further event identification and extends event description model, focusing at anchoring events in time, ordering events and reasoning about the persistence of events. We prepared the specification, which is designed to address these issues and we annotated all documents in Polish Corpus of Wroclaw University of Technology (KPWr) using our annotation guidelines