Processing Medical Natural Language Data by the System WAREL

1988 ◽  
Vol 27 (02) ◽  
pp. 67-72 ◽  
Author(s):  
W. Dorda ◽  
B. Haidl ◽  
P. Sachs
Keyword(s):  
Natural Language ◽  
Knowledge Base ◽  
Clinical Data ◽  
Textual Analysis ◽  
Statistical Analyses ◽  
Language Form ◽  
Scientific Investigations ◽  
Textual Data ◽  
Language Data

SummaryMany clinical data are in natural language form (diagnoses, therapies, etc.). There is great interest in making these data retrievable to form samples of patients for scientific investigations (statistical analyses, courses of diseases, etc.). To perform this task, “medical natural language data” have to be prepared and stored in a retrieval-oriented database. In this paper, the advantages of processing textual data are shown in contrast to coding. Accordingly, in our system WAREL medical thesauri (like ICD 9 or SNOMED) are not used for codification; they are taken as a knowledge base during the retrieval and for testing the quality of the data during documentation. The fundamental methods (computerized textual analysis and different algorithms for comparing texts) are explained in detail, and their realization within the system WAREL is illustrated (WAREL stands for Wiener Allgemeines Relationenschema).

scholarly journals Design of a Diabetic Diagnosis System Using Rough Sets

2013 ◽  
Vol 13 (3) ◽  
pp. 124-139 ◽  
Author(s):  
Margret Anouncia S. ◽  
Clara Madonna L. J. ◽  
Jeevitha P. ◽  
Nandhini R. T.
Keyword(s):  
Decision Making ◽  
Knowledge Base ◽  
Clinical Data ◽  
Uncertain Data ◽  
Decision Making Process ◽  
Diagnosis System ◽  
Adequate Knowledge ◽  
Simple Set ◽  
Made In

Abstract Traditionally the diagnosis of a disease is done by medical experts with experience, clinical data of the patients and adequate knowledge in identifying the disease. Such diagnosis is found to be approximate and time-consuming since it purely depends on the availability and the experience of the medical experts dealing with imprecise and uncertain clinical data of the patients. Hence, to improve decision making with uncertain data and to reduce the time consumption in diagnosing a disease, several simulated diagnosis systems have been developed. Most of these diagnosis systems are designed to possess the clinical data and symptoms associated with a specific disease as knowledge base. The quality of the knowledge base has an impact not only on the consequences, but also on the diagnostic precision. Most of the existing systems have been developed as an expert system that contains all the diagnosis facts as rules. Notably, applying the concept of a fuzzy set has shown better knowledge representation to improve the decision making process. Therefore an attempt is made in this paper to design and develop such diagnosis system, using a rough set. The system developed is evaluated using a simple set of symptoms that is added to clinical data in determining diabetes and its severity.

scholarly journals Textual analysis or natural language parsing? A software engineering perspective

2015 ◽  
Author(s):  
Sebastiano Panichella
Keyword(s):  
Software Engineering ◽  
Software Development ◽  
Natural Language ◽  
Communication Channel ◽  
Textual Analysis ◽  
Textual Information ◽  
Its Analysis ◽  
Textual Data ◽  
Natural Language Parsing ◽  
Maintenance Decisions

The problem of designing effective methodology to summarize, and analyze the amount of textual information produced by developers remains particularly challenging especially when the goal is to help developers in making better development/maintenance decisions. Moreover, contrasting results might be obtained depending on the communication channel being mined and the technique adopted for its analysis. In our work we investigate the usage of Natural Language Parsing (NLP) and Textual Analysis (TA) techniques to automatically classify development content. Results of our study highlight the superiority of NLP techniques over the traditional TA techniques when used to analyze the textual data produced in software development. We also show the benefits of NLP when used to enhance software engineering recommenders.

scholarly journals Textual analysis or natural language parsing? A software engineering perspective

2015 ◽  
Author(s):  
Sebastiano Panichella
Keyword(s):  
Software Engineering ◽  
Software Development ◽  
Natural Language ◽  
Communication Channel ◽  
Textual Analysis ◽  
Textual Information ◽  
Its Analysis ◽  
Textual Data ◽  
Natural Language Parsing ◽  
Maintenance Decisions

The problem of designing effective methodology to summarize, and analyze the amount of textual information produced by developers remains particularly challenging especially when the goal is to help developers in making better development/maintenance decisions. Moreover, contrasting results might be obtained depending on the communication channel being mined and the technique adopted for its analysis. In our work we investigate the usage of Natural Language Parsing (NLP) and Textual Analysis (TA) techniques to automatically classify development content. Results of our study highlight the superiority of NLP techniques over the traditional TA techniques when used to analyze the textual data produced in software development. We also show the benefits of NLP when used to enhance software engineering recommenders.

scholarly journals Stochastic swarm clusterization method in natural language data processing

2018 ◽  
Vol 11 (3) ◽  
pp. 5-18
Author(s):  
G.A. Yuryev ◽  
E.K. Verkhovskaya ◽  
N.E. Yuryeva
Keyword(s):  
Natural Language ◽  
Data Processing ◽  
Reduction Method ◽  
Optimization Method ◽  
Feature Maps ◽  
Discriminating Power ◽  
Language Data ◽  
Linear Dimensionality Reduction ◽  
Dimensionality Reduction Method

Consider natural language data processing technology based on non-linear dimensionality reduction method which takes into account the discriminating power of the solution found for given values of the categorical variable associated with each observation. Stochastic optimization method known as the “Particle swarm optimization” is proposed to found characteristics that ensure the best separation of observations in terms of a given quality functional. The basis for evaluating the quality of the solution lies in the purity of the clusters obtained with the k-means method, or with using self-organizing Kohonen feature maps.

Relationships between Population Dynamics and Operating Parameters in an Activated Sludge-Plant by Statistical Analysis

1992 ◽  
Vol 25 (4-5) ◽  
pp. 399-400 ◽  
Author(s):  
L. Cingolani ◽  
M. Cossignani ◽  
R. Miliani
Keyword(s):  
Population Dynamics ◽  
Statistical Analysis ◽  
Activated Sludge ◽  
Treatment Plant ◽  
Operating Conditions ◽  
Statistical Analyses ◽  
Operating Parameters ◽  
Aerobic Treatment

Statistical analyses were applied to data from a series of 38 samples collected in an aerobic treatment plant from November 1989 to December 1990. Relationships between microfauna structure and plant operating conditions were found. Amount and quality of microfauna groups and species found in activated sludge proved useful to suggest the possible causes of disfunctions.

scholarly journals A high degree of knee flexion after TKA promotes the ability to perform high-flexion activities and patient satisfaction in Asian population

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hyuk-Soo Han ◽  
Jong Seop Kim ◽  
Bora Lee ◽  
Sungho Won ◽  
Myung Chul Lee
Keyword(s):  
Quality Of Life ◽  
Patient Satisfaction ◽  
Clinical Data ◽  
Knee Flexion ◽  
Male Gender ◽  
Flexion Angle ◽  
Knee Flexion Angle ◽  
High Flexion ◽  
Standing Up

Abstract Background This study investigated whether achieving a higher degree of knee flexion after TKA promoted the ability to perform high-flexion activities, as well as patient satisfaction and quality of life. Methods Clinical data on 912 consecutive primary TKA cases involving a single high-flexion posterior stabilized fixed-bearing prosthesis were retrospectively analyzed. Demographic and clinical data were collected, including knee flexion angle, the ability to perform high-flexion activities, and patient satisfaction and quality of life. Results Of the cases, 619 (68%) achieved > 130° of knee flexion after TKA (high flexion group). Knee flexion angle and clinical scores showed significant annual changes, with the maximum improvement seen at 5 years and slight deterioration observed at 10 years postoperatively. In the high flexion group, more than 50% of the patients could not kneel or squat, and 35% could not stand up from on the floor. Multivariate analysis revealed that > 130° of knee flexion, the ability to perform high-flexion activities (sitting cross-legged and standing up from the floor), male gender, and bilateral TKA were significantly associated with patient satisfaction after TKA, while the ability to perform high-flexion activities (sitting cross-legged and standing up from the floor), male gender, and bilateral TKA were significantly associated with patient quality of life after TKA. Conclusions High knee flexion angle (> 130°) after TKA increased the ease of high-flexion activities and patient satisfaction. The ease of high-flexion activities also increased quality of life after TKA in our Asian patients, who frequently engage in these activities in daily life.

A Hybrid Siamese Neural Network for Natural Language Inference in Cyber-Physical Systems

2021 ◽  
Vol 21 (2) ◽  
pp. 1-25
Author(s):  
Pin Ni ◽  
Yuming Li ◽  
Gangmin Li ◽  
Victor Chang
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Short Term Memory ◽  
Physical World ◽  
Heterogeneous Data ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Language Data ◽  
Text Language ◽  
Different Sources

Cyber-Physical Systems (CPS), as a multi-dimensional complex system that connects the physical world and the cyber world, has a strong demand for processing large amounts of heterogeneous data. These tasks also include Natural Language Inference (NLI) tasks based on text from different sources. However, the current research on natural language processing in CPS does not involve exploration in this field. Therefore, this study proposes a Siamese Network structure that combines Stacked Residual Long Short-Term Memory (bidirectional) with the Attention mechanism and Capsule Network for the NLI module in CPS, which is used to infer the relationship between text/language data from different sources. This model is mainly used to implement NLI tasks and conduct a detailed evaluation in three main NLI benchmarks as the basic semantic understanding module in CPS. Comparative experiments prove that the proposed method achieves competitive performance, has a certain generalization ability, and can balance the performance and the number of trained parameters.

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