Clinical records contain patient information such as laboratory values, doctor notes, or medications. However, clinical notes are underutilized because notes are complex, highdimensional, and sparse. However, these clinical records may play an essential role in modeling clinical decision support systems. The study aimed to develop an effective predictive learning model that can process these sparse data and extract useful information to benefit the clinical decision support system for the effective diagnosis. The proposed system conducts phasewise data modeling, and suitable text data treatment is carried out for data preparation. The study further utilized the Natural Language Processing (NLP) mechanism where word2vec with Autoencoder is used as a clustering scheme for the topic modeling. Another significant contribution of the proposed work is that a novel approach of learning mechanism is devised by integrating Long Short Term Memory (LSTM) and Convolution Neural Network (CNN) to learn the inter-dependencies of the data sequences to predict diagnosis and patient testimony as output for the clinical decision. The development of the proposed system is carried out using the Python programming language. The study outcome based on the comparative analysis exhibits the effectiveness of the proposed method.
The area of healthcare sector is now meeting a new challenge of data management. Owing to adoption of advance technology for patient-related services as well as diagnosis, a high-dimensional data is being generated. The biggest problems of such data are manifold e.g. i) they are much bigger in size that is difficult to be stored in physical servers, ii) they are massively growing in size with respect to increase of time, iii) they are of various forms and formats owing to be generated from multiple devices, and iv) there is larger dimensionality of uncertainty too. Owing to all these problems, it is almost impossible to apply the conventional data analysis algorithm for extracting teh knowledge. This paper discusses about the some of the recently adopted technique for analysis such medical data for an effective disease detection and classification with a contribution of exploring the research gap for the existing literatures.
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