Abstract
Clinical records contain patient information such as laboratory values, doctor notes, or medications. However, clinical notes are underutilized because notes are complex, high-dimensional, 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 phase-wise data modeling, and suitable text data treatment is carried out for data preparation. The study further utilized the Natutal 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.
Highlights
Depending on the entry point of the patient, the system needs to be designed in such a way that it gives higher importance to patient testimony if the patient is entering from the OPD, and gives lower importance to the same while the patient is entering from ER [9-10]
Recent advances in technologies have shown that natural language processing (NLP) and ML algorithms can be used to build an effective Clinical Decision Support (CDS) system to benefit a successful diagnosis with high scores
This paper has presented an effective learning system to support the clinical decision process in the patient diagnosis
Summary
The diagnosis is a critical part of the healthcare system that decides the kind of treatment that needs to be given to the patient and builds the entire treatment strategy. These clinical notes usually have two parts to it, namely, i) Patient testimony ii) Doctor's notes These two contain different types of information, which acts as a powerful resource providing detailed patient conditions and clinical inference, which usually cannot be obtained from the other components of the electronic health record [3-4]. These two parts of the clinical notes have distinct importance in order to make diagnosis better. Among the patients who enter the hospital from Out Patient Department (OPD), it is shown that 80% of the patient (Non-psychological conditions) testimony is fully reliable [7-8] This proves the point that initial diagnosis is the critical step in the healthcare cycle. The main contribution of the proposed study is highlighted as follows: To emphasize the usage of discharge summary of a patient in order to extract more information data associated with admission of patient for leveraging diagnosis
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Computer Science and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
