Revolutionizing Medical Decision Making: Intelligent CDSS Improves Diagnosis and Treatment
A computer-aided Clinical Decision Support System (CDSS) has the potential to significantly enhance the diagnosis and treatment processes in the medical field. This intelligent system can act as a knowledgeable expert for less experienced clinicians or provide a second opinion to experienced clinicians, thereby improving their decision-making abilities. However, designing and developing such a functional system that ensures high accuracy has proven to be quite challenging.
In light of these challenges, a groundbreaking research work has focused on the development of an intelligent CDSS based on a multimodal approach. This system aims to revolutionize the diagnosis, classification, and treatment in medical domains, particularly stress and post-operative pain management.
Utilizing various Artificial Intelligence (AI) techniques such as Case-Based Reasoning (CBR), textual Information Retrieval (IR), Rule-Based Reasoning (RBR), Fuzzy Logic, and clustering approaches, this research work has investigated the integration of patient data from diverse sources. These sources include finger temperature measurements through sensor signals, pain measurements using a Numerical Visual Analogue Scale (NVAS), and patient information from text and multiple-choice questionnaires. The proposed approach also incorporates multimedia data management to enable its utilization in CDSSs for both stress and post-operative pain management domains.
To evaluate the functionalities and performance of the intelligent CDSS, close collaboration with experts and clinicians in the respective domains was conducted. In stress management, data from 68 measurements taken from 46 subjects were analyzed, along with 1572 patient cases out of approximately 4000 in post-operative pain management. Additionally, three trainees and one senior clinician were involved in the experimental work for stress management. The evaluation results demonstrated that the system reached a level of performance close to that of an expert and outperformed both the senior and trainee clinicians. Consequently, this CDSS can be utilized as an expert opinion for less experienced clinicians or as a valuable second opinion for experienced clinicians in stress management.
In post-operative pain treatment, the CDSS retrieves and presents the most similar cases, which includes both rare and regular cases, along with their outcomes. This assists physicians in making well-informed decisions. Remarkably, an automatic approach was introduced to identify rare cases, and it successfully identified 18% of the cases (276 out of 1572) as rare cases within the entire cases library. Among the identified rare cases, approximately 57.25% were classified as ‘unusually bad’ based on the average pain outcome value of 5 or higher on the NVAS scale of 0 to 10. Identifying rare cases plays a crucial role in improving individual pain treatment and is an important aspect of the PAIN OUT project.
The development of this intelligent CDSS holds tremendous potential in revolutionizing medical decision making. By harnessing the power of AI techniques and multimodal data integration, clinicians will have access to a valuable tool that enhances their diagnostic and treatment processes. The evaluation and validation of the system’s functionalities through collaboration with experts have demonstrated its effectiveness and performance. As medical technology continues to evolve, innovative solutions like this intelligent CDSS will undoubtedly have a profound impact on the future of healthcare, improving patient outcomes, and advancing the field of medicine.
