Analysis of a Multilevel Diagnosis Decision Support System and Its Implications: A Case Study

Abstract

Medical diagnosis can be performed in an automatic way with the use of computer-based systems or algorithms. Such systems are usually called diagnostic decision support systems (DDSSs) or medical diagnosis systems (MDSs). An evaluation of the performance of a DDSS called ML-DDSS has been performed in this paper. The methodology is based on clinical case resolution performed by physicians which is then used to evaluate the behavior of ML-DDSS. This methodology allows the calculation of values for several well-known metrics such as precision, recall, accuracy, specificity, and Matthews correlation coefficient (MCC). Analysis of the behavior of ML-DDSS reveals interesting results about the behavior of the system and of the physicians who took part in the evaluation process. Global results show how the ML-DDSS system would have significant utility if used in medical practice. The MCC metric reveals an improvement of about 30% in comparison with the experts, and with respect to sensitivity the system returns better results than the experts.

Nanopublishing Clinical Diagnoses: Tracking Diagnostic Knowledge Base Content and Utilization

“…In [35], we describe a multi-level diagnosis system based on [2], [5], [24], [25]. The output from that investigation includes both clinician and automated diagnoses; however, provenance data was not formally associated with these outputs, for example, how the clinician or diagnostic system reached its conclusion.…”

“…Our software first creates a Nanopublication Collection file containing the clinical data of the patients from [35]. This includes the Clinical Findings manifested by the patient and provenance data related with those assertions.…”

Personalization and Patient Involvement in Decision Support Systems: Current Trends

“…Only one paper reports results in terms of size effect of the patient exposure to the system on some fac-tors, such as knowledge about the disease, and attitude towards screening [11]. Fifteen papers present an evaluation of the effectiveness of the system, either in terms of correctness of the decision [16,18,24,25,26,42,46,75], clinical outcome [31], care flow [15,29,34], compliance [38,47] or costs [13]. As far as correctness of the decision is concerned, in six cases the system output is compared with expert opinion, using a number of patients ranging from 20 to 99 (fake subjects in two cases), and results are given in terms of a variety of indicators, such as recall, accuracy, sensitivity and specificity [18,24,25,26,42,46].…”

A Dual Hesitant Fuzzy Multigranulation Rough Set over Two-Universe Model for Medical Diagnoses

“…However, to cope with imperfect and uncertain information induced by several sources of vagueness, the classical fuzzy set is confronted with some limitations. Thus, many extension forms of fuzzy sets have been introduced and utilized in disease diagnoses [ 2 – 5 ].…”