ISSN: 2277-9655 [Korrapati* et al., 6(5): May, 2017] Impact Factor: 4.116 IC™ Value: 3.00 CODEN: IJESS7 http: // www.ijesrt.com © International Journal of Engineering Sciences & Research Technology [399] IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY COMPREHENSIVE RESEARCH MAP ON MEDICAL DECISION SUPPORT SYSTEMS (MDSS) – HISTORICAL EVOLUTION REVIEW Raghu Babu Korrapati * * Department of Computer Science, Rayalaseema University, India DOI: 10.5281/zenodo.573546 ABSTRACT Medical Decision Support System (MDSS) plays an increasingly crucial role in medical practices as it assists physicians to make clinical decisions and thus MDSS are expected to improve the overall quality of medical care. Research in the field of MDSS has suggested the there is a rising need to understand and discuss various developments in the field and how they have enhanced the medical and healthcare sector. This paper will provide a research roadmap to various developments over the years in the field of MDSS. KEYWORDS: Medical Decision Support System (MDSS), Research Roadmap, Historical Evolution INTRODUCTION and RESEARCH MAP Research in the field of MDSS has suggested the there is a rising need to understand and discuss various developments in the field and how they have enhanced the medical and healthcare sector. This section will provide a roadmap to various developments over the years in the field of MDSS. The first research article dealing with medicine and computers appeared in late 1950s (Ledley & Lusted, 1959). Artificial intelligence has been proposed as a reasoning tool to support clinical decision-making since the earliest days of computing (Ledley & Lusted, 1959). Later an experimental prototype appeared in the early 60s (Warner et al., 1964). At that time limited capabilities of computer did not allow it to be a part of medical domain. However, in these early years three advisory systems: de Dombal’s system for diagnosis of abdominal pain (de Dombal et al., 1972), Shortliffe’s MYCIN system for antibiotics selection (Shortliffe, 1976), and HELP system for medical alerts delivery (Haug et al., 1994) were considered the most significant. de Dombal’s system for diagnosis of abdominal pain (1972) provides a comparison of human diagnosis as opposed to computer aided diagnosis of patients suffering from acute abdominal pain. The results showed that system accuracy was significantly higher than those of even extremely senior and experienced clinical team. Shortliffe’s MYCIN used backward chaining through its rule base to collect information to identify the organism causing bacteremia or meningitis in patients. A large number of rule-based MDDS systems have been developed over the years, but most rule-based MDDS systems have been devoted to narrow application areas, due to the extreme complexity of maintaining rule-based systems with more than a few thousand rules (Miller, 1994). HELP has the ability to generate alerts when abnormalities in the patient record are noted, and its impact on the development of the field has been immense, with applications and methodologies that span nearly the full range of activities in biomedical informatics (Haug et al., 1994). The HELP hospital information system has been used to explore computerized interventions into the medical decision making process. By their nature these interventions imply a computer-directed interaction with the physicians, nurses, and therapists involved in delivering care. The 1990s and 2000s witnessed a large-scale shift from administrative systems to clinical decision support systems. Study by Forgionne in 1991 discuss the cost related problems in the approach to prepaid medical care management. Bayesian Networks has been successfully used in MDSS (Korrapati, 2000a, Korrapati, 2000b). The study aims to identify the problems and suggest how decision support systems are used to overcome and design cost-efficient and market effective health plans. In 2009, Übeyli conducted a thorough research in the area of medical support system, which shows that there are several machine learning techniques that can be used to predict susceptibility of chronic diseases. Diagnosis expert systems have been developed in order to help predict and diagnose certain kinds of diseases. Following Figure 1 shows the historical evolution of MDSS.