Clinical healthcare has its ambiguities, especially in the usage of several literal terms, such as more, less, adequate, poor, well, good, worse, bad, better, and many more while stating the health conditions. These terms create a wide and indefinite search space for both the providers and the receivers. It is one of the most common reasons for confusion and communication gap. In the clinical domain, symptoms are often much subjective in nature, e.g., an episode of illness can be expressed with ‘mild’, ‘moderate’, and ‘severe’ symptoms and signs, and even in its combinations, e.g., mild-to-moderate or moderate-to-severe, etc., which is decoded by the clinicians by assigning some weightage to each of these based on the clinical rule base that they have gathered and learned during their medical career (1). The conventional probability theory often fails to address this practical challenge as its working principle is either ‘present’ or ‘absent’. Therefore, the field of ‘ambiguity’ remains a fertile domain for using AI methods, such as soft computing methods (e.g., fuzzy set and fuzzy logic, neural networks, and genetic algorithm and their various combinations) which traditional machine learning approaches may not be suitable to address the real-world subjective issues (2).
Fuzzy set and fuzzy logic have been proposed and implemented by Prof. Lotfi A. Zadeh in 1965 to solve the issue of uncertainty due to literal ambiguity, such as more, less, moderate, mild, severe, etc. in mathematics (3). The fuzzy set explains the possibility of belongingness by computing the membership grade [0,1], where ‘0’ is the minimum and ‘1’ is the minimum score. It gives a wider search space than the probability of belongingness which is either ‘0’ i.e., not existing vs ‘1’ i.e., existing. Fuzzy logic is a set of IF (antecedent)-THEN (consequent) rules, which are used for decision-making. Given input with a fuzzy term, the algorithm defuzzifies it into a crisp value. Using fuzzy rules, the output is predicted. Together, it is called a fuzzy decision system (FDS). There are three types of FDS – Mamdani’s (4), Takagi-Sugeno’s (5), and Tsukamoto’s techniques (6). The first technique uses the center of gravity or centroid technique, while the remaining two use the weighted average to defuzzify and compute crisp output (7). The FDS methods have been used in diagnosing diseases, such as typhoid fever (8), mental illnesses (9), cancers (10), risk of heart disease (11), erectile dysfunction (12), etc.
Abdominal pain, fever, and vomiting feature a possibility of surgical emergency (13). Peritonitis is one of the most common surgical emergencies, which presents with this symptom triad. Peritonitis happens due to a diffuse inflammation of the peritoneal membrane that covers the abdominal organs (14). Leakage of fluid from the viscera is one of the commonest causes of this inflammation when they rupture (14). Other potential conditions are peritoneal dialysis, pancreatitis, diverticulitis, and trauma (14). The severity of peritonitis needs to be assessed by an experienced clinician, as it may turn out to be life-threatening due to septic shock, multiorgan failure, and finally cardiorespiratory arrest (14). During the history-taking process, symptoms are often presented by the patients and their caregivers in subjective literal terms, as discussed above. The experienced clinician can decode it into objective terms by assigning some weights to each of the symptoms and the rule base they have acquired over a good time of clinical practice. It is called the doctor’s ‘clinical eye’. Sharper is the clinical eye, accurate are the diagnoses. Unfortunately, highly experienced clinicians are not always available In developing nations, where most rural health centers are starving of experienced clinicians (physicians, surgeons, radiologists, etc.) and treatment facilities (X-ray and ultrasonogram or CT or MRI-scan, high-level blood tests, and so forth). It results in referring the patient to higher facilities. The referral process has documentation, logistic, and financial challenges. As a result, precious time is wasted in the patient’s life. Another issue lies with ‘wrong’ or ‘delayed’ referrals due to the respective overdiagnosing or underdiagnosing of the case. Underdiagnosis may lead to life-threatening catastrophes due to the delay while overdiagnosis raises a false alarm and wastage of money and logistic support for the transfer to happen. To address it, a simple, symptom-based predictive tool such as an intelligent referral system (IRS) may be thought of that can handle the issue.