# Instance-Based Learning Following Physician Reasoning for Assistance during Medical Consultation
## 1 Introduction  
The search for better medical practices is a perpetual challenge for modern medicine. In this regard, computational intelligence has emerged as a promising subject for developing smart systems in healthcare practice [1]. Computational intelligence allows implementing automatic tools, enabling physicians to provide patients with a better quality of attention by performing early and accurate diagnosis and improving treatment. Furthermore, automatic systems and technologies based on computational intelligence have proven to be useful solutions to be applied in clinical practice. Some important advantages of intelligent automatic methods over traditional ones include better efficiency, accuracy, consistency, more time available for face-to-face consultation, and more time for critical tasks and critical cases, among others [2]. A specific subject where the learning capabilities of computational intelligence methods is very helpful to improve medical practice is analyzing and processing electronic medical records (EMRs). EMRs refer to digital records, collected by the individual medical practice, that contain the general health information of patients [3]. They usually consist of several types of health data, including, but not limited to, demographics, medical family history, medication, allergies, test results, and radiology images. Currently, the majority of medical history recording products are based on predefined templates, which provide very limited freedom for writing patients medical records. Structured data entry is a hindrance to the usability of medical record applications, and is frowned upon by physicians who usually prefer to document using free text [4]. In addition, structured data entry systems do not take into account the particularities of the annotations of each physician, failing to effectively record the singularities of medical consultations. Alerts and suggestions offered by conventional products are generally based on previously defined rules, or according to mechanisms whose behavior remains the same throughout its operational life. The dissatisfaction of physicians with actual medical history recording products is increasing as they gain knowledge about automatic assistant tools. Consequently, physicians are increasingly aspiring to have sophisticated tools that help facilitate their clinical practice during medical consultations. The research reported in this article is motivated by the need to further explore new ways of capturing, storing, and fostering medical reasoning. Thus, a formal proposal must be conceived to provide an accurate tool capable of following medical reasoning, aiming at helping physicians during medical consultations. In this line of work, this article presents a novel approach to represent clinical knowledge, which supports an appropriate methodology to follow reasoning in medical consultation. Likewise, the proposed representation does not pose formal restrictions to physicians, as they usually find when using common clinical data entry systems. An instance-based learning method is also introduced to provide suggestions in order to help during the process of registering a medical consultation. The developed system extends Praxis (2018), a software used to follow medical reasoning with no templates, based on the accumulation of case types used to provide suggestions for subsequent cases. The proposed approach was evaluated for a case study in which more than 50 advanced medical students had collaborated. Students tested the feasibility of the approach by using a proof-of-concept prototype. The performance of the proposed learning method was found to be satisfactory after being evaluated on 250 real instances constructed by the students. Results showed that the learning method was able to produce suggestions in a reasonable time frame, even when processing large volumes of data.