The research on doctor-patient interactions is gaining popularity, thanks to the recent advances in deep learning models that can handle unconstrained input. Most systems developed for doctor-patient interactions employ a task-oriented dialog (TOD) system that solves a patient's particular task, such as diagnosis, monitoring, assistance, and counseling. A modern TOD system is based on frame-based architecture. In frame-based architecture, the frames consist of slots that are filled with values elicited from the user. The conversations between the system and the patient will flow towards completing the frame. The frame specific to clinical interactions mostly records the patient's demographic and medical history. However, the information recorded in a frame might be insufficient if the patient forgets to mention a few symptoms. Then how do we get the complete information from the user? In this paper, we present a novel task symptom recommendation - whose goal is to automatically remind symptoms to the patient by learning directly from the doctor-patient conversational dataset. For this task, we gathered a real-life dataset with doctor-patient dialogs involving different medical specializations. Also, we experiment with multiple popular RS models.