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IntroductionOur current medical curriculum devotes a large percentage of time to knowledge acquisition by means of didactic lectures. Psychomotor skill acquisition takes a back seat. Certain lifesaving skills like basic life support skill training have not even made an appearance in the current curriculum. Equal time distribution to cognitive and psychomotor skills should be allotted for MBBS trainees, which is a very practical subject. Simulation can prove to be a valuable tool in imparting skill training. The present study aims to evaluate the efficacy of different teaching modalities in imparting lifesaving skills among first-year MBBS students.Materials and methodsThis cross-sectional study was conducted among 33 first-year students who consented to participate. Approval was obtained from the institutional ethics committee. The students were divided into three groups, each undergoing either didactic lecture or animation-based videos or simulation studies. Pretest, posttest, and skills tests were administered to them. One-way analysis of variance (ANOVA) and paired t test were the statistical tests employed using SPSS version 21.ResultsThe pretest and posttest scores were comparable in the three groups while the improvement in the posttest scores in all the three groups was significant. The skills test was significantly better in the group undergoing simulation training compared to the other groups.ConclusionDidactic, animation, and simulation are all good methods in imparting cognitive knowledge, but simulation is the method of choice in imparting psychomotor skills.Clinical significanceAn overhauling of the medical curriculum to include more skills training to the budding doctors using simulation-based techniques is recommended.How to cite this articleSuseel A, Panchu P, Abraham SV, Varghese S, George T, Joy L. An Analysis of the Efficacy of Different Teaching Modalities in Imparting Adult Cardiopulmonary Resuscitation Skills among First-year Medical Students: A Pilot Study. IJCCM 2019;23(11): 509–512.

In our research work we will collect the data of drugs as well as protein regarding hematic diseases, then applying feature extraction as well as classification, predict hot spot and non-hot spot then we are predicting the hot region using prediction algorithm. Parallelly from the hematological drug we are extracting the feature using molecular finger print then classifying using a classifier and applying deep learning concept to reduce the dimensionality then finally using machine learning algorithm predicting which drug will interact with the help of a hybrid approach.