Non Hodgkin Lymphomas (NHL) are a group of neoplastic hematologic diseases which are characterized by chemo resistance, progressions and relapses. It would be very important to treat patients according to their disease characteristics and, when the neoplasm recurred, recognized it as soon as possible through a proper follow up program. In our study we tried to face these two aspects with the help of computer science analysis. First we developed a partitioning recursive algorithm, know as Hypothesis testing Classifier System algorithm (HCS), with the aim to discover features potentially useful to detect patients' subsets with different clinical behavior and prognosis, and therefore use it to shape the treatment. In such a way we analyzed data concerning 651 patients. The algorithm was able to detect two major groups: patients who achieved respectively complete (CR) or partial remission (PR). Even if the quality of response seemed the more important feature, when running again the algorithm others characteristics emerged among high grade NHL diagnosis, especially age and treatment approach (transplant approach and immunotherapy). Then we tried to improve our follow up schedule through a method known as multi-objective analysis. This approach works starting to choose the costs which could reflect the effectiveness of a follow up, then it calculates these values for our current one (on the basis of data available of 418 patients) and finally looks for the possible new follow up with the aim to optimized our schedule. Even if six new possible ones were obtained, the maximum improving for our current follow up was 4%: therefore our current program could be considered properly planned. In conclusion the use of this innovate approach applied to hematologic patients has been successful with the achievement of good and interesting results.