This report describes how good medication guidance may be provided to patients with blood cancer and discusses the role of the pharmacist in raising the satisfaction of patients and family members with treatment. Chemotherapy for blood cancer causes a variety of severe adverse reactions, giving patients and their families a feeling of anxiety towards it. It is therefore necessary for pharmacists to provide medication guidance in order to reduce anxiety towards drugs during longterm chemotherapy.In the present study, we examined questions regarding chemotherapy from patients and their families for 32 cases in the past 3 years. The 485 questions asked were classified according to subject as follows : 1) Adverse reactions-40%, 2) Chemotherapy protocol-12%, 3) Blood transfusions-7 % , 4) Effects of chemotherapy-6%, 5) Granulocyte-colony stimulating factor (G-CSF)-6%, 6) Method of use-4%, 7) Drugs preventing opportunistic infections-4%, 8) Administration method-3 %, 9) Chemotherapy in outpatient ward-3 % , 10) High-dose chemotherapy-3 % , 11) Discharge and short stay at home-1%, 12) Drop-out-1 % , 13) Drawing blood-1 % , 14) Color of chemotherapy agents for injection-1 % , and 15) Differences between treatment strategies at other hospitals-1%.

This paper evaluates human emotional change by sound stimuli focused on chord progression in jazz music and conducts computational emotion classification from physiological information. Psychological experiments using chord progression tunes as sound stimuli are conducted with 117 subjects and the result of subjective evaluation shows that positive emotional valance chord progression tunes that have ascending fourth aroused positive images, and negative emotional valence chord progression tunes that have chromatic descent aroused negative images. Psychophysical experiments using chord progression tunes to excite emotions in subjects are conducted to gather acceleration plethysmogram data. For computational emotion classification, multi-layer neural network using feature values extracted from heart rate and acceleration plethysmogram is used to discriminate emotional class. In experiments of computational emotion classification, an average of 38.3% classification rate is attained in three emotions -positive, negative, and neutral.