A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial

Death by suicide demonstrates profound personal suffering and societal failure. While basic sciences provide the opportunity to understand biological markers related to suicide, computer science provides opportunities to understand suicide thought markers. In this novel prospective, multimodal, multicenter, mixed demographic study, we used machine learning to measure and fuse two classes of suicidal thought markers: verbal and nonverbal. Machine learning algorithms were used with the subjects’ words and vocal characteristics to classify 379 subjects recruited from two academic medical centers and a rural community hospital into one of three groups: suicidal, mentally ill but not suicidal, or controls. By combining linguistic and acoustic characteristics, subjects could be classified into one of the three groups with up to 85% accuracy. The results provide insight into how advanced technology can be used for suicide assessment and prevention.     

化学致癌机制的系统科学方法思考

本文用系统科学方法分析了化学物致癌机制，化学致癌是一个长期的，多基因，多阶段，多病因过程已被公认，研究者们认为化学致癌系统由基因和非基因因素构成，在此系统中存在若干系统属性，如整体性，综合性，动态性等，医学研究者要在癌症病因，治疗和预防中取得的半功倍成效，就应该科学地运用致癌系统中的这些系统属性。