Machine-Learning Algorithms Can Predict Suicide Risk More Readily Than Clinicians, Study Finds

Machine-Learning Algorithms Can Predict Suicide Risk More Readily Than Clinicians, Study Finds

Published By: Newsweek, 2/27/2017

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Human clinicians are known not to be very accurate at predicting suicides, so researchers are developing machine-learning algorithms that use multiple factors to identify short-term suicide risk. Data scientists trained the algorithm on data from thousands of clinical records, from both non-fatal suicide cases and random patients. Accuracy was significantly better than studying only one risk factor at a time. Using such a system could aid clinicians in targeting at-risk patients and treating them early.

Flesch-Kincaid Grade Level of Article: 10.5

Extended Discussion Questions

  • The article mentions that convincing clinicians to trust a machine-learning algorithm instead of their instincts could be challenging.
    • What do you think it would take for clinicians to trust this machine-learning algorithm?
    • What questions might they have?
  • Do you think the mental healthcare field should begin to use machine-learning algorithms to predict patients’ suicide risk?
    • Under what circumstances would you expect such a system to be used?
    • What questions and concerns do you think health insurance companies might have about treatments being recommended based on an algorithm’s prediction of suicide risk?
  • Do you think this research has the potential to spur progress in mental health care in general? Why or why not?
  • Moving beyond suicide risk, as a patient, what would you think if your doctor or healthcare provider told you they were recommending a treatment (for any condition) based on the risk predictions of a machine-learning algorithm?
    • Based on what you’ve learned in this class, what questions would you have before deciding whether to go through with the treatment?
  • If medical researchers wanted to do a similar study using social media, would you allow them to access your online feeds to obtain data to train their algorithms? Why or why not?
    • How do you think other people your age would respond? People in different age groups?
    • Could any ethical issues arise from such research?

Relating This Story to the CSP Curriculum Framework

Global Impact Learning Objectives:

  • LO 7.3.1 Analyze the beneficial and harmful effects of computing.
  • LO 7.4.1 Explain the connections between computing and real-world contexts, including economic, social, and cultural contexts.

Global Impact Essential Knowledge:

  • EK 7.2.1B Scientific computing has enabled innovation in science and business.
  • EK 7.3.1A Innovations enabled by computing raise legal and ethical concerns.

Other CSP Big Ideas:

  • Idea 3 Data and Information
  • Idea 4 Algorithms

Banner Image:Network Visualization – Violet – Offset Crop“, derivative work by ICSI. New license: CC BY-SA 4.0. Based on “Social Network Analysis Visualization” by Martin Grandjean. Original license: CC BY-SA 3.0.

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