If you are someone who suffers from Post Traumatic Stress Disorder, you may be interested to learn that memory can help you overcome it. Research suggests that the ability to remember the negative events of an aversive experience, and to systematically erase memories of such events, can play an important role in recovery. This is because fear memories are not only a source of emotional discomfort; they also play a critical role in preventing a person from having a similar experience.
Posttraumatic stress disorder (PTSD) is a psychopathological condition that develops after a traumatic event. Individuals with PTSD reexperience the traumatic event in a way that causes symptoms. These symptoms can include depression, anxiety, hypersensitivity, and numbness. As a result, people with PTSD may not engage in activities that they previously enjoyed. This raises an enormous public health burden.
A key feature of PTSD is a failure of inhibitory learning. People who develop PTSD have difficulty inhibiting their fear response when there is no threat in the environment. Similarly, fear-related symptoms take longer to fade away than normal.
Fear extinction and inhibitory learning are important mechanisms of cognitive control in psychiatric disorders. Inhibitory learning can be enhanced through exposure-based therapy or nonpharmacological techniques. Exposure-based therapy involves engaging an individual in trauma-related stimuli in a safe, controlled setting. The goal is to reduce conditioned fears in the presence of safe situations and to induce extinction.
Extinction is not simply erasing the knowledge that the danger signal precedes the aversive stimulus
Extinction learning is a process that results in an individual's fear of a stimulus being diminished or eliminated. It may generalize to new situations, with varying stimuli. However, extinction does not just mean that a person's fear response is reduced, but that the original stimulus and associated danger signal are weakened.
A variety of behavioral strategies can help enhance extinction learning. For instance, using positive affect can make a threatening stimulus more positive and can lead to enhanced rehearsal.
Another strategy involves reducing context dependence. Specifically, reducing the reliance on a conditioned stimulus's context can improve extinction learning. Device-based stimulation techniques, such as rTMS, have been found to augment extinction learning.
Machine learning techniques may provide insights into subgroups that may benefit from particular interventions
Machine learning techniques have been used in health care to improve the accuracy of predictive models. This new technology can detect nonlinear and higher-order relationships. It can also detect and recognize probabilistic matches across databases. Combined with a good data set, machine learning techniques can improve the accuracy of predictions.
One area in which machine learning can help is predicting whether a patient will develop a disease. Detecting early signs of a disease can reduce the risk of illness and improve outcomes. For example, a health insurer may use predictive modeling to identify members with complex health needs. The insurer can then prioritize outreach based on the member's risk score.
A machine learning model might be able to detect if a patient's medical record contains a missing symptom. The health insurance industry is now using machine learning to analyze large amounts of patient and provider data. However, health insurers are faced with challenges in assessing and mitigating bias.