AI Flashcards Lecture 5

Question 1

What is the prevalence of conduct disorder in the general population according to the lecture?

Answer 1

Conduct disorder has a prevalence of 5-7% in the general population.

Question 2

What are some of the core symptoms of conduct disorder, according to the lecture?

Answer 2

Core symptoms of conduct disorder include aggression/intimidation, destruction/vandalism, lying/stealing, and breaking rules. It is also more common in males than females.

Question 3

What is the prevalence of psychopathy in the general population according to the lecture?

Answer 3

Psychopathy has a prevalence of 1% in the general population, but a much higher prevalence of 30% in the incarcerated population.

Question 4

What are some of the key signs of psychopathy, according to the lecture?

Answer 4

Key signs of psychopathy include a lack of empathy/guilt/remorse, insensitivity to punishment or fear, reward-orientation, and self-centeredness.

Question 5

What are some of the limitations of current research on antisocial behaviour, according to the lecture?

Answer 5

Current research is limited by a focus on groups instead of individuals, small studies, a lack of research harmonisation, low sociodemographic diversity, mono-disciplinary research, and limited “team science”.

Question 6

What is the ‘reproducibility crisis’ in research, according to the lecture?

Answer 6

The ‘reproducibility crisis’ refers to the difficulty in replicating findings from previous studies due to the issues described in flashcard 5.

Question 7

What are the proposed solutions to address the ‘reproducibility crisis’, according to the lecture?

Answer 7

The lecture proposes moving from group to individual-level inferences, employing big data and modern statistics, and enhancing interdisciplinary ‘team science’ as solutions.

Question 8

What is the classic case-control approach and what are its limitations according to the lecture?

Answer 8

The classic case-control approach assumes that cases and controls each form a well-defined group. However, clinical populations are often more heterogeneous and may be composed of multiple groups with distinct pathologies, which this method overlooks.

Question 9

What is ‘Big Data’, as described in the lecture?

Answer 9

‘Big Data’ refers to data that is so large, fast, or complex that it is difficult or impossible to process using traditional methods.

Question 10

What is Artificial Intelligence (AI) in the context of big data, according to the lecture?

Answer 10

AI involves teaching computers how to learn from big data to make decisions or predictions on new, unseen data. This includes uncovering patterns, handling multimodal data, and prioritizing generalizability. Machine learning is often employed.

Question 11

What are some of the benefits of using Big Data and AI, according to the lecture?

Answer 11

Benefits include more data and resources, better expertise, multi-disciplinary collaboration, oversight, correction of malpractice, and harmonization of methods.

Question 12

What is the ‘team science’ approach, according to the lecture?

Answer 12

The ‘team science’ approach emphasizes interdisciplinary collaboration, leveraging diverse expertise, and ensuring oversight and control, to improve research outcomes.

Question 13

What are some examples of how AI and big data can be used for prediction, according to the lecture?

Answer 13

An algorithm has been developed that can predict mental health crises within the next 28 days with 80% accuracy, and crime one week in advance with 90% accuracy.

Question 14

What ethical considerations are raised with the use of big data and AI in crime prediction, according to the lecture?

Answer 14

While crime prediction algorithms can be very successful, they can also expose ethical shortcomings such as biases in crime enforcement.

Question 15

What is ENIGMA in the context of the lecture?

Answer 15

ENIGMA is a research project focused on antisocial behaviour, anxiety, and youth mental health, utilising big data and team science.

Question 16

How is ENIGMA using AI to study anxiety in youth, according to the lecture?

Answer 16

ENIGMA is using AI to classify anxious youth vs. controls based on brain morphology, achieving accuracy up to 65% with some approaches and aiming for at least 80% for clinical relevance.

Question 17

What does ENIGMA hope to achieve through deep integrated data and personalised care, according to the lecture?

Answer 17

ENIGMA aims to provide early detection, treatment, and prognosis using integrated data including genetics, brain connectivity, stress/immunity, behaviour, and socio-environment factors to provide personalised care to at-risk and psychiatric youth.

Question 18

What are the different phases of the ENIGMA project and what are some of the research goals, according to the lecture?

Answer 18

Phase 1 focused on anxiety disorders and involved brain morphological data from 4000 patients and 9000 controls. Phase 2 involves a broader transdiagnostic approach with over 20,000 participants and aims to study anxiety, MDD, SZ, BD, and controls. The research goals include classification, prediction, and clustering to advance our understanding of mental disorders.

Question 19

What is the analytical approach used in the ENIGMA project according to the lecture?

Answer 19

The analytical approach involves multi-layered data exploration via machine learning (ML) pipelines, incorporating data pre-processing, fusion and integration of data, and then clustering, prediction, and classification.

Question 20

What are some of the implications of AI and big data for youth mental health, according to the lecture?

Answer 20

AI and big data show encouraging early results for improving our understanding of and ability to treat youth mental health. However, it is also acknowledged that there is still a long way to go, and many ethical issues need to be considered, including strict replication and validation, and that AI is not a silver bullet.