Artificial Intelligence

Software that allows machines or computer systems to perceive their environment and use learning and intelligence to achieve a defined goal.

Machine Learning

An area in artificial intelligence with a focus on statistical algorithms that can learn from data and generalize to unseen data.

Deep Learning

A subset of machine learning methods that are based on neural networks, with deep implying multiple layers.

• a subset of AI and a type of deep learning model
• designed to create new and "original" content
• trained on massive datasets of existing content

A supervised machine learning approach requires labelled input and output data, allowing human oversight of the model's classification.

• Regression (prediction of a continuous variable):
  • Linear Regression
  • Polynomial Regression
• Classification (prediction of a categorical variable):
  • Decision Trees
  • Random Forest
  • Logistic Regression
  • K-Nearest Neighbors

An approach that can be used to group data when no labels are present. Typically applied to cases where the model is representative of the data to ask

• Clustering:
  • K-Means
  • DBSCAN
  • Hierarchical Clustering
• Dimensionality Reduction:
  • Principal Component Analysis (PCA)
  • Singular Value Decomposition (SVD)

A computational model inspired by biological neural networks, inspired by the behavior of neurons.

Can be supervised, semi-supervised, self-supervised, unsupervised.

https://adamharley.com/nn_vis/mlp/2d.html

• Type of problem - supervised vs unsupervised; image recognition or NLP
• Model Complexity - more layers or nodes = more training data needed
• Data Quality and Accuracy - noisy data will require more training data

• Estimated $100 Million to Train GPT-4
• Trained on roughly 13 trillion tokens / 10 trillion words

• Multinomial Logistic Regression (pangolin 2.0)
• Decision Trees (pangolin 2.0 and 3.0)
• Random Forests (pangolin 4.0)

"Using phenotypic AST profiles as the reference, error designation and accuracy statistics were performed for 152 bacterial-drug combinations with an overall categorical agreement of 80.9% and very major, major, and minor error rates of 3.8%, 23.3%, and 11.8%, respectively."

Combination of WGS surveillance and ML of electronic health records to identify outbreaks and transmission routes.

"EDS-HAT could have prevented 25 (lower bound) to 63 (upper bound) transmissions. Moreover, 3.1–8.0 fewer 30-day attributable readmissions and 1.6–3.3 fewer deaths would have occurred had EDS-HAT been running in real time."

• cross-sectional study of 195 randomly drawn patient questions from Reddit’s r/AskDocs
• compared physician’s and chatbot’s responses to patient’s questions asked publicly on Reddit’s r/AskDocs
• chatbot responses were preferred over physician responses and rated significantly higher for both quality and empathy

• NYUTron - an LLM trained on clinical language and fine-tuned across a wide range of clinical and operational predictive tasks
  • 30-day all-cause readmission prediction
  • in-hospital mortality prediction
  • comorbidity index prediction
  • length of stay prediction
  • insurance denial prediction

AI hallucination - a phenomenon where a large language model perceives a pattern that is nonexistent to human observers resulting in outputs that are nonsensical or inaccurate.

• LLM Hallucinations
  • False Facts - confidently state incorrect information
  • Imaginary Scenarios - entirely fabricated stories or events
  • Nonsense/Incoherence - output that doesn't follow any logical flow or grammatical rules