Unveiling the Dynamics of Human-AI Complementarity through Bayesian Modeling
Article Title: Bayesian modeling of human–AI complementarity Authors & Year: M. Steyvers, H. Tejeda, G. Kerrigan, and P. Smyth (2022) Journal: Proceedings of the National Academy of Sciences of the United States of America [DOI:10.1073/pnas.2111547119] Review Prepared by David Han Exploration of Human-Machine Complementarity with CNN In recent years, artificial intelligence (AI) and machine learning (ML), especially deep learning, have advanced significantly for tasks like computer vision and speech recognition. Despite their high accuracy, these systems can still have weaknesses, especially in tasks like image and text classification. This has led to interest in hybrid systems where AI and humans collaborate, focusing on a more human-centered approach to AI design. Studies show humans and machines have complementary strengths, prompting the development of frameworks and platforms for their collaboration. To explore this further, the authors of the paper developed a Bayesian model for image classification tasks, analyzing predictions from both humans…
![Flow chart showing the steps of the LLM approach for clinical prdeiction. The top left is part a with a Lagone EHR box connected to 2 boxes, NYU Notes(clinical notes) and NYU Fine-Tuning (clinical notes and task specific labels). The top right is the pret-training section with NYU notes (clinical notes) in the far top right connected to a language model box which then connects to a larger masked language model box below it (fill in [mask]: a 39-year-old [mask] was brough in by patient (image representing llm replying) patient)). The bottom left has nyu fine tuning (clinical notes and task specific tasks) in the top right of its quadrant connected to a pretrained model box which connects down to a larger fine-tuning box, specifically the predicted p(label) ground truth pair (0.6, 0.4) which connects to a small box inside the big on lageled loss which goes to another small box labeled weight update which goes back to the pretrained model box. The last quadrant in the bottom right has two boxes on the left of fine-tuned model and hospital ehr(clinical notes) connected to inference engine in the top right of the quadrant that connects down to the email alert (physician) box](https://mathstatbites.org/wp-content/uploads/apollo13_images/msbHanNyu1-7ipft2sg6himqf4p6irp5l74aclvk4e4w6.png)
The A.I. Doctor is In – Application of Large Language Models as Prediction Engines for Improving the Healthcare System
Predictive Healthcare Analytics
Physicians grapple with challenging healthcare decisions, navigating extensive information from scattered records like patient histories and diagnostic reports. Current clinical predictive models, often reliant on structured inputs from electronic health records (EHR) or clinician entries, create complexities in data processing and deployment. To overcome this challenge, a team of researchers at NYU developed NYUTron, an effective large language model (LLM)-based system, which is now integrated into clinical workflows at the NYU Langone Health System. Using natural language processing (NLP), it reads and interprets physicians’ notes and electronic orders, trained on both structured and unstructured EHR text. NYUTron’s effectiveness was demonstrated across clinical predictions like readmission (an episode when a patient who had been discharged from a hospital is admitted again), mortality (death of a patient), and comorbidity (the simultaneous presence of two or more diseases or medical conditions in a patient) as well as operational tasks like length of stay and insurance denial within the NYU Langone Health System. Reframing medical predictive analytics as an NLP problem, the team’s study showcases the capability of LLM to serve as universal prediction engines for diverse medical tasks.
Teaching Models by Adding Feature Hints
Machine learning models are excellent at discovering patterns in data to make predictions. However, their insights are limited to the input data itself. What if we could provide additional knowledge about the model features to improve learning? For example, suppose we have prior knowledge that certain features are more important than others in predicting the target variable. Researchers have developed a new method called the feature-weighted elastic net (“fwelnet”) that integrates this extra feature knowledge to train smarter models, resulting in more accurate predictions than regular techniques.
How to Learn about Housing Dynamics when You Don’t Have Housing Data
Data surrounds us in many aspects of our lives. We look at ratings on Amazon to determine whether to buy a product. We use Fitbits to track our step count. We browse Netflix recommendations generated using our streaming history. Everywhere, decisions are being made from numbers and data. However, while it seems like we can get data on anything, some datasets are much easier to collect than others.