Data Fission – Statistical Analysis through Data Point Separation
Title: Data Fission: Splitting a Single Data PointAuthors & Year: J. Leiner, B. Duan, L. Wasserman, and A. Ramdas (2023)Journal: Journal of the American Statistical Association[DOI:10.1080/01621459.2023.2270748]Review Prepared by David Han Why Split the Data? In statistical analysis, a common practice is to split a dataset into two (or more) parts, typically one for model development and the other for model evaluation/validation. However, a new method called data fission offers a more efficient approach. Imagine you have a single data point, and you want to divide it into two pieces that cannot be understood separately but can fully reveal the original data when combined. By adding and subtracting some random noise to create these two parts, each part contains unique information, and together they provide a complete picture. This technique is useful for making inferences after selecting a statistical model, allowing for better flexibility and accuracy compared to traditional data splitting…
![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.