Healthtech & tech frontiers

 Here’s a summarized list of the main factors that influence hallucinations in large language models (LLMs): 1. Training Data Quality Accurate, Curated Data : High-quality, fact-checked training data reduces hallucinations by ensuring the model has reliable information to work with. Diverse Sources : A broad range of reputable sources helps the model make informed decisions and avoid speculative answers. 2. Model Fine-Tuning Domain-Specific Training : Tailoring models for specific industries or tasks (e.g., medical, legal) reduces hallucinations in those areas. Reinforcement Learning with Human Feedback (RLHF) : Human feedback helps the model improve its responses, making them more grounded in reality. 3. Prompt Design Clear and Detailed Prompts : Specific prompts provide more context, helping guide the model toward accurate answers. Using Constraints : Directing the model to verify facts or base responses on certain conditions reduces speculative outputs. 4. Algori...

Key Parameters for AI Output Control Temperature (Randomness) Definition : Governs how exploratory the model is in generating text. Range : Low (e.g., 0.1) : Prioritizes high-probability responses for deterministic outputs (useful for technical or factual tasks). High (e.g., 1.5) : Allows for more creative and diverse outputs by considering lower-probability options. Implication : Balances between deterministic (precise) and stochastic (creative) responses. Top-k Sampling Definition : Limits the model to only consider the top k highest-probability tokens. Range : Low k (e.g., 5) : Reduces diversity for focused responses. High k (e.g., 50) : Increases diversity but may risk incoherence. Top-p (Nucleus) Sampling Definition : Considers tokens from a probability distribution until their cumulative sum exceeds a threshold p . Range : Low p (e.g., 0.1) : Restricts to highly probable options, ensuring coherent and focused outputs. High p (e.g., 0.9) : Expands ...

 The Risk Adjustment Factor (RAF) score is a numerical value used in healthcare coding and reimbursement systems to reflect the predicted health care costs for patients, primarily in Medicare Advantage and other risk-adjusted payment models. It is associated with the Hierarchical Condition Categories (HCC) coding system. Key Points about RAF Score in HCC Coding: Purpose : Used to predict the expected costs of care for individuals based on their health status and demographic factors. Helps ensure that plans are adequately compensated for patients with higher disease burdens. Calculation : The RAF score is calculated using: HCC codes : Diagnoses that are categorized into specific condition groups. Demographics : Age, gender, and other factors. Interactions : Certain conditions and demographic factors can interact, leading to adjusted risk scores. Each HCC has a specific weight, contributing to the overall RAF score. Range : The RAF score typically ranges fr...