An Expert System for Health Diagnosis Based on Natural Language Processing and Reasoning Engine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The paper discusses expert systems in the field of digital health, which provide services for health diagnosis. An expert system is an artificial intelligence technology that simulates a doctor's diagnostic process and provides diagnostic and treatment recommendations. The paper describes the techniques and methods related to expert systems, mainly including natural language processing and inference engines. Natural language processing is used to process and understand the natural language input provided by the patient, extract key information, and convert it into a machine-understandable form. The inference engine uses the medical knowledge base and rules to perform logical reasoning and inference to generate diagnostic results and treatment recommendations. The paper also describes related experiments and evaluations, as well as ethical issues and challenges. The aim is to explore the application and development of AI in expert systems for health diagnosis to inform and inspire the field of digital health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it