A Patient-Centered Text-Derived Neural Network Paradigm for Diagnosis of Schizophrenia
Bibliographic record
Abstract
A text-derived neural network for diagnosing Schizophrenia is illustrated in this paper.Schizophrenia is a continuous mental condition that affects the job performance, social relationship, and livelihood of individuals.Using DSM-V criterion for schizophrenia diagnosis, we collected data from medical records of 1205 patients in psychiatric hospitals (57% Schizophrenia and 43% Related Illnesses) and developed a neural network model.In order for the developed model to categorize the test data into classes, significant features from the acquired dataset were fed into it to identify indicators in the training data.The model diagnosed schizophrenia with 90% accuracy, 92% specificity, 84% precision and Area under the Receiver Operating Characteristic (ROC) curve of 0.97.These results are promising for schizophrenia diagnosis in the near future.The text-derived ANN developed is more accurate and faster computationally and can be used to generalize in the case of new data when compared to image-based classification.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".