Medical AI Mastery: GPT Clinical & Genomic Assistant

Split the dataset into 80% training and 20% testing to train on xtrain and validate on xtest, using a random state 42, preventing overfitting and giving a realistic performance estimate.

Make predictions on test data with the trained model and evaluate accuracy score, classification report, and confusion matrix, highlighting malignant versus benign misclassifications and false negatives.

Understand feature importances from a fitted random forest or xgboost, as a nonnegative numpy array summing to one, and visualize them with a matplotlib bar chart.

Use logistic regression with max iter 1000 to fit on xtrain and ytrain, predict test data, and report accuracy, classification report, and confusion matrix for health care compliance.

Evaluate the XGBoost classifier using ROC curves and AUC, comparing its true positive and false positive rates to logistic regression; this visualization highlights model discrimination between benign and malignant tumors.

Create a model comparison summary table, align results side by side, and evaluate accuracy scores on test data for logistic regression, GNN, and XGBoost, displaying results in a dataframe.

Explore cancer classification with the AUC ROC curve to assess diagnostic ability across thresholds. Implement step-by-step using sklearn metrics to compute ROC, AUC, and use predict_proba for probability scores.

Explore logistic regression by analyzing log probs, predict_prob, and x_test, then assess the roc curve and roc auc score on y_test.

Analyze XGBoost with ROC curves and AUC to distinguish benign from malignant tumors, compare against logistic regression, and interpret curves near the top-left versus near the diagonal.

Normalize features such as mean radius and worst concave points to ensure equal contribution, and compare performance for KNN and gradient descent, with logistic regression benefiting from consistent scales.

Apply feature normalization with StandardScaler after creating X and y and performing a train-test split. Transform the test features with the fitted scaler to prepare the X test.

Explore k nearest neighbors with k-fold cross-validation to evaluate performance using accuracy and auc, including skf data splits and kNN model training.

Execute xgboost with k-fold cross-validation to train on scaled features, evaluate with accuracy and AUC from train and test splits, and report mean metrics in the results table.

Learn to use scispaCy to extract medical entities from cancer pathology reports, link terms to UMLS, handle abbreviations and scientific syntax, and prepare structured data for ChatGPT-style simplification and Q&A.

Load the spaCy NLP pipeline, explore transformer-based components, and use the UMLS entity linker to resolve abbreviations and map clinical and genomic terminologies.

Analyze a case report of invasive ductal carcinoma of the breast with contralateral axillary lymph node metastasis, detailing ER-positive, HER2-negative status and BI-RADS five imaging findings.

Extracting and displaying medical entities links invasive ductal carcinoma to a UMLS concept and shows its UI, preferred name, and definition in a Colab notebook.

Convert to structured json for ai use explains json, a lightweight, human readable data interchange format that transmits web data and represents numbers, booleans, strings, arrays, and objects.

Install the OpenAI API, then configure the API key by importing OpenAI, setting OpenAI.api_key to your key or using an environment variable or Colab secrets in notebooks.

Interpret a patient report detailing invasive ductal carcinoma in the upper outer quadrant of the right breast, ER positive, HE2 negative, and axillary lymph node involvement.

Learn to call the OpenAI chat API and construct a chat completion request, including model selection, messages with system and user roles, and temperature settings, in a medical context.

Wrap in function uses optional wrappers to translate pathology reports into language for non doctors, via OpenAI chat completion, and discusses a Flask app with translation and voice accessibility.

Build a cancer Q&A chatbot with Lang Chain that reads pathology reports or medical texts, answers questions with source citations from PubMed, and grounds responses in real documents.

Install python dependencies for medical ai with lang chain and OpenAI, configure the API key in Colab, and run a step-by-step implementation using a sample medical document.

Load and split text for chunking to enable retrieval. Apply unsupervised k-prototypes clustering on TNBC clinical data and random forest to predict os and pfs from biomarker variables.

Embed documents into a vector store with OpenAI embeddings for fast retrieval and semantic search over high-dimensional vectors, using k-means clustering and product quantization to index efficiently.

Create a retrieval qa chain by linking a retriever and vector store with a language model to generate grounded answers from retrieved documents and external knowledge sources, with optional sources.

Explore building a retrieval-augmented question answering pipeline that uses embeddings, a vector database retriever, and an LLM to answer medical queries with grounded, cited results from source documents.