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AKTU AI in Clinical applications (Theory) (BP703T) Syllabus PDF – B.Pharm 7th Semester
AI in Clinical applications (Theory) BP703T
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Official AKTU syllabus of AI in Clinical applications (Theory) (BP703T) for B.Pharm 7th Semester – 2 credits, L-T-P 2-0-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — AI in Clinical applications (Theory)
Download PDFBP703T · 2 credits · L-T-P 2-0-0
Course Objectives
- Introduce AI applications in pharmacology, pharmacokinetics, and drug safety.
- Enable students to apply supervised ML models to clinical and pharmacovigilance datasets.
- Develop interpretation skills for predictive modeling in healthcare.
- Build understanding of real-world healthcare data analytics.
- Promote responsible and ethical AI usage in patient care.
Course Outcomes
| CO | Upon successful completion of this course, the students will be able to: |
| No. | |
| 1 | Explain the applications of AI & ML in pharmacokinetics, pharmacodynamics, and clinical decision-making. |
| 2 | Apply regression and classification models to analyze clinical, pharmacokinetic, and pharmacovigilance datasets. |
| 3 | Interpret predictive model outputs for adverse drug reactions, therapeutic response, and clinical risk assessment. |
| 4 | Evaluate the performance of machine learning models using healthcare metrics such as accuracy, sensitivity, specificity, precision, recall, and RMSE. |
| 5 | Analyze real-world healthcare datasets and assess the ethical and practical implications of AI-based clinical decision support systems. |
| Unit No. | Topics Lectures | No. of |
| I | AI in Pharmacokinetics & Dose Optimization Review of pharmacokinetic parameters (Cmax, Tmax, AUC, clearance) Modeling concentration-time relationships: Linear regression in PK data modeling, multiple regression for dose adjustment based on patient variables (age, weight, renal function) Interpretation of regression coefficients in clinical context Error metrics (RMSE, R²) in PK modeling Limitations of linear modeling in nonlinear pharmacokinetics | 6 Hours |
| II | AI in Drug Safety & Pharmacovigilance Overview of pharmacovigilance systems Structure and data formats from real systems (e.g. FAERS, EudraVigilance) Conceptual overview of frequency analysis and signal detection Logistic regression for ADR risk prediction Confusion matrix and clinical performance metrics Sensitivity, specificity, precision, recall Bias and confounding in observational datasets | 6 Hours |
| III | AI in Personalized Medicine & Risk Stratification Concept of precision medicine Patient covariates and therapeutic response Logistic regression for disease risk prediction Classification of responders vs non-responders Evaluation metrics in healthcare prediction Ethical implications of predictive modeling | 6 Hours |
| IV | AI in Clinical Decision Support & Real-World Data Structure of Electronic Health Records (EHR) AI in Clinical Decision Support Systems (CDSS) Regression models for outcome prediction Classification models for risk scoring Real-world data analytics | 6 hours |
Limitations of AI in clinical environments Accountability and interpretability
| V | Guided Supervised Learning Project Pharmaceutical and Clinical Applications Students will undertake a guided supervised learning project (individually or in groups) applying regression or classification models to pharmaceutical or clinical datasets. The project should include the following steps: Identification and definition of a relevant pharmaceutical or clinical problem Selection of an appropriate publicly available dataset Identification of predictor variables and outcome variables Data preprocessing and preparation for analysis Application of suitable supervised learning methods (e.g., linear regression or logistic regression) Evaluation of model performance using appropriate metrics Interpretation of results in the context of pharmaceutical or clinical relevance Discussion of limitations, potential biases, and ethical considerations Presentation of findings to faculty mentors or peers. Suggested project topics (not limited to): Dissolution rate prediction, tablet hardness prediction, stability degradation modelling, quality control batch failure prediction, assay variability modelling, impurity prediction, moisture impact on formulation stability, coating thickness prediction, solubility enhancement modelling, tablet defect classification, adverse drug reaction (ADR) prediction, therapeutic response prediction using patient datasets, hospital readmission risk prediction, dose requirement prediction using pharmacokinetic data, diabetes risk prediction from health markers, antibiotic treatment success prediction, ICU stay duration prediction, medication adherence analysis, and disease severity classification. | 6 Hours |
Recommended References
- Aggarwal, C. C. and Reddy, C. K. Healthcare Data Analytics. CRC Press.
- Bonate, P. L. Pharmacokinetic Pharmacodynamic Modeling and Simulation. Springer.
- Campbell, M. J., Machin, D. and Walters, S. J. Medical Statistics: A Textbook for the Health Sciences. Wiley-Blackwell.
- Schmidt, S. and Derendorf, H. Applied Pharmacometrics. Springer.
- Steyerberg, E. W. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. Springer.
- Strom, B. L., Kimmel, S. E. and Hennessy, S. Pharmacoepidemiology. Wiley- Blackwell.
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