Ai Health Informatics Internship | NTHRYS | Research Oriented

Implement data engineering models with ablations, error analysis, and clear reporting for stakeholders for validation

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Khandwa (East Nimari), Ai Health Informatics Internship at Lucknow, Ai Health Informatics Internship at Bhurhanpur, Ai Health Informatics Internship at Maharajganj, Ai Health Informatics Internship at Barwani, Ai Health Informatics Internship at Mahoba, Ai Health Informatics Internship at Alirajpur, Ai Health Informatics Internship at Mainpuri, Ai Health Informatics Internship at Neemuch, Ai Health Informatics Internship at Mathura, Ai Health Informatics Internship at Mau, Ai Health Informatics Internship at Meerut, Ai Health Informatics Internship at Mirzapur, Ai Health Informatics Internship at Moradabad, Ai Health Informatics Internship at Muzaffarnagar, Ai Health Informatics Internship at Pilibhit, Ai Health Informatics Internship at Pratapgarh, Ai Health Informatics Internship at RaeBareli, Ai Health Informatics Internship at Rampur, Ai Health Informatics Internship at Saharanpur, Ai Health Informatics Internship at Sant kabir, Ai Health Informatics Internship at Sant Ravidas, Ai Health Informatics Internship at Shahjahanpur, Ai Health Informatics Internship at Shravasti, Ai Health Informatics Internship at Siddharthnagar, Ai Health Informatics Internship at Sonbhadra, Ai Health Informatics Internship at Sultanpur, Ai Health Informatics Internship at Unnao, Ai Health Informatics Internship at Varanasi

Ai Health Informatics Internships at NTHRYS

During internship in AI Health Informatics, use AI on FHIR resources, claims data, and registries to support interoperability analytics and quality measures and build reproducible pipelines. Implement data engineering models with ablations, error analysis, and clear reporting for stakeholders for validation. Ship a mini project report plus repo, model card, and data notes that reviewers can rerun end to end for publications

Focused Areas

Choose any one

Foundations of clinical data representation learning in longitudinal EHRs
Mathematical properties of temporal sparsity in healthcare event sequences
Bias–variance tradeoffs in risk prediction under missing-not-at-random mechanisms
Identifiability conditions for causal effects from observational EHR data
Formal semantics of clinical concepts across coding systems and ontologies
Representation theory for patient similarity in heterogeneous clinical spaces
Calibration theory for probabilistic models under population shift in medicine
Uncertainty quantification frameworks for clinical machine learning decisions
Sample complexity of phenotyping from weakly labeled clinical text
Generalization bounds for multi-site healthcare prediction models
Information-theoretic limits of de-identification in structured health data
Foundations of privacy-utility tradeoffs for health analytics dashboards
Robust statistics for outlier detection in physiologic time series
Foundational methods for learning with irregularly sampled clinical measurements
Latent variable models for disease progression and hidden clinical states
Graphical models for comorbidity structure discovery in population data
Kernel methods for clinical trajectory comparison and alignment
Contrastive learning objectives for multi-modal patient representations
Self-supervised learning theory for EHR sequences and clinical notes
Foundational evaluation metrics for early warning systems in hospitals
Decision-theoretic modeling of clinical alerting and action thresholds
Optimal stopping theory for monitoring patient deterioration
Foundations of counterfactual explanations in clinical risk models
Causal interpretability: linking mechanistic hypotheses to ML explanations
Fairness definitions and impossibility results in healthcare prediction
Normative frameworks for health equity in algorithmic decision support
Measurement error models for lab values and device-derived vitals
Foundations of data quality: completeness, plausibility, and conformance in EHRs
Ontology-driven feature construction for clinical learning systems
Formal verification of guideline-based decision support rules
Logic programming approaches to computable clinical phenotypes
Foundations of temporal abstraction for clinical events and care episodes
Probabilistic record linkage theory for master patient indexing
Graph-based entity resolution in healthcare: theory and guarantees
Statistical learning under changing coding practices and concept drift
Modeling ICD/SNOMED mapping uncertainty and downstream impact
Fundamental limits of cohort selection with noisy inclusion criteria
Learning to rank patient cohorts: theoretical properties and consistency
Foundations of clinical NLP: section segmentation and discourse structure
Semantic parsing of clinical narratives into structured representations
Negation, uncertainty, and temporality: formal models for clinical language
Domain adaptation theory for clinical language models
Grounded clinical language modeling with knowledge bases and ontologies
Symbolic–neural integration for clinical reasoning: foundational architectures
Knowledge graph completion theory for biomedical and clinical graphs
Logical consistency constraints for patient knowledge graphs
Time-aware knowledge graphs for clinical histories: representation and inference
Foundations of multi-task learning across diagnoses, outcomes, and interventions
Transfer learning limits between specialties and care settings
Meta-learning for personalized clinical prediction: theoretical analysis
Continual learning theory for evolving hospital data streams
Catastrophic forgetting mitigation in longitudinal healthcare modeling
Mechanisms of dataset shift in healthcare and formal detection tests
Covariate, label, and concept shift: taxonomy and diagnostics for EHR data
Robust optimization for worst-case clinical model performance
Distributionally robust learning for patient risk prediction
Adversarial robustness in clinical ML: threat models and guarantees
Reliability diagrams and calibration curves: foundations and pitfalls in medicine
Proper scoring rules for clinical probability estimation
Decision-curve analysis: theoretical underpinnings and extensions
Utility-sensitive learning for clinical outcomes and interventions
Foundations of cost-sensitive classification for imbalanced medical events
Rare event modeling in ICU data: asymptotics and estimators
Survival analysis fundamentals for ML with censoring in healthcare
Competing risks models for multi-outcome clinical trajectories
Joint modeling of longitudinal biomarkers and time-to-event outcomes
State-space models for hidden physiologic dynamics
Gaussian processes for irregular clinical time series: theory and inference
Neural ODEs for physiologic modeling: identifiability and stability
Dynamic treatment regimes: foundational methods and assumptions
Reinforcement learning for clinical decision-making: theoretical safety constraints
Off-policy evaluation in healthcare: identifiability and variance control
Safe exploration principles for data-driven clinical policies
Constrained RL for clinical workflows: Lagrangian and primal-dual foundations
Bandit theory for adaptive clinical alerts and triage systems
Causal bandits for treatment selection with confounding
Instrumental variables in health informatics: assumptions, tests, and limits
Regression discontinuity designs in clinical operations research
Natural experiments in EHR data: formal criteria and sensitivity
Sensitivity analysis frameworks for unmeasured confounding in EHR studies
E-value and bounding approaches: theoretical comparisons and extensions
Target trial emulation: foundational design principles and identifiability
Transportability and external validity across hospitals: formal conditions
Causal discovery in healthcare: assumptions, identifiability, and algorithms
Structure learning for causal graphs with mixed data types in EHRs
Mechanism-based causal models linking physiology and clinical records
Foundations of mediation analysis for complex care pathways
Path-specific effects in clinical process models
Process mining foundations for care pathways and conformance analysis
Temporal point processes for healthcare events: theory and estimation
Hawkes processes for contagion-like clinical event modeling
Event prediction with marked point processes for labs and medications
Sequence alignment theory for heterogeneous clinical event streams
Foundations of anomaly detection for patient safety surveillance
Change-point detection in clinical monitoring streams
Early detection theory: tradeoffs between timeliness and false alarms
Signal detection theory applied to clinical decision support
Statistical hypothesis testing for model drift and recalibration triggers
Federated learning foundations for multi-institutional health systems
Secure aggregation protocols: formal guarantees for clinical federated learning
Differential privacy in healthcare analytics: mechanisms and composition
Privacy accounting for repeated cohort queries in clinical data marts
Synthetic health data generation: fidelity, privacy, and theoretical limits
Membership inference risks in clinical language models: formal analysis
Attribute inference attacks on EHR embeddings: threat modeling and defenses
Cryptographic foundations for secure multi-party computation in health research
Homomorphic encryption for clinical prediction: complexity and feasibility theory
Access control models for health data: formal policy languages
Audit log anomaly detection: foundational statistical models
Data lineage and provenance in health informatics: formal frameworks
Reproducibility theory for observational health analytics pipelines
Benchmark design for clinical ML: sampling, leakage, and validity
Dataset curation principles for EHR research: formal definitions of leakage
Temporal validation strategies: theory of forward-chaining evaluation
External validation metrics and confidence intervals for clinical models
Uncertainty in ground truth labels: latent label models for clinical endpoints
Weak supervision for healthcare: theory of noisy labeling functions
Learning from imperfect labels in ICD-coded outcomes
Foundations of multi-label classification for comorbidity prediction
Hierarchical classification using ICD taxonomies: consistency and calibration
Multi-resolution modeling of health data: visits, episodes, and lifecourse
Functional data analysis for physiologic curves in ICU research
Topological data analysis in patient trajectories: theory and applications
Manifold learning for high-dimensional clinical phenotypes
Clustering validity in healthcare cohorts: theoretical criteria
Stability of unsupervised phenotypes under sampling and preprocessing
Identifiability of latent subtypes in mixture models of disease
Bayesian nonparametrics for discovering clinical subpopulations
Foundations of hierarchical Bayesian models for multi-site healthcare data
Empirical Bayes shrinkage for small-hospital performance estimation
Multi-level modeling of provider effects and patient outcomes
Causal attribution of provider performance under selection bias
Measurement of clinician workload from EHR logs: foundational constructs
HCI theory for clinical decision support: cognitive load and trust
Human-in-the-loop learning for medicine: convergence and reliability
Active learning for chart review: sample complexity and stopping rules
Inter-rater reliability modeling for clinical annotation studies
Gold standard creation: adjudication models and uncertainty propagation
Explainability vs interpretability: formal definitions in clinical AI
Counterfactual reasoning in triage: axioms and desiderata
Algorithmic recourse in healthcare: feasibility and ethical constraints
Ethical frameworks for clinical ML: principles, conflicts, and formalization
Risk of harm modeling for decision support: foundational safety cases
Clinical safety evaluation of ML: formal hazard analysis methods
Fail-safe design patterns for clinical AI systems
Abstention and deferral learning: theory for clinician escalation
Selective prediction with coverage guarantees in health settings
Conformal prediction for clinical risk scores: validity under shift
Prediction intervals for survival models: foundational approaches
Quantile regression for clinical outcomes: asymptotic properties
Missing data theory for EHR: MNAR models and identification
Multiple imputation in high-dimensional clinical data: theory and practice
Inverse probability weighting for informative missingness in labs
Measurement-driven missingness as signal: foundational modeling strategies
Learning with delayed outcomes in healthcare operations
Temporal feedback loops between predictions and care: formal analysis
Performative prediction in healthcare: equilibrium and instability
Algorithm-induced behavior change in clinicians: modeling and measurement
Causal feedback and policy evaluation with deployed clinical models
Foundations of clinical simulation environments for policy testing
Digital twins in health informatics: theoretical requirements and validation
Mechanistic-ML hybrid models for physiology: identifiability and robustness
Scaling laws for clinical language models: data, compute, and generalization
Prompting vs fine-tuning in clinical NLP: theoretical comparisons
Evaluation theory for summarization of clinical records
Faithfulness metrics for clinical summaries: formalization and testing
Hallucination detection in clinical LLM outputs: foundational benchmarks
Knowledge-grounding guarantees for clinical question answering systems
Retrieval-augmented generation in clinical domains: theory of error reduction
Formal constraints for medication dosing recommendations in AI systems
Mathematical modeling of lab reference ranges across populations
Foundations of risk stratification in population health analytics
Stratified sampling and weighting for representative health studies
Linkage bias in claims–EHR integration: theory and corrections
Probabilistic fusion of multi-source health data: identifiability
Record-level uncertainty propagation through analytics pipelines
Foundations of geospatial health informatics: measurement and inference
Spatial autocorrelation effects on health outcome modeling
Spatio-temporal models for disease surveillance: identifiability and power
Graph signal processing on patient–provider networks: foundational methods
Network causal inference for contagion and peer effects in health
Privacy in network health data: theoretical limits and anonymization
Federated analytics vs federated learning: foundational distinctions
Statistical disclosure control for aggregate health reporting
Foundations of clinical guideline knowledge representation (CQL/Arden)
Temporal logic for clinical protocols and pathway verification
Formal methods for interoperability testing of FHIR implementations
Ontological reasoning for computable phenotypes: description logic foundations
Semantic interoperability: formal models of meaning preservation
Concept mapping drift detection: theory and statistical tests
Longitudinal fairness: definitions for evolving patient risk over time
Fairness under censoring and competing risks: foundational metrics
Calibration within groups: theory and paradoxes in healthcare
Intersectional evaluation of clinical models: statistical power and design
Uncertainty-aware triage: decision theory under resource constraints
Queueing theory foundations for patient flow and predictive scheduling
Fundamental limits of ED crowding prediction from partial observability
Markov decision processes for bed management: theoretical formulations
Optimization under uncertainty for staffing in hospitals: robust frameworks
Foundations of multi-objective optimization in clinical operations and outcomes

Note: Interns get complete handson exposure to all steps in the allocated methodology.
For more details, please visit: NTHRYS Internship Program Structure

Application Form

Name

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(Our Team will initiate whatsapp contact after this form submission.)

Field of Interest:

Mode of Internship:

Duration:

Internship Type:

Yes, all the above info is accurate

Duration	Academic Mode	Technical Mode	Research Mode
Ai Health Informatics Internship Fee Structures
5 Days	Rs 13750	Rs 22000	Rs 33000
10 Days	Rs 16500	Rs 24750	Rs 35750
15 Days	Rs 18150	Rs 26400	Rs 44000
20 Days	Rs 24750	Rs 33000	Rs 55000
30 Days	Rs 27500	Rs 38500	Rs 71500
45 Days	Rs 33000	Rs 44000	Rs 82500
2 Months	Rs 38500	Rs 49500	Rs 99000
3 Months	Rs 44000	Rs 82500	Rs 126500
4 Months	Rs 66000	Rs 104500	Rs 154000
5 Months	Rs 82500	Rs 115500	Rs 181500
6 Months	Rs 99000	Rs 132000	Rs 198000
7 Months	Rs 104500	Rs 148500	Rs 236500
8 Months	Rs 115500	Rs 165000	Rs 264000
9 Months	Rs 132000	Rs 192500	Rs 302500
10 Months	Rs 159500	Rs 220000	Rs 357500
11 Months	Rs 176000	Rs 247500	Rs 412500
1 Year	Rs 209000	Rs 275000	Rs 522500
18% additional GST on all fee structures.
Installment options are available for all durations.
NTHRYS Students

Deliverables	Academic Mode	Technical Mode	Research Mode
Certification	✓	✓	✓
Hands-On Practical Exposure	✓	✓	✓
Thesis	✓	✘	✓
PowerPoint Assistance	✓	✓	✓
Protocol Repetitions	✘	✓	✓
Publication Coauthorships	✘	✘	3 Months Duration onwards
References	✘	✓	✓
Recommendations	✘	✘	✓
Experience Letters	✘	✘	6 Months Duration Onwards
Placement Assistance	✘	✘	6 Months Duration Onwards
Placement Guarantee	✘	✘	1 Year Duration

Modes Available: Offline and Online / Virtual

Offline Locations

Offline Timetable

^{3 - 5 Hours per day | 4 Days Practicals / week | 1 Day Reporting}

Weekly Schedule

Tuesday to Friday: Practical Sessions
Saturday: Documentation Day
Sunday & Monday: Weekend Holidays

Lab Working Hours

9:30 AM to 5:30 PM

Students/Scholars are assigned 3 to 5 hours/day based on their protocols.

Online Mode Workflow

2 to 4 hours per day as per discussed with NTHRYS Management before booking the slot.

All activities (Online / Virtual and Offline) are managed through the NTHRYS Project Dashboard System — a web portal designed exclusively to facilitate, guide, and track your progress throughout each phase.

Phase 1: Topic / Title Finalization

Guided assistance in selecting and refining your research topic or project title within the dashboard interface.

Phase 2: Research Methodology Finalization

Step-by-step guidance in defining objectives, research questions, and methodology using interactive templates and mentor feedback on the dashboard.

Phase 3: Software, Tools, and Statistical Approaches

Installation assistance, tool demonstrations, and access to recommended software provided via the dashboard with mentor instructions and documentation.

Phase 4: Task Execution

Students/scholars execute research tasks on their own systems while mentors monitor and guide progress through the dashboard system.

Phase 5: Results Analysis

Data analysis, results review, and interactive discussions are facilitated through dedicated dashboard modules ensuring research integrity.

Phase 6: Documentation

Proper documentation of results, methodology, and conclusions using dashboard templates, ensuring consistency and completeness.

No Video Calls, No Theory Classes: All learning is task-based, with hands-on execution by students/scholars under the guidance of mentors using the NTHRYS Project Dashboard System.

Click Here to know schedule, offline locations, calendar, modes of operation etc.,

Important Note

Note 1: Candidates may select any one of the focused areas listed for their internship.
Note 2: Fundamental concepts are provided as guidance for candidates who require them; however, candidates may choose to bypass these sections if desired.
Note 3: All candidates will gain practical, hands-on experience with every step outlined in the provided methodology.
Note 4: Comprehensive placement assistance and career guidance will be available to all candidates during and after the internship.
Note 5: We understand that many students may lack basic practical exposure due to shortcomings in their college education. This is not the fault of the students but rather a failure of the institutions and their staff. At NTHRYS, our staff excel at training every student from the ground up, ensuring they gain the necessary skills and experience.