AI is exploding across industries, including pharmaceuticals and life sciences, but many companies are deploying models that don’t realize their full potential.
Poor data preparation is often the cause of disappointing results in AI projects. This article details the key stages of preparing your pharmaceutical data for successful AI integration.
1. Data Structure Analysis
Ensure your data is consistent, well-organized, and ready for AI algorithms.
1.1 Data Consistency and Completeness
Build trust in your data by identifying and fixing issues.
- A value is missing. This will be addressed using imputation methods such as means for continuous variables and LOCF for longitudinal studies.
- There is a duplicate. Duplicates are identified based on a unique identifier and cleaned up by merging them.
- error. Data validation is managed through rules that enforce data type restrictions and expected value ranges.
1.2 Normalization
Assess data structure, type, format, and redundancy:
- Data type. Aligns with common pharmaceutical datasets to ensure consistent date formats and standardized units of measurement.
- Redundancy. Data normalization techniques are used to minimize and streamline the structure while maintaining integrity.
1.3 Analyzing data relationships between tables
Understanding data connections between databases:
- Human relationships. Identify the primary and foreign keys that link data points between tables.
- Entity Relationship Diagram (ERD). Visualize the connections between patients, drugs, diagnoses, and outcomes.
1.4 Adherence to predefined standards
Create a uniform naming convention and schema design:
- Standardized naming. Implement a controlled vocabulary for drug names, diagnoses, and procedures.
- Data dictionary. Define data elements with types, allowed values, and units specific to pharmaceutical research.
1.5 Defining Schemas for Reporting Usage
Designing data structures for both AI analysis and report generation:
- Descriptive naming. Use clear column names that reflect the meaning of the data.
- Schema comment. Includes table and column descriptions.
- Data lineage. Track data provenance and transformations to ensure data structures remain consistent over time, and any changes that occur must be properly accounted for.
- Schema design for reporting. Use optimized designs such as star and snowflake schemas to create efficient data extractions and informative reports.
2. Accuracy of Data
To get trustworthy AI insights, ensure all training data is accurate.
2.1 Reflecting real-world characteristics
Evaluate whether your data accurately represents real-world objects:
- Characteristics of the drug. Check whether the data captures chemical and biological properties such as molecular structure, solubility, and interactions with proteins.
- Clinical trial data. Ensure data reflects patient demographics, treatment plans, and outcomes.
- EHR data. Accurately records diagnoses, medications, and patient responses.
2.2 Data Normalization
Apply consistent principles and rules to data normalization:
- Standardized units. Ensure consistent units of measurement (e.g., mg/mL for drug concentrations).
- Controlled vocabulary. Maintain consistency in terminology regarding diagnoses, medications, and procedures to avoid misunderstandings.
2.3 Typo in the data
Identifying and correcting typos and data entry errors:
- Important fields. Detect typos in drug names, dosages, and patient identifiers to prevent critical inaccuracies in your models.
- Domain-specific validation. Implement validation rules for drug data to ensure valid dosage ranges and correct anatomical terminology.
2.4 Anomalies in the data
Detect and act on anomalous data points:
- Clinical trial outliers. Investigate outliers in treatment response to determine whether they indicate a biological effect or a data collection error.
- Biochemical outliers. Identify abnormal test results that may indicate errors or rare medical conditions.
2.5 Missing Data
Missing data analysis and management:
- Patterns of missing data. Identify systematic gaps that may indicate broader data collection issues, such as missing patient demographic information.
3. Data uniqueness check
Prevent duplicate data points to avoid inflated sample sizes and misinterpretation of AI-generated insights.
3.1 Identifying duplicates
Steps to identify duplicate data objects:
- The criteria to match. Establish criteria for identifying duplicates, such as patient IDs, compound structures, demographic details, and clinical trial identifiers.
- Fuzzy match. Implement techniques to account for variations in data entry, such as slight differences in the spelling of names or inconsistencies in date formats.
3.2 Duplicate Source Analysis
Investigate the root cause of the duplicate records and focus on the following:
- Data integration issues. Detect issues and standardize processes to prevent duplication that occurs from integrating different databases (e.g., EHRs and clinical trial systems).
- human error. Address data entry errors by implementing validation rules and controlled vocabulary.
3.3 Strategies for handling duplicates
Determine the most appropriate approach for handling duplicates:
- Merging duplicates. Merge duplicates while preserving the relevant data points for each instance.
- Flagging and deleting. Flag duplicates for further investigation, or remove them if you’re unsure of the “correct” record.
- Domain-specific considerations. Tailor duplicate management strategies to specific data types, such as patient demographics or compound structural data.
3.4 Preventing future duplication
This will prevent future duplication.
- Standardized data collection. Use standardized forms and electronic data capture systems to minimize human error.
- Data cleansing routines. Schedule regular data cleansing to identify and address duplicates before they affect your analysis.
4. Data Existence Check
Ensure you have complete data across time, location and user context to avoid biased models and inaccurate outputs.
4.1 Time-based data checks
Ensure that you have complete data points across the entire time period that is relevant to your analysis.
- Clinical trials. Ensure complete data capture across all study phases, including enrollment, dosing, and adverse event reporting.
- EHR data. Ensure there is a comprehensive record of the patient’s medical history, treatment progress, test results, and diagnosis.
- Pharmacovigilance. Ensure that adverse events are reported thoroughly throughout post-marketing surveillance.
4.2 Location-based data checks
Do the same with geography.
- Clinical trial site. Ensure complete capture of patient registration location including country, region and specific location.
- Pharmacovigilance. See where adverse events are occurring and identify geographic trends.
- Supply chain tracking. Assess the completeness of geolocation data for pharmaceutical manufacturing and raw materials.
4.3 User-Based Data Checks
Check whether the data associated with a particular collector or user is comprehensive enough.
- Clinical trial data. Ensure complete data at each study site, including dosing records by study personnel.
- EHR data. Verifying data entered by healthcare professionals, such as a doctor’s diagnosis.
5. Data Augmentation
Combat data shortages that can severely hinder the validity of studies and models.
5.1 Data Augmentation
Manipulate existing data (medical images, EHRs, etc.) to create variation (rotation, noise) to increase the generalizability of your model.
5.2 Synthetic Data Generation
Create entirely new and realistic data points about:
- Rare disease research (diagnosis, treatment)
- Clinical trial design (patient selection)
- Drug discovery (virtual screening)
Important considerations:
- Data quality. Bias in the original data may be amplified.
- verification. Ensure that the data generated reflects reality so that no misleading patterns are introduced.
- Rules. Transparency and documented processes are crucial for regulatory compliance.
Beyond these basic data preparation steps, Data Annotation and Anonymization It plays a very important role. Data Annotation It adds meaning to your data and enables AI models to interpret it effectively. Anonymization We protect patient privacy through techniques such as pseudonymization and data minimization, and we also recommend regular risk assessments and review processes for comprehensive data protection.
Want to learn more about preparing your pharma data for AI? This blog post Here we will explain each preparation step in detail.
