User Personae

Presented here is a series of stakeholder or user descriptions - referred to here as personae, which informed preliminary development of the cancer models.

Translational Scientist with “Dry Bench” Bioinformatics skills

Background

• PhD trained scientist in wide range of fields relevant to cancer (e.g. genetics, pharmacology, molecular biology, immunology)
• Analytically trained and familiar with statistical methods, including genomics/bioinformatics.
• Unfamiliar with Natural Language Processing (NLP) Concepts
• Unfamiliar with NLP tools and resources
• Limited familiarity with OO programming languages
• Familiar with text manipulation languages ( e.g. Python, Perl, Ruby)

Premise/Story

Cancer biologists are unraveling the genomic and molecular changes that drive tumors towards specific behaviors such as progression and metastasis. Identifying these molecular drivers will require information about the specific cancer behaviors that they produce. This class of users will examine data for case finding and to classify cases based on outcome.

Expectations

• Population-level statistics, summarization, and comparisons.
• Graphical displays, including bar charts, error bars, etc.
• Inferential statistics
• Export to statistical software (SAS,SPSS,RapidMiner, R) ###Information needs
• Demographic data
• Treatment data
• Disease progression, metastasis and other outcomes (e.g. RECIST criteria)
• Available biomarkers and other clinical molecular information not in structured format (e.g. Oncotype Scores) ###Current tools and limitations
• Mac desktop, Linux and Windows computing
• Some familiarity with DBMS and data management principles
• Knowledge and use of statistical software (e.g. SAS, SPSS, RapidMiner, R), but time required to extract and format data is substantial.
• Routine access to PHI clinical text for work, able to interpret clinical text reports, but in-depth review is too error-prone and time-consuming.