Study-unit DATA SCIENCE FOR HEALTH SYSTEMS
| Course name | Computer engineering and robotics |
|---|---|
| Study-unit Code | 70A00039 |
| Curriculum | Data science e data engineering |
| Lecturer | Alessio De Angelis |
| Lecturers |
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| Hours |
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| CFU | 6 |
| Course Regulation | Coorte 2023 |
| Supplied | 2024/25 |
| Learning activities | Affine/integrativa |
| Area | Attività formative affini o integrative |
| Sector | ING-INF/07 |
| Type of study-unit | Opzionale (Optional) |
| Type of learning activities | Attività formativa monodisciplinare |
| Language of instruction | Italian |
| Contents | The goal of the course is to provide tools for addressing data science problems in the biomedical field. The first part of the course deals with the origin of data, with the study of measurement theory and the main classes of biomedical measurements. Particular focus is given to bioimages. The second part of the course is dedicated to the fundamental statistical techniques relevant for the biomedical field. To develop practical skills, an important portion of the course is comprised of computer exercises using software tools that are widely applied for statistical analysis of biomedical data. The main concepts are presented through the use of example datasets. |
| Reference texts | Course material provided by the instructor. |
| Educational objectives | - understand the basis of statistical and measurement methods used in the biomedical field - understand the main problems in some of the application areas of biomedical data science - develop skills in using the main software tools used in biomedical data science - acquire the terminology of this sector |
| Prerequisites | Probability theory |
| Teaching methods | Classroom lectures. Computer exercises. |
| Other information | Lecturer: alessio.deangelis@unipg.it |
| Learning verification modality | - written exam comprised of multiple-choice questions and open questions - project work, survey or experimental type, performed by 1 to 4 people. Brief report and presentation - Optional oral test |
| Extended program | 1. Origin of data: measurements. Measurement theory and uncertainty. Monte Carlo method for uncertainty assessment. 2. Biomedical data. Biomedical measurement systems and sensors. Characteristics and performance parameters. 3. Bioimaging. Bioimaging instrumentation: computed axial tomography, operating principle and reconstruction algorithm, Radon transform and anti-transform, ultrasound echography, positron emission tomography, magnetic resonance imaging. Processing methods and standards for bioimaging. 4. Medical informatics. Definitions, application context, medical data. Coding and classification of medical information: international classification of diseases (ICD), diagnosis related groups (DRGs), HL7 standards. 5. Biomedical data analysis and biostatistics. Statistical studies, meta-analysis, hypothesis testing, inference in the biomedical field. Statistical tests: goodness of fit, parametric, Student's t, Pearson's chi square, nonparametric (of ranks, for ordinal measurement scales), ANOVA, post-hoc analysis, Bonferroni and Benjamini-Hochberg corrections, ROC, ANCOVA, GLM. Classroom exercises with R language and RStudio environment: main commands and syntax of R, loading and managing biomedical datasets, exploratory data analysis (EDA), graphical tools, summary statistics, statistical tests. |