Master’s Degree in Data Analytics and Artificial Intelligence in Health Sciences (DAIHS)

The 2-year course specifically focuses on the intersection between data science, medicine, and life sciences, aiming to train a professional with advanced knowledge in data analytics, machine learning, artificial intelligence and with all the knowledge necessary to apply these advanced skills within hospitals, research laboratories, companies and regulatory bodies.

DAIHS course will be conducted entirely in English and will offer 50 places for national and international students. The course stems from the large medical, biological and healthcare experience in medicine provided by Humanitas University and its joint Hospital networks, including flagship hospital Humanitas Research Hospital, and the large experience in AI, data science and data analytics from Bocconi University.

Rationale

New technologies based on Artificial Intelligence and Machine Learning are transforming the field of medicine and life sciences. When considering the latest advancements in information technology applied to health sciences, such as  the development in -omics data, imaging, electronic health records, and digitalized biology application, we are surprised by the massive volume of data these new techniques generate every day. However, these data mines are often underutilized due to the challenges associated with their analysis and application, leading to the emerging need for an expert who can bridge the gap between data science and health sciences. To address these unmeet demands from hospitals, patients, research and companies, Humanitas University and Bocconi University joined to develop DAIHS program, with the ultimate goal of improving patient care and quality of life, advancing scientific research, and harnessing the value of data within health sciences.

The Program

The study plan for DAIHS aims to develop a cultural and professional profile able to directly contributing to the improvement of both patients’ lives and healthcare organisations.

To reach this objective, the study plan has been structured to merge scientific disciplinary fields from the LM Data Science degree class, with scientific disciplinary fields from the medical-biological field. This will allow to combine deep training in advanced programming, statistics, Machine Learning, and Artificial Intelligence with solid knowledge of biology, genetics, ethics and specific regulation within the healthcare sector.

The course is structured over two years, and will be held entirely in English. International lecturers and experts with strong professional experience abroad are part of the faculty of the DAIHS course.

Students will also have the opportunity to learn in international experiences as part of the development of the dissertation.

The 1st year is mainly focused on providing the necessary knowledge in advanced programming, statistics, Machine Learning and Artificial Intelligence, and due to these characteristics is mainly based in Bocconi University. The 2nd year takes place at Humanitas University to immerge students into the reality of a large teaching hospital, working on biological and clinical data.

Teaching and Assessment

The didactic organisation of the course will enable the student to develop the essential autonomy in decision-making, and the critical thinking skills, through not only of face-to-face lectures, but also extensive use of applied laboratories and seminars. This will enable the acquisition of skills and knowledge required to collect, handle, analyse and communicate, improving the determination of autonomous judgment.

The 1st year of the course is divided into four bimesters, articulated as follow:

• The 1st bimester will propose two foundation course in Advanced Statistics for the Life Sciences, and Advanced Programming in Life Sciences course.
• The 2nd bimester focuses on the practical and applied side of health care through the Artificial Intelligence for Life and Health Sciences course (module 1) and trough the Machine Learning course
• The 3rd bimester builds upon the core knowledge acquired in the first two bimester. In the course Artificial Intelligence for Health Care (module 2), students further explore how AI can revolutionize the way health care is delivered by deepening its practical applications. The course Data Systems in Healthcare enables students to understand the design, implementation and management of data systems in healthcare environments, with specific knowledge on the regulatory processes, privacy problem and guideline while handling health and biological data. During this bimester the student will have also the opportunity to choose among different optional courses, including the topics of computer modelling, causal inference, and natural language processing.
• The 4th bimester shifts the focus toward biological and clinical applications, and is consequently based at Humanitas University. It contains the Biology and Genetics course, the Clinical Epidemiology & Data Science course, and the Data science for Clinics.

Following this approach, the 2nd year takes place at Humanitas University to immerge students into the reality of a large teaching hospital, working on biological and clinical data. It is divided in two semesters, and structured to provide an immersive, hands-on learning experience through the delivery of compulsory integrated teaching, elective exams, seminars, hands-on experiences and independent research.

• The 1st semester focuses on the application of advanced technologies in clinical and biological sciences. The course next-generation sequencing will teach the technique that has revolutionised genetics and molecular biology. In the course Application of Artificial Intelligence in Clinical Research students complete their training by learning about AI applications for improving the quality and efficiency of patient care.

Learning Outcomes

The main learning outcomes of this core curriculum will be to provide a professional autonomous and highly knowledgeable in these areas:

• Analysis and navigation of large databases: exploratory statistics, architecture and programming for exploration of databases, including big data and data lakes;
• Statistics: statistical inference, statistical modeling, statistical learning, artificial intelligence and machine learning methods
• Programming: knowledge of programming and algorithms, including python language and SQL.
• A thorough background in the area of artificial intelligence techniques applied to predictive models and diagnostic models, as well as new areas such as computer vision and natural language processing
• Thorough understanding of the fundamentals of biological-health area, with a focus on biological disciplines (biology, genetics) and medicine (human anatomy, physiology and pathology, aspects of clinical medicine, diagnostic imaging and radiology).
• Epidemiology: knowledge of health epidemiological in order to understand disease patterns, clinical data analysis, and the influence of AI and machine learning solutions on public health
• Healthcare system organization: how health care organizations operate, specific regulations in healthcare sector
• Knowledge of legal issues related to the management of sensitive data in order to understand the limits and conditions imposed by current regulations.
• Problem solving skills combined with analytical skills

What’s Next?

We will create a comprehensive professional who is ready to be employed by health technology companies, hospitals and healthcare organization, as well as research institution, pharmaceutical and biotechnology industries, health analytics companies, and regulatory bodies.

Graduates are expected to possess those analytical skills which are valuable in health sciences. They will master data analytical skills combined with an understanding of the peculiarities of healthcare data, of the national healthcare systems and the ethical and regulatory implication of collaboration between academia and industry.

Here we report some of the answer yielded by companies, when consulted on DAIHS degree and whether the DAIHS graduates would be able to meet the needs expressed by their industry sector or by society.

“This profile could be relevant and effective in strategic consulting, especially if the company or organization for which the process is being conducted is involved in health care or related fields. The acquired skills can be critical in analyzing business data, identifying trends, making predictions, and making informed strategic decisions. The use of artificial intelligence and data analytics can lead to innovative solutions in business processes and healthcare.”

Bain & Company

“Healthcare is a strong growth area at our company in the near future. A training course based on DA & AI can certainly build skills and mindsets that can add value in this area.”

Capgemini

“This is a rapidly expanding field in which there is a need for figures with a cross-disciplinary background, and deep AI skills applied to the various fields of life sciences and healthcare.”

FT Pharma company

“Science is one of the industry where AI and GenAI will be applied most, due to the mass research related, modelling use-cases .”

Roland Berger

“I consider it an excellent foundation for training the new professionals who will soon be required in the complex healthcare market that our company serves. In particular, the ability to use advanced tools such as artificial intelligence will be fundamentally helpful for the development of new solutions in healthcare in a faster and more cost-effective manner, thanks to the ability to aggregate and analyze the enormous amount of data generated in the context of the services and treatments offered to patients.”

Thermo fisher scientific

Fees and Scholarships

Requirements

Entry requirements

• First level degree: “Laurea Triennale” or foreign equivalent (level 6 EQF)

Curricular requirements

• At least 30 cpu in a set of specific disciplinary fields (SSD) of the following areas:
  Statistics (SECS-S/), mathematics (MAT/), computer science (INF/), computer engineering (ING-INF/), physics (FIS/) + biology (BIO/), medicine (MED/)

• of which, at least 18 cpu in the subset comprising the following disciplinary areas: statistics (SECS-S/), medical statistics (MED/), mathematics (MAT/), computer science computer science (INF/), computer engineering (ING-INF/), physics (FIS/)

English language proficiency equal to at least B2 level (post-intermediate; CEFR European Common Framework for Languages).

Adequate preparation evaluated by the Admission Committee (HU+BU joint committee) on previous academic performance (GPA weighted on CPU and CPU gained) integrated by a careful evaluation of the overall student profile.

Students are then ranked and they are admitted/non admitted according to their ranking position and the available slots.

Apply

The online application procedure consists of the following steps:

1. Registration

Register to the Humanitas University Registration Portal: candidates must register to the web portal by entering their name, surname and e-mail address.

After receiving the first e-mail of the Microsoft authentication process, it is necessary to click on Accept Invitation and complete the registration by requesting the single-use access code.

N.B.: candidates have to request a single-use access code for each access.

2. Application

Candidates must log in, enter the required data in the Personal Details section, click on the menu item Apply and select the Master of Science in Data Analytics and Artificial Intelligence in Health Sciences Program.

3. Conclusion and Payment

Upload of the academic documentation required for evaluation and complete the payment of the application fee.

The application fee is € 50.

The registration procedure is completed once the application fee is paid, and it is not refundable under any circumstances.

Admission

Students are ranked by the Admission Committee (HU+BU joint committee) based on their previous academic performance (GPA weighted on CPU and CPU gained), integrated by a careful evaluation of the overall student profile. At the end of each round, the Committee draws up an admission list which determines if a candidate is admitted/non-admitted.

Candidates may visualise their admission status (admitted/not admitted) through the University web portal MyPORTAL, by accessing the reserved area and clicking on the menu item “Admission tests” from the “Student Office” section.

All admitted candidates receive an email from info@hunimed.eu featuring the enrolment procedure and deadline.