Lesson 1 University of Bergamo Engineering and Management for Health FOR CHRONIC DISEASES MEDICAL SUPPORT SYSTEMS LESSON 1 Introduction to the course. Perspectives: from the physician making decisions by hand; to the support of mathematical models; to the integration of the models in the machines. Ettore Lanzarone March 11, 2020 References Contacts Ettore Lanzarone No textbook. ettore.lanzarone@unibg.it Slides for the first part of the ettore.lanzarone@cnr.it course “common mathematical approaches applied in medicine www.mi.imati.cnr.it/Ettore/MSS.html and possible issues”. Student reception: Collection of scientific papers for • Wednesday from 13:30 to 14:30 each example examined in the Write an email to fix an appointment course. 1
Lesson 1 Evaluation 1. written exam dealing with all theoretical background and examples discussed in the course, followed by oral exam to discuss the written exam and including other questions 2. presentation of the group project Presentation of the project consists of providing the report and preparing a presentation of 15 minutes. This presentation will be made by the students of the group together. It can be done both before or after the other part. The mark will be registered after both parts are completed. The mark is a weighted average (weight 75% for exam; 25% for project) Introduction Introduction to the course 2
Lesson 1 Introduction The topic of the course is the development of medical support systems, where these systems are intended to be mathematical tools that support the decision making process. How to use mathematical models as decision support tools to make medical decisions? • how to develop a mathematical model to support a medical decision? • how to use patient-specific information to calibrate the model? • how to validate the model? • how to insert the model in an automatic decision making process? Introduction The course is divided into three parts: 1. Introduction to common mathematical approaches applied in medicine and possible issues: • stochastic approaches • differential approaches • optimization approaches 2. Set of examples from real-word medical practice 3. Development of students’ projects 3
Lesson 1 Introduction For each real-word medical: 1. Description of the medical background and problem statement & possible solutions. 2. Practical work: • students will receive a dataset related to the problem under analysis; • students will propose and try an approach to meet the problem objectives. 3. Discussion: • discussion of the solutions proposed by the students; • analysis of one or more solutions available in the literature; • discussion about using the solution for medical decision making. Introduction Considered medical problems and mathematical models: 1) hemodialysis Hemodialysis (HD) is still associated with non-negligible rate of comorbidities. In particular, due to therapy discontinuity, HD induces considerable changes in osmotic balances and rapid variations in fluid volumes and electrolytic concentrations within patients’ body compartments. Treatment customization/optimization is required to reduce the associated comorbidities, because the individual tolerance to HD may vary from patient to patient also in the presence of the same treatment conditions. 4
Lesson 1 Introduction Considered medical problems and mathematical models: 1) hemodialysis Customization requires to simulate and predict the patient-specific response to HD treatment in terms of electrolyte and catabolite kinetics. We will refer to a parametric multi-compartment kinetic model of solute, and we will obtain the customization by means of patient-specific model parameters, which modulate the mass and fluid balance across the main membranes involved in HD process. A patient-specific description of the dynamics will allow to optimize the dialyzer parameters and to identify the most suitable therapy for reducing intra-dialysis complications and associated long-term dysfunctions. Introduction Considered medical problems and mathematical models: 2) lumped parameter models of the cardiovascular system Fluid-dynamic behavior of blood in vessels can be characterized in terms of a set of lumped parameters in each segment. The resulting mathematical description is equivalent to that of an electric circuit. 5
Lesson 1 Introduction Considered medical problems and mathematical models: 2) lumped parameter models of the cardiovascular system The resulting system of differential equations can be easily studied to evaluate: • the impact of alterations in model parameters ( e.g., the link between reduced compliance and hypertension ); • the interaction of the system with external devices ( e.g., cardiopulmonary bypass ). Introduction Considered medical problems and mathematical models: 3) bioimaging analysis Estimation of functional parameters from a set of images acquired on a patient. Example in Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI): how to estimate diffusion and pseudo-diffusion coefficients from a set of images acquired at different b values. Estimated parameters are useful biomarkers for detecting several pathologies. 6
Lesson 1 Introduction Considered medical problems and mathematical models: 4) aortic compliance evaluation Similar idea of estimating functional parameters from a set of images acquired on a patient. Introduction Considered medical problems and mathematical models: 4) aortic compliance evaluation • Set of aortic Computed Tomography (CT) acquired over the cardiac cycle • Identification of the radius profile over the cardiac cycle in several sections • Mechanical model to get the compliance given the radius profile. 7
Lesson 1 Introduction Considered medical problems and mathematical models: 5) extracorporeal membrane oxygenation (ECMO) The goal is to provide the best set of parameters to the ECMO machine and alarms if the conditions become critical. Mathematical model to describe the ECMO, the patient, and their interactions. Comparison between model outcomes and measurements to evaluate patient’s parameters. Decisions on setting and alarms based on patient’s parameters e predicted evolution from the model. Introduction Considered medical problems and mathematical models: 6) artificial pancreas The goal is to perform an automatic blood glucose control. It requires an estimation of the patient’s needs and his/her conditions, together with a decision model to set the released quantity based on the esimated needs. 8
Lesson 1 Introduction Considered medical problems and mathematical models: 6) machine learning for decision-making models Machine learning and artificial intelligence are spreading in all applicative fields. We will see one of the applications in medicine, e.g., the implantable bioartificial pancrease. How machine learning can support the design and control of these artefacts? Introduction Calendar 24 lessons of 2 hours 9
Lesson 1 Introduction Development of students’ project: • Students divided into groups with at maximum 3 students • Each group chooses one of the examples discussed in the course or propose a new problem ( which needs to be approved ) • Each group proposes a solution and performs a detailed analysis on the dataset • Outcomes will be presented in a report and a 15 minutes presentation Introduction Evaluation: 1. written exam dealing with all theoretical background and examples discussed in the course, followed by oral exam to discuss the written exam and including other questions 2. presentation of the group project Presentation of the project consists of providing the report and preparing a presentation of 15 minutes. This presentation will be made by the students of the group together. It can be done both before or after the other part. The mark will be registered after both parts are completed. The mark is a weighted average (weight 75% for exam; 25% for project) 10
Lesson 1 Medical decision making: history and perspectives A quick historical view of medical decision making to highlight the future perspectives Medical decision making: history and perspectives Timeline ANCIENT GRECE In ancient Greek medicine illness was initially regarded as a divine punishment and healing as, quite literally, a gift from the gods. No idea of searching for a cause . 11
Lesson 1 Medical decision making: history and perspectives Timeline ANCIENT GRECE By the 5th century BCE, attempts to identify the material causes for illnesses rather than spiritual ones. Greek medical practitioners, began to take a greater interest in the body itself and to explore the connection between cause and effect , the relation of symptoms to the illness itself and the success or failure of various treatments. Medical decision making: history and perspectives Timeline MIDDLE AGES Medieval doctors did not have an idea of what caused a disease. Magic motivations not related to observations on previous patients. 12
Lesson 1 Medical decision making: history and perspectives Timeline EARLY MODERN AGE First scientific discoveries! For example, William Harvey's discovery of the circulation of the blood in 1628, and Anton van Leeuwenhoek's observation of bacteria in 1683. Connection between observations and health conditions . Medical decision making: history and perspectives Timeline EARLY MODERN AGE Idea of observing patients and taking information from previous observations. Patients may share similar behaviors. 13
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