University of Manchester Health Data Science Masters Modules

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1 University of Manchester Health Data Science Masters Modules We are taking applications now for Masters CPD modules beginning in February. All modules are 15 credits and cost 750. Timetable is as follows for each: Principles in Digital Biology Thursday 4 February 2016 Thursday 11 February 2016 Thursday 18 February 2016 Thursday 25 February 2016 Thursday 3 March 2016 Biology is currently undergoing a revolution. The success of the human genome project and other high-throughput technologies is creating a flood of new data. Capturing, interpreting and analysing this data provides real and significant challenges for computer scientists. The course is organised in 4 sections: 1. basic introduction to modern biology and bioinformatics 2. data capture 3. data delivery 4. data analysis Each section will commence with a short taught component delivered as research seminars. Indicative content: 1. Intro to Biology - Intro to Biology - the central dogma (2 hours) Intro to genomics (2 hours) Biology databases (2 hours) 2. Data capture - Capturing microarray data (1 hour); Proteomics seminar (1 hour); The gene ontology (1 hour); Resource meta-data (1 hour) 3. Data delivery - HCI and bioinformatics (2 hours); Dealing with heterogeneous, distributed data. (2 hours); Bioinformatics and the grid (2 hours) 4. Data analysis - Integrated approaches to post-genome data (2 hours)

2 Understanding Data and Decision Making Tuesday 23rd February 2016 Wednesday 24th February 2016 Tuesday 15th March 2016 Wednesday 16th March 2016 Tuesday 19th April 2016 Wednesday 20th April 2016 The applications in health data science are vast; each incorporating a different set of methods and technologies to be able to address the health problem. To improve the delivery of healthcare there is a need to maximise the potential of health data by turning it into useful intelligence to provide insights into past, current and future healthcare delivery. The role of the health data scientist is to be at the forefront of this and to influence decision-making for healthcare delivery by deriving understanding and significance from data. To be able to do this it is critical to understand the decision-making governance and cycle; know how to elicit understanding of the clinical and public health objectives and processes; understand the different data sources available to support decision-making, and which are appropriate to use; understand which techniques and methodologies are most appropriate to investigate data; and know how to communicate and visualise results /ideas to various stakeholders (often from a non-technical background; including the public); and determine how this will impact on healthcare. This unit will integrate technical and methodological skills with each of these issues and apply them to uses of big data in healthcare. This unit will be delivered in the context of a number of real-world case-studies drawn from research at the University of Manchester and NHS. Indicative content for the unit includes: Governance - Decision-making process Data sources, quality, technical, ethical and legal issues; linking data sources Data visualisation techniques, software and presentation style Communication/presentation styles requirements elicitation; Organisational and change management Risk management

3 Introduction to Health Informatics Monday 14 March 2016 Monday 11 April 2016 Monday 18 April 2016 Monday 25 April 2016 Monday 2 May 2016 Central to any efforts to build an efficient health care system, or in supporting evidence-based medicine, is the need to capture information around patient diagnosis and medical treatment. At the core of this activity is the development of electronic patient records. Good quality electronic patient records require us to have systematic and unambiguous tools for recording the health state of a patient, and the treatments they receive. This unit provides a basic introduction to the development and use of electronic patient records, their long history, and the challenges still to be overcome. Patient data is captured in many parts of the health service, in GP surgeries, in hospital labs, by different clinical teams. All this data needs to be brought together and shared - whether that be to build a record for a single patient, or provide an overview of activity across a region. This requires that data can be shared and integrated. However, the challenges in developing such systems are not purely technical. Issues around organisational and human factors are at least as difficult to develop effective solutions as developing an appropriate IT infrastructure. Indicative content Part 1: Coding 5. Introduce students to basic concepts in coding systems used in health care 6. Examine the history of coding schemes in medicine 7. Explore some of the current coding schemes in detail (Read codes) and discuss future developments (Snomed) 8. Examine some of the issues and problems around the use and maintenance of coding schemes 9. Discuss the development and deployment of electronic patient records (EPRs) 10. Explore issues of data governance and patient confidentiality around electronic patient records.

4 Part 2: Sharing 1. Introduce students to basic concepts in of interoperability in health care 2. Introduce basic messaging concepts, including an overview of HL7 3. Introduce issues and problems around data interoperability 4. Examine strategies for data integration Part 3: EPRs in practice 1. Introduce students to basic concepts in of organisational and human factors in e-health 2. Explore issues of EPRs in practice 3. Introduce basic concepts around qualitative research methodologies 4. Introduce basic concepts around useability in healthcare 5. Introduce concepts around organisational change in the health service All sessions are 10am 4pm Other modules available later in the year Tutorials in Health Data Science A health data scientist brings together individual pieces of knowledge and understanding in order to be able to devise an appropriate strategy to improve healthcare. A key characteristic for this role is to be an adaptable, curious and entrepreneurial thinker who can synthesis information quickly to devise research strategies to improve healthcare delivery from individual treatments, service delivery to population health. In order to do this it is key to be able to have an understanding of the wider academic research landscape and how individual health data science research fits within this. This translational thinking will drive new innovations and research within the health sector. This unit will provide an environment from which student will confidently develop critical skills around a set of core academic papers.

5 Introduction to Software Development in Java Object-oriented basics - What is Java?; Mental models - how we deal with the world; Software objects - mental models on a computer; Creating objects and sending messages; A complete simple class Importance of documentation javadoc; Other ways of programming - and why OO is better! (optional) Imperative programming: Nuts and bolts (scalar values and expressions); Handling text (Strings and the magic +); Saying things and doing things (declarations and statements); Making choices (if and switch); Repeated computation (while and for); The simplest collection (arrays); How fast does it go? (A first look at complexity); Dividing up the job (procedural abstraction, parameter passing) Classes, responsibilities and Collaborations Alternative implementations (encapsulation); Alternative interfaces (overloading); When are two objects the same (object references, equality vs. identity); Assigning responsibilities to classes (which methods go where, unit testing);collaborating classes to solve problems (putting it all together, system testing); What if there's no object to send a message to (static things); Larger-scale organisation (packages) Inheritance Mental models revisited - is-a-kind-of hierarchies; Abstract classes (representing common abstractions); Extending classes (concrete sub-concepts); The way objects understand messages (static checking, dynamic binding); What have we inherited? (inheritance semantics); When to use inheritance (is-a test, evils of implementation inheritance) Interfaces (in the Javaspecific sense) Exception Handling What if unexpected things happen at runtime?; Basic constructs - try.. catch.. finally, exception propagation; Throwing exceptions and declaring them (throw and throws); Standard exception types (Throwables, Errors, Runtime Exceptions, checked exceptions); Contracts (informal notion) Collections and algorithms Overview: collection interfaces and implementations; Sample (1.5) classes (Lists and Maps); Basic algorithms (e.g. sorting) and their complexity; Recursion and tree structures Building simple GUIs Platform independent graphics and GUIs: AWT and Swing; Building basic GUIs; What's an applet - and what's it good for?; Handling events Stream and File I/O Streams - System.out revealed; Text I/OFile handling Options for storing data XML vs serialization vs. relational DB

6 Health Information Systems and Technologies This unit will provide an opportunity for students to develop an understanding of health systems and technologies, and their implementation. In order to understand a health system or technology a health data scientists needs to be aware of the core issues and methodologies in developing, deploying and managing a healthcare system, and the impact to healthcare delivery. It is also paramount to understand how the data is created, collected, stored and retrieved so as to be able to use it. In particular, the unit will cover the framework for handling patient data in a confidential and secure manner to ethical and quality standards that are appropriate for a modern health service. To be able to have a full understanding of the issues it is useful to have an insight into how the healthcare has arrived to its present day situation. This unit will cover the following indicative content: Health Information Systems and Technologies social and mobile health technologies; architectures; networks; internet; cloud; Data storage and retrieval technologies databases and Standard Querying Language (SQL) Information governance and security Data models and architecture System testing and quality assurance: ISO-standards System design cycle and methodologies agile; waterfall Description of main requirements analysis methodologies Evaluation of informatics systems design/development Critical appraisal and understanding failure and success factors of historical and current project Fundamental Mathematics and Statistics for Health Data Science Currently, there is a large amount of health and related data that is not analysed in order to provide insights into healthcare delivery. A core skill required of a health data scientist is to be able to analyse various forms of health data: the unit will cover the fundamental knowledge required to do this including understanding the data; pre-processing steps; key analytical skills and a suitable statistical programming language. This unit will introduce students into what can be achieved through the analysis of health data. Key research questions will be drawn from HeRC to illustrate these techniques. Indicative content: Overview of key statistical measures and theory Introductory mathematical toolkit - Probability theory Data pre-processing - Cleaning, visualisation and integrity checks

7 Supervised learning - Goodness of fit; Risk/loss functions (correlation, RMSE, accuracy, sensitivity, specificity); Univariable methods and distributions; Normal, Poisson, Bionomial, Multinomial, Gamma-families; Chi-squared test, t-test, Wilcoxon, ANOVA, Kruskal-Wallis Classification and Regression - Linear models; Generalised linear models Data mining/unsupervised learning - Principle Component Analysis; Hierarchical clustering; Partitional clustering Study Designs - Trial-type design; Cohort (observational) studies; Confounding and causality; Biomedical Modelling for Health Data The health sector is rich with data that currently remains under-utilised and often uses data to look at past healthcare delivery rather than using the data in order to provide insight to enhance healthcare delivery. A key component skill set of a data scientist is to be able to understand and implement a suite of predictive modelling and data mining methods in order to this. This unit will be build on central concepts and methods introduced in the pre-requisite unit Fundamentals in Mathematics and Statistics in Health Data Science in order to provide a complete data analytical toolkit (including machine learning methods) to explore health data. The unit will be application driven with case-studies and examples will be drawn from health research across the University of Manchester and Health eresearch Centre. The unit will cover the following topics: Section 1: Survival and Time series analysis Kaplain Meirer; Cox (cause-specific and competing risk; time updated variables; joint longitudinal models; Survival Trees and Models Hierarchical Linear Models Section 2: Neural Networks Decision Trees Ensemble-tree like methods including random forests; boosting; bagging Kernel smoothing Section 3: Feature Selection stepwise and shrinkage methods for linear models Model Selection, error estimation, model robustness in sample error estimation; extra sample error estimation

8 Fundamentals of Epidemiology Summary In this course students will learn about the history of the discipline of epidemiology, as well as its uses in medical research and in informing health policy. Students will be introduced to common types of observational study designs, including descriptive, case-control and cohort study designs, and the appropriate methods of measuring and comparing risk in each type of study. Students will also learn about the limitations of epidemiological studies and how to minimise systematic errors when conducting their own investigations. Vocational Relevance Epidemiology is the core scientific skill for the practice of public health, and also been adopted by clinicians as the scientific basis on which evidence-based practice is built. This course is therefore relevant to current or future professionals involved with either conducting health-related research or interpreting the findings of research studies. Content Structure Uses of epidemiology, including studying causation, historical aspects and use for health policy Various measures of morbidity, including incidence and prevalence Measures of risk and use for understanding causation Study designs, advantages and disadvantages of the different study designs Practical issues in conducting epidemiological studies Collection of information in epidemiological studies Bias, confounding and effect modification in epidemiological studies Further information available from Kieran O Malley ([email protected])