Medical Informatics: Computational Analytics in Healthcare Liu Nan Department of Emergency Medicine Singapore General Hospital Division of Research Health Services Research & Biostatistics Unit Singapore General Hospital

Medical Informatics: What is it?

• A discipline at the intersection of healthcare, information science, computer science, social science, and behavioural science, etc

• Deals with the resources, devices, and methods required to optimize the acquisition, storage, retrieval, and use of information in health and biomedicine

• Needs computing infrastructure, clinical guidelines, formal medical terminologies, and information and communication systems

• Application areas include nursing, clinical care, dentistry, pharmacy, public health, physical therapy, etc

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Medical Informatics: Why Do We Need It?

• Hospitals have moved from paper-based information management to electronic health record (EHR) system. This has enabled the retrieval of massive data (e.g. free text, image, video, audio, etc)

• Computational modeling methods have been applied to a wide spectrum of applications such as big data analytics, information retrieval, robotics, bioinformatics, and medicine

• Conventional statistical and mathematical methods continue to play important roles while new emerging technologies like machine learning and data mining have established their reputations in solving complex and difficult problems

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Medical Informatics: Research Areas

• Clinical informatics: Evaluate and refine clinical processes; Develop, implement, and refine medical decision support systems

• Public Health Informatics: Apply informatics in areas of public health, including surveillance, prevention, preparedness & health promotion

• Translational Bioinformatics: Transform biomedical data and genomic data, into proactive, predictive, preventive, and participatory health

• Other areas like bioimaging informatics, etc

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What is Machine Learning

Machine Learning:

The Core of

Medical Informatics

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What is Machine Learning

• Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data

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An example of

supervised

learning: 2-class

classification

Class 1

Class 2

Decision boundary

Testing sample

Popular Machine Learning Approaches

• Decision tree learning

• Artificial neural networks

• Support vector machines

• Clustering (unsupervised learning)

• Bayesian networks

• Representation learning (feature extraction)

• Similarity and metric learning (data ranking)

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Machine Learning vs. Biostatistics

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Pros • Machine learning is flexible; It provides a lot of options • Machine learning usually achieves better prediction performance Cons • Some machine learning approaches are black-box systems • Predictive variables may not be statistically significant

Which one to choose? • Use traditional biostatistics for primary analysis • Use machine learning for secondary analysis • Both methods are complementary, not competing each other

What is Machine Learning

Medical Informatics Example:

Prediction of

Major Adverse Cardiac Events

(MACE)

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Background

• Triage is the clinical process of rapidly screening large numbers of patients to assess severity and assign priority of treatment

• Currently, triage is generally done by nurses and depends on traditional vital signs and other physiological parameters

• Objective, fast and accurate risk stratification is important to quickly identify high risk patients in the Emergency Department (ED)

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Motivation & Objective

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• Medical resources are limited. Numbers of doctors, nurses, medical facilities may not be sufficient for fluctuating demand

• Traditional vital signs used in triage are not shown to correlate well with MACE

• To explore the utility of new variables, e.g. heart rate variability (HRV)

• To design state-of-the-art intelligent and statistical scoring methods for risk stratification in critically ill patients

Heart Rate Variability

• HRV is the beat-to-beat variation in time interval between heart beats (RR interval) under control of autonomic nervous system

• HRV has shown significant relationship between autonomic nervous system and cardiovascular mortality

• We have previously shown that HRV outperforms vital signs in risk stratification and a combined use of both performs even better

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Study Design & Data Collection

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Patient

Acquire ECG signals

Acquire vital signs, e.g. SpO2

Process raw ECG signal and calculate

HRV parameters

Machine learning based scoring system

Risk scores

Collected data from previous patients: • HRV parameters • Vital signs • Outcomes

Preliminary Results

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ML MEWS TIMI

AUC 0.813 0.672 0.621

Sen. 78.9% 42.1% 78.9%

Spe. 74.1% 78.5% 36.7%

PPV 9.6% 6.4% 4.2%

NPV 99.0% 97.5% 98.0%

ML: Machine learning; MEWS: The modified early warning score; TIMI: Thrombolysis in myocardial infarction

News Report

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Technical Challenge: Data Imbalance

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• Some medical datasets are imbalanced where majority class is over-presented (only 5% samples of our data meet clinical outcome)

• Most machine learning techniques are not applicable with such bias dataset where majority class samples dominate decision making

• Our solution is using ensemble learning methods to manipulate data to create several balanced subsets for risk model training

• Data imbalance is common in real-world medical applications. Traditional statistical methods are usually not suitable

Technical Challenge: Variable Selection

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• Redundant and irrelevant information may degrade prediction

performance, thus variable selection methods are needed

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Technical Challenges: System Design

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• On the one hand, better prediction performance may require more inputs such as 12-lead ECG and vital signs, which make device big and complex

• On the other hand, light-weight and easy-to-use are the most important features for devices in ED or at home

• Need to find a trade-off between size and performance

What is Machine Learning

Medical Informatics Example:

Applications in

Other Medical Specialties

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Pan-Asian Resuscitation Outcomes Study (PAROS)

• PAROS is a research network (10+ countries) dedicated to Pre-hospital & Emergency Care (PEC) research

• Out-of-Hospital Cardiac Arrest (OHCA) being one of the leading causes of death

• Outcome prediction using machine learning may be useful for analyzing the effects of different resuscitation strategies

• Good-quality interventions are needed

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Retinal Vascular Abnormalities and Risk of Hypertension

• Image processing methods have been used to calculate clinical parameters

• Machine learning can be applied to investigate the association between these parameters with the risk of hypertension

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Cheung et al. Stroke. 2013;44:2402-2408

Other Ongoing Projects

• Deriving predictors for traumatic brain injury among children (Paediatrics)

• Identification of patients at risk of walking disability 6 months post total knee arthroplasty (Physiotherapy)

• Derivation and validation of a predictive model for patients at risk of readmission (Family Medicine)

• Natural language processing and its application on unstructured medical free text for knowledge discovery (General Surgery)

• Others

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What is Machine Learning

Medical Informatics

is Important

in Healthcare

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Summary

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• The aim of medical informatics is to improve patient care

• Machine learning is applicable in many different medical specialties: emergency medicine, eye, surgery, paediatrics, family medicine, allied health, etc

• Machine learning is an alternative method for data analysis

• Machine learning is complementary to statistical analysis

• Machine learning is promising when you aim for filing a patent and/or building a start-up company for commercialization

What is Machine Learning

Thank you!

Liu Nan

liu.nan@sgh.com.sg

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