Applied Computing is a field within SCIENCE which applies practical approaches of computer science to real world problems презентация

Technology
d.aljumeily@ljmu.ac.uk

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of Data Science in Healthcare and Medicine

practical approaches of computer science to real world problems across multiple disciplines to produce effective solutions.
The techniques used within computing open up possibilities for other disciplines which can be explored for solutions and improvements to tricky problems which ultimately are either too complex or big for humans to provide the answers.
Applied computing uses various computer science techniques such as algorithms, databases, networks and more modern applications such as deep learning, Big Data and more to build systems and applications which can be used in the real world.

Applied computing has been effective in many areas for developing solutions which can analyse data to provide informative results for applications in real-world situations, the following branches are some of those in which applied computing has been effectively used:

Education and Learning Techniques
Health and Medicine
Gaming
Business
Economics

since ancient times. Such precursors of the modern computing era now serve to demonstrate both the conceptual and technological difficulties in the evolution of such processing capabilities during the pre-digital age.
Early examples of applied computing include the well known abacus, the earliest computational instrument on record, along with less widely known mechanical artefacts such as the Antikythera mechanism, an intricate analog device developed during the first century BC to calculate astronomical events.

the Antikythera mechanism, an intricate analog device developed during the first century BC to calculate astronomical events.
However, despite the endurance of both manual arithmetic devices and one-of-a-kind automatic mechanisms during periods of antiquity, the position of computing remained largely unchanged up until 1800, a time at which the effects of the industrial revolution had become well established. It is estimated that fewer than 100 automatic computing mechanisms were ever built prior to this time.

Century, a sustained transition began to occur from devices that could perform arithmetic, to devices that could represent logic through mechanical operation. During this time, commercially viable devices that could accept task-specific "programs” were developed, such as the Jacquard loom developed in 1804.

such inventions, developed designs for two automatic devices, the "Difference Engine" and the "Analytical Engine". The latter represented a conceptual breakthrough, a sketch for the first programmable digital computer. Though conceptually sound, the Analytical Engine was never realised due to the cost and difficult demanded by such an undertaking at the time.

machines that were created to perform information processing tasks, specific to a goal of interest. The physical machine could be programmed to alter the nature of the task, for example in the case of the Jacquard loom. However, if the goal of interest was to change, the physical machine itself, the substrate upon which the computation was run, would have to be redesigned and reconstructed. In 1936, a theoretical concept known as the "Universal Turing Machine" was introduced by Alan Turing in a paper titled "On computable numbers, with an application to the Entscheidungsproblem".
Turing had discovered a property of computation that would enable the full potential of computing applications to be unleashed, he found that with the provision of a set of simple primitive operations, the space of all possible computations could be represented on any machine without undertaking a redesign of the machine itself. Conversely, the same realisation simultaneously proved that most previously developed machinery was fundamentally limited, since the not all of the so called primitives were incorporated into these devices. The notion of "Turing Completeness" is now well recognised and is fundamental to the design of all general purpose computing systems, having opened the way for both modern applied computing research and the exploration of artificial intelligence.

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Conventional computers follow a set of ‘well-defined’ instructions in order to solve a problem.

When equipped with the appropriate rules, it can perform many tasks better or faster than humans.

BUT
conventional computing ‘struggles’ with certain types of tasks !

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Computer performed better than the human at chess in the late 1990’s !

in waht oredr the ltteers in a wrod are, the olny iprmoetnt tihng is taht frist and lsat ltteer is at the rghit pclae. The rset can be a toatl mses and you can sitll raed it wouthit porbelm.

[rscheearch at Cmabrigde Uinervtisy]

or previously unseen situations
Decision making
Extremely large number of possible solutions to choose from

Examples:

Recognising complex patterns such as human faces or emotions
Recognising a variety of handwriting.
Identifying the words and context in spoken language.
Driving a car through busy streets.

deal by studying natural and biological systems and modelling some of their problem solving and adaptation strategies

Bio-inspired computing:
Computing based on ideas, principles and concepts from biology and nature

Ants
Bees / Wasps
Immune System
DNA
Birds / Fish
Others !

Algorithm / Computing paradigms
Artificial Neural Networks (Rosenbl’t 1958, Rumelhart 1986).
Genetic Algorithms (Goldberg and David, 1989)
Particle Swarm Optimisation (Kennedy, 1995)
Ant Colony Algorithms (Dorigo and Di Caro, 1999)
Artificial Bee Colony algorithm (Karaboga, 2005)
Artificial Immune Systems (De Castro, Timmis 2002)
DNA Computing (Adleman 1994)
Flocking Behaviour (Reynolds 1987)
Others !

(e.g. path planning)
Security (e.g. intrusion detection)
Speech and Natural Language Processing


Bio-inspired Computing examples
Neural Computing (Neural Networks)
Evolutionary Computing (Genetic Algorithms)

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computation differently from conventional computers
Learn from examples and experience
Massive parallelism (1010 Neurons)

Neurons communicate with each other through signals that can be excitatory (+) or inhibitory (-).

The artificial (binary) neuron computes a weighted sum of the inputs and outputs a value according to whether the sum is above or below a threshold.

theodysseyonline.com

Artificial Neuron

Biological Neuron

Activation
function

(neurons) working in parallel to solve a specific problem.

Not programmed to perform a specific task.

Instead, neural networks learn by example.

Go through a learning (training) process to become an ‘expert’ in a specific task.

Can then be used to perform this task in similar and new situations or scenarios.

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challenges

Over-learning and under-learning
Representative-ness of the training set
Local ‘optimum’ solutions

for ‘optimum’ solutions.

An optimisation problem is one which has a set of possible solutions, and the goal is to find ‘best’ solution.

E.g: Traveling Salesman Problem

Cannot evaluate all possible solutions !
computationally expensive, impractical or impossible.

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Traveling salesman problem
Given a set of cities and the distances between them, find the shortest possible route that visits each city once and returns to the origin city

Genetic Algorithms (GAs)

of high-fitness solutions through many generations.

Use a ‘fitness’ measure to evaluate the fitness of the solutions.

of high-fitness solutions through many generations.

Use a ‘fitness’ measure to evaluate the fitness of the solutions.

of high-fitness solutions through many generations.

Use a ‘fitness’ measure to evaluate the fitness of the solutions.

Crossover: combination of parents materials to form children

Selection/reproduction Give preference to better solutions (Survival of the fittest principle).

of high-fitness solutions through many generations.

Use a ‘fitness’ measure to evaluate the fitness of the solutions.

Crossover: combination of parents materials to form children

Selection/reproduction Give preference to better solutions (Survival of the fittest principle).

Mutation: change to part of some solutions

(2008). An intelligent systems framework for prototyping tactical driving decisions. Intelligent Systems Technologies and Applications.
Tawfik, H. and Liatsis, P. (2006). Modelling tactical driving manoeuvres with GA-INTACT. Computational Science.

GA selects a ‘tactical’ driving decision in terms of change of lane, change of speed, change of acceleration, etc

‘sub-optimal’ solutions

(internal layers provide learned representations of the input data)
Can be trained on very large collections of images, videos, and speech samples.
Major improvements in recent years in image, speech and handwriting recognition.

Automatic generation of text captions for images with deep networks
In: Jordan, M.I. and Mitchell, T. M. SCIENCE, 2015

be processed or analysed using traditional processes or tools
Organisations produce vast amounts of data each and every day
Some of this is unstructured or at best semi-structured

structure or pattern
Is easy to find what you are looking for as it will always be in the correct place
Can be processed by humans relatively easily
Think of a phone book:
Ordered by Surname
Follows a set pattern for all entries

Unstructured data

Has no meaningful pattern
Think of a wall with space to write on and a million people with a marker pen!
Difficult to find anything without trawling through all the data and trying to make sense of it (not for humans!)
Computers can be used to help process the data and find things

can take
Velocity - the speed at which the data is produced or needs to be processed
Volume – the amount of data produced or consumed
Veracity – the truthfulness (or uncertainty) of data

IBM characterises Big Data by the four Vs

can be carried out by directly analysing and learning from data.

A lot of knowledge discovery can be driven by the availability of the massive amount of data.

It can be much easier to train a system by presenting it with examples of desired response than to program it to predict the response for all possible scenarios.

Descriptive Analytics (What has happened?) Predictive Analytics (What could happen ?) Prescriptive Analytics (What should we do?)

CDC data

New models are estimating which cities are most at risk for spread of the Ebola virus.

Partition Tolerance

meaningful and useful information for business analysis purposes. BI can handle enormous amounts of unstructured data to help identify, develop and otherwise create new strategic business opportunities - Wikipedia

Institute, White House, Berkeley

Big Data Science)

computing paradigms that are powerful, fault tolerant and capable of adapting to the challenging nature of the data.

Scientists and engineers are turning to biologically and nature-inspired computing, and other AI, techniques to obtain useful insights from big data.

within the previous slides; the application of applied computing becomes apparent further to the end product.

The research involved within our area is predominantly theory-based; the applications of the derived theory can then be used to provide applied computing opportunities.
By using Machine Learning (ML) techniques and statistical methods to classify and produce predictive outcomes; these conclusive algorithms and results can be used within structures such as Decision Support Systems (DSS) which can aid health professionals in making decisions about a patients condition, their treatment options or in outlining the risk factors associated with a patients’ condition given their biological make-up or environment.

machine learning refers to a computer program that able to learn and gain knowledge from past experiences and/or through identifying the important features of a given dataset in order to make predictions about other data that were not a part of the original training set.

MACHINE LEARNING WITHIN CLINICAL DECISION SUPPORT SYSTEMS (CDSS)

ML considered to be the backbone for the majority of sophisticated CDSS.
It is one of the principal components of the information architecture of CDSS.
It is essential part of CDSS and enables such systems to learn over time.
It would handle more complicated decisions that might require a specialist knowledge as well as evaluating the consequences of the suggested solution.

is one of the most prevalent diseases, which could have an influence on patients’ lives due to red blood cell (RBC) abnormality.
Life expectancy by 20 to 30 years and affects a large number of inhabitants worldwide, particularly in Africa and Asia continent.
SCD can easily inherited to the child throughout genetic of sickle haemoglobin (Hb S) either from both parents or from one of them measured as abnormal haemoglobin.
According to the World health Organisation (WHO), 7 million born each year suffers from this disease.
5% of the population around the globe carries traits genes for haemoglobin disorders.

vital for patients to mitigate the severe of disease by taking the appropriate medicine at proper time.
Within this context, there is a significant need for constructing cooperative care environment to improve quality of care and increase caregivers’ efficiency with the purpose of providing regular information for medical experts and patients.
In order to achieve that facilities, this research focus on how to develop an intelligent system to provide short term interpretation of overall goals in Sickle Cell Disease (SCD).

TREATMENT CHALLENGES

are using manual approach that depends completely on patient input, which can be slowly analysed, time consuming and stressful as well.
The most challenging aspects that facing healthcare in these days is that there is still insufficient communication between the SCD patients and association healthcare providers.

There still need for developing of an intelligent SCD diagnosis system that eligible to provide a specific treatment plan inspired by expert system.
There are still a number of barriers to obtain excellent communication between both patient-medical experts relationship in terms of workload, patients' fear and anxiety, and fear of verbal or abuse physical.

variation in living organisms.

DNA
Genes are made up of DNA. DNA is essentially a code which uses 4 nucleotide bases to define a ‘message’, these 4 nucleotide bases are: Adenine (A), Guanine (G), Cytonsine (C) and Thymine (T).

This DNA is then unwrapped from its’ double-helix form and subjected to various processes in order to produce RNA through the transcription process and Proteins from the translation process. This whole process is called the ‘Central Dogma’.

Within each cell the genetic information flows from DNA to RNA to protein.

TRANSCRIPTION PROCESS
By unravelling the Double-Helix strand, a copy of the template strand of DNA will be made which forms what we call ‘RNA’. The Thymine which was originally in the DNA changes to Uracil during this process.

TRANSLATION PROCESS
The RNA produced from the transcription will be used to produce protein in the translation process. The RNA will be up of a single strand of nucleotides which will then be separated into block of 3–

a c g u g g c a u c c u g u a

These block of 3, ‘codons’, will be translated into proteins e.g. a c g ? Threonine

sequence - including genes (exons) and “noncoding” (intron) DNA segments in the chromosomes and how those genes interact with each other, as well as the internal and external environments they are exposed to.

WHAT HAS BEEN DONE IN THE AREA?
Human Genome Project is an example of a considerable achievement in the area of genomics. It was an international research effort to determine the sequence of the human genome and identify the genes that it contains.

WHAT HAS BEEN FOUND?
Overall it has been estimated that there are only approximately 24,000 genes in the human body (previously thought to be 100,000 genes!)

The aim is to find out, at what stage the problems start in the genome for each disease, disorder and illness; and if they can find this (can they produce a treatment or prevention?)

biological data such as human genes.

There are many data types which can be used to determine different variations and causations for diseases and disorders.

Types of data we are considering in our research:

RAW DATA
Signal data





Translated signal data
a c g t a a t g c t a t t g c t c c a g t

SINGLE NUCLEOTIDE POLYMORPHISMS(SNPs)






PROTEIN EXPRESSION



CLINICAL DATA

individuals body based on their own personal information, and as a result the treatment of a patient should be more comfortable with the elimination, or at a very minimum considerable reductions of side effects.

Current and Common Approach: ‘One size fits all’

Personalised medicine: ‘Catered Medication’

sequence) of individuals on their drug metabolism or drug response.
Each patient reacts differently to the medication; pharmacogenomics can help to determine whether a patients’ reaction to medication puts them in the category of responders or non-responders or results in bad side effects (Adverse Drug Reaction [ADR]).
Pharmacogenomics can advance the development of personalized medicine.
 

PHARMACODYNAMICS
This area focuses on the resulting function of the medication and the effects . This study takes into account the drug concentration, site of action and the resulting effect of the administered drug.

PHARMACOKINETICS
How the body interacts with the drug (the transportation). This area focuses on how long it takes for the drug to reach its’ destination (or if it even will). It measures the ‘time course’ of:
Absorption
Distribution
Metabolism
Excretion (Elimination)

to faults and uncertainties, make them a valuable data science tool in the big data era.


Novel developments/areas of research include:
More sophisticated bio-inspired systems
Collaborative and team based learning
Crowd computing
Quantum computing