When diagnosing a patient to determine the presence and type of cancer, a doctor would analyse their test results. Needless to say that correctly diagnosing cancer as well as its type is a cornerstone of successful treatment.
Today a doctor would turn to the literature on the subject and examine similar cases. But another option is entering the patient’s test results and medical history into a computer program and let it compare to millions of similar pathology records.
There are types and subtypes of cancer that are very rare and difficult to distinguish. And different subtypes of cancer may require dramatically different treatment plans.
To address this problem, a team from MIT’s Computer Science and Artificial Intelligence Laboratory introduced a model that aims to automatically distinguish the type of lymphoma – a group of blood cancers.
The model uses many techniques, among them are Natural Language Processing and Machine Learning.
The elaborated framework analyses pathology reports, which provide a comprehensive scope of measurements, observations, and interpretations made by pathologists – all expressed in natural language. With detailed feature analysis, their system generates meaningful features and medical insights into lymphoma classification.
Team’s model can help doctors make more accurate diagnosis based on more comprehensive evidence. Imagine what great impact would of such a system make if expanded across other institutions!
Now answers and advice of specialized domain can be provided not only by an expert, but via a software program as well.
And such software could be created for professionals from any sphere – from insurance adjusters to engineers and managers.
And do it even better, than a human.
What is an Expert System
An expert system (ES) is a program that is designed to solve problems within specialized domain that ordinary requires a human expert.
By mimicking the thinking of the human experts, the system can perform the analysis, design, or monitoring, make decisions and more.
In fact, such systems were built long ago, and were the first successful implication of Artificial Intelligence. But due to the poor development of AI, NLP, the Expert Systems did not live up to the business-world expectations and the term itself has left out from the IT-world lexicon.
But now, with the rapid development and prominent advancements of Artificial Intelligence, Machine Learning, Deep Learning and Natural Language Processing we are about to observe the comeback of them.
They can be called under different names, but the essence stays the same – solving expert-level issues.
1. Knowledge base
The power of expert systems stems from the specific knowledge about a narrow domain one stores. The knowledge base of an ES contains both factual and heuristic knowledge.
2. Inference engine – the reasoning mechanism
Inference engine provides a methodology for reasoning about information in the knowledge base. Its goal is to come up with a recommendation, and to do so it combines the facts of a specific case (input data) with the knowledge contained in the knowledge base.
Inference can be performed using semantics networks, production rules, and logic statements.
There are two types of data-driven strategies – forward and backward chaining. Forward chaining is applied to make predictions, whereas backward chaining finds out the reasons why a certain act has happened.
3. User interface – the hardware and software that provides interaction between program and users.
Types of problems that can be solved
ESs can be differentiated by the action they perform or a type of problem they help resolve:
Classification & Diagnosis: identify an object based on stated characteristics
Examples: medical disease diagnosis, insurance application fraud detection
Monitoring: continuously comparing data with prescribed behavior
Examples: leakage monitoring in long petroleum pipeline, founding out faults in vehicles
Prediction: showing the optimal plan
Examples: prediction of share market status, contract estimation
Design: configuring a system according to specifications
Examples: airline scheduling, cargo scheduling
Benefits of ES
Expert knowledge becomes available
Expertise is very difficult to obtain and capture.
At a certain point, many experts deepen their understanding to such a degree, that their decisions become somewhat intuitive. As a result, their explanations wouldn’t be of much help. Besides, their time is precious and should not be dispersed on indirect tasks too often.
But once expert knowledge was mined and stored into the software in a structured way, it can then be easily retrieved and comprehended.
Pieces of information are taken together
The specialists whom an a professional might like to consult may be not within reach. Also, a specialist may be not aware of modern inventions, new studies and discoveries related to a part of their job.
An ES can be of great help by offering knowledge of similar cases, especially if used by an international company. Besides, an ES can also serve as a self-check tool.
Automation & speed
ESs offer great speed and reduce the amount of work an individual puts in.
Reduced errors and risk
An ESs error rate is lower as compared to human errors.
Not to mention the fact that they can work in the environment dangerous to humans.
Help even to non-experts
An ES can help by serving as a training tool for young employees and non-experts.
Artificial Intelligence & Expert Systems
A short reminder: Artificial Intelligence is the field of Computer Science that is devoted to giving the machines features that associated with human intelligence. These include reasoning, evaluation, learning, language recognition, decision-making and problem solving.
Expert systems were the first successful implication of Artificial Intelligence to the purposes of business.
Their decision-making was rule-based – it consisted of the great number of “if – then” rules.
For instance, “If it is sunny, then I’ll go swimming”, and so on.
Rule-based systems are the simplest form of Artificial Intelligence.
But such approach wasn’t enough for a really powerful, robust ES.
Rule-based decision couldn’t deal with many issues. For example, the systems often failed when faced with a new, not hard-coded situation. It was also challenging to gather expert knowledge (“data acquisition” problem) and construct a knowledge base.
As a result, Expert Systems did not live up to the business-world expectations. For a while, they have sunk into oblivion.
A shift from rule-based approach to a data-driven one paved the way to a new era in Artificial Intelligence
Prior to advancements in AI, there was a serious increase in computing power capabilities.
Also, data became easier to gather and inexpensive to store.
Then, the whole AI paradigm has changed.
Instead of making a system that is attempting to draw logical conclusions based on predefined rules, AI-software began use a data-driven and probability-based approach.
By exposing large quantities of known facts to a learning mechanism, and performing tuning sessions, you get a system can make predictions or identifications of unseen cases. This is approach of constant trial and errors.
That is, in essence, the underlying concepts of Machine Learning.
For Expert Systems, it seems, the tide has turned
If we define an Expert System by its direct use – as a software intended to solve expert-level problems and tasks, rather than by the method of achieving it, we are quite sure they are about to return.
Diagnostic expert system applications continue to be the most popular. One of the example could be IBM’s Watson is better at diagnosing cancer than human doctors.
Also, in more recent years recommendation systems have taken over in recommending products to customers. A notable development was the Netflix contest for movie recommendations which led to a burst of innovation and interest in the area. And this trend will continue.
There is every reason to believe that the recent advancements of Artificial Intelligence technologies would greatly contribute to the further development of expert systems.
Today we can build robust Expert Systems which were only dreamt of several decades ago. It requires large amount of data and as always a team of professionals with substantial expertise in software development and Machine Learning.
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