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Artificial Intelligence in Clinical decision making is medicine's future

By Dr Tarun Lall | PUBLISHED: 05, Jan 2017, 16:37 pm IST | UPDATED: 16, Jan 2017, 21:00 pm IST

Artificial Intelligence in Clinical decision making is medicine's future Clinical scenario no 1-

A doctor is examining a Patient on his electronic medical record. He enters the patients complaints, history, vitals and does the physical examination. The computer guides him to suggested labs. After the lab results have come they are added in the computer. Finally the computer gives the diagnosis. It recommends the treatment accordingly.

Clinical scenario no 2.

A doctor has seen his patient and has prescribed medications to him. Suddenly his computer lights up and alerts the doctor to a possible harmful reaction that the medication could have. The doctor quietly withdraws the medication and prescribes another suitable one. Suddenly his computer lights up and alerts the doctor to a possible harmful reaction that the medication could have. The doctor quietly withdraws the medication and prescribes another suitable one.

What's Artificial Intelligence

The advancement in computer technology has encouraged researchers to develop software for assisting doctors in making decisions.

The software development exploits the potential of human intelligence such as reasoning, making decision, learning (by experiencing) and many others.

Artificial intelligence is not a new concept, yet it has been accepted as a new technology in computer science.

It has been applied in many areas such as education, business, medical and manufacturing. Here we explore the potential of artificial intelligence techniques particularly for web-based medical applications. In most developing countries insufficiency of medical specialists has increased the mortality of patients suffering from various diseases.

The insufficiency of medical specialists will never be overcome within a short period of time. The institutions of higher learning could however, take an immediate action to produce as many doctors as possible. However, while waiting for students to become doctors and the doctors to become specialists, many patients may already die. Current practice for medical treatment required patients to consult specialist for further diagnosis and treatment. Other medical practitioner may not have enough expertise or experience to deal with certain high-risk diseases.

However, the waiting time for treatments normally takes a few days, weeks or even months. By the time the patients see the specialist, the diseases may have already spread out. As most of the high-risk disease could only be cured at the early stage, the patients may have to suffer for the rest of their life. Computer technology could be used to reduce the number of mortality and reduce the waiting time to see the specialist. The softwares are not meant to replace the specialist or doctor, yet it is developed to assist general practitioner and specialist in diagnosing and predicting patient’s condition from certain rules or "experience".

Patient with high-risk factors or symptoms predictive for certain diseases or illness could be short listed to see the specialist for further treatment. Employing the technology especially Artificial Intelligence (AI) techniques in medical applications could reduce the cost, time, human expertise and medical error.

Computer program known as Medical Decision-Support System was designed to help health professionals make clinical decision. The system deals with medical data and knowledge domain in diagnosing a patient's condition as well as recommending suitable treatments for the particular patient.

Patient-Centered Health Information Systems is a patient centered medical information system developed to assist monitoring, managing and interpreting patient’s medical history. In addition the system provides assistance to patient and medical practitioner. The system serves to improve the quality of medical decision-making, increases patient compliance and minimizes iatrogenic disease and medical errors.

Computer technology also helps reducing the cost and time during registration process. Hospital attendance could key in patient’s ID and update patient’s record. Signals are sent to notify the doctor.

While diagnosing the patient, doctor can refer to patient’s history record for a history treatment. A prescription of medicine can automatically be sent to the dispensary. Using the technology, problems in preparing the medicine and drug complication can be avoided. The advancement in computer technology and communication encourages health-care provider to provide health-care over the Internet or telemedicine.

Telemedicine is the integration of telecommunications technologies; information technologies, human-machine interface technology and medical care technologies for the purpose of enhancing health care delivery across space and time define telemedicine as any instance of medical care occurring via the Internet and using real-time video-teleconferencing equipment as well as more specialized medical diagnostic equipment.

In general, telemedicine means, the use of computer and communications technologies to augment the delivery of health-care services. Telemedicine can improve access to care, increase health-care quality and reduce the cost. Patients from rural areas can access to the same quality of health-care as those in big city. As an example patients suffered Heart-attack do not have to consult cardiologist directly. Local doctors or medical practitioners could perform the diagnosis with the help from cardiologist using communication channel such as Internet, telephone line and others. The approach reduces the cost and time for both patients and doctors.

The benefits of the electronic records would be many, namely enhance traditional records, fast storage and retrieval, promote telemedicine and encourage research in medical applications. As many applications are geared toward web-based, this study proposed a model for web-based medical diagnosis and prediction, specifically for medical practitioners. Several artificial intelligence techniques for diagnosis and prediction tasks were explored and identified.

Artificial Intelligence in Medicine

Artificial Intelligence (AI) is a study to emulate human intelligence into computer technology. The potential of AI in medicine has been expressed by a number of researchers. Many intelligent systems have been developed for the purpose of enhancing health-care and provide better health care facility, reduce cost etc.

Intelligent system was developed to assist users (particularly doctors and patients) and provide early diagnosis and prediction to prevent serious illness. Even though the system is equipped with "human" knowledge, the system will never replace human expertise as humans are required to frequently monitor and update the system’s knowledge. Therefore, the role of medical specialist and doctors (or medical practictioner) are important to ensure system validity.

Early studies in intelligent medical system such as MYCIN, CASNET, PIP and Internist-I have shown to outperform manual practice of diagnosis in several disease domain. MYCIN was developed in the early 1970s to diagnose certain antimicrobial infections and recommends drug treatment. It has several facilities such as explanation facilities, knowledge acquisition facilities, teaching facilities and system building facilities.

CASNET (Causal Associational NETworks) was developed in early 1960s is a general tool for building expert system for the diagnosis and treatment of diseases. CASNET major application was the diagnosis and recommendation of treatment for glaucoma.

PIP an abbreviation for Present Illness Program was developed in 1970s to simulate the behaviour of an expert nephrologist in taking the history of the present illness of a patient with underlying renal disease.

The work on Internist-I in early 1982s was concentrated on the investigation of heuristic methods for imposing differential diagnostic task structures on clinical decision making. It was applied in diagnoses of internal medicine. In 1990s, the studies in intelligent system was enhanced to utilize the system based on current needs. In several studies two or more techniques were combined and utilized the function of the system to ensure system performance.

ICHT (An Intelligent Referral System for Primary Child Health Care) developed to reduce children mortality especially in rural areas. The system success in catering common paediatric complaints, taking into consideration the important risk factors such as weight monitoring, immunization, development milestones and nutrition. ICHT utilized expert system in the process of taking history data from patients.

Experience Based Medical Diagnostics System, an interactive medical diagnostic system is accessible through the Internet Case Based Reasoning (CBR) was employed to utilize the specific knowledge of previously experienced and concrete problem or cases. The system can be used by patients to diagnose them without having to make frequent visit to doctors and as well as medical practitioner to extend their knowledge in domain cases (breast cancer).

Data mining is an AI technique for discovery of knowledge in large databases, could be used to collect hidden information for medical purposes. It could also be combined with neural network for classification of fuzzy pattern of HIV and AIDS using unsupervised learning, Patients status life or dead was classified as training and testing pattern. Data mining was also used to generate a scatter diagram and a model of rules statement to enhance current rule base system.

We can develop information system that supports knowledge discovery and mining in medical imaging. Fuzzy logic is another branch of artificial intelligence techniques. It deals with uncertainty in knowledge that simulates human reasoning in incomplete or fuzzy data. We applied fuzzy relational inference in medical diagnosis. It was used within the medical knowledge based system, which is referred to as Clinaid. It deals with diagnostic activity, treatment recommendations and patient’s administration.

Neural Network (NN) is one of the powerful AI techniques that has the capability to learn a set of data and constructs weight matrixes to represent the learning patterns. NN is a network of many simple processors or units. It simulates the function of human brain to performs tasks as human does. As an example, a study on approximation and classification in medicine with incremental neural network shows superior generalization performance compared with other classification models NN has been employed in various medical applications such as coronary artery (Lippmann, 1995), Myocardial Infarction cancer, pneumonia and brain disorders was implemented as a hybrid with textual description method to detect abnormalities within the same images with high accuracy.

Centralized Databases and WWW

To date, most of the systems developed were standalone applications with specific databases for certain diseases. This implies that patient’s information in one system can only be used by that particular system. Another problem with standalone database is that, the database for the same system in another place would differ as the number of patients
using the systems increases. This problem affects the knowledge acquired from the databases and thus influence the decision made by the system.

For system using AI techniques, when the number of patients is high the system will produce more accurate results compared to the system with less number of patients. The patient’s records are valuable information for the knowledge-based system. The current patient’s data would enhance and strengthen the validity of the system reasoning Current enhancements in information technology such as development of information superhighway inevitably encourage many organizations including government to develop electronic medical information and make it available on the Internet. The patients can use the information and monitor their risk level from their home or office without having to consult the physician However, the proposed model do not meant for the patients to monitor their health, rather to assist clinician in making diagnosis and prediction of patient’s illness. This will enable the clinician to access the system and provide the consultation as expert does regardless of the location.

A Patient’s record or patient’s database could be installed at the main server. The electronic record could be accessed by health-care providers and the data could be stored and updated frequently. By using this method, the system knowledge will always be updated. The interface for the interactions between the database (and the system) and the clinician would be through WWW. The Internet supports two-ways communications between users around the world at minimum cost In medical, communication is very important as new information or new discovery is the key for the future survival In addition, communications helps doctors sharing their knowledge or expertise.

As an example, a specialist from Sydney can provide on-line medical assistance to doctor at Kuala Lumpur who is treating a patient that suffers from serious cancer problem. Another doctor from other country such as United Kingdom can share his experience dealing with the same cases. Communications between doctors or specialist from other region helps doctor at Kuala Lumpur diagnosing his patient and provides appropriate treatment. In telemedicine, Multimedia and Internet (or computer network) are two of the main tools that support the collaboration and distribution of information. Multimedia is a combination of media such as text, audio, visual and graphics can be used in medical application such as in image transmission (X-Ray images, pictures and etc.).

Web-Based Medical Diagnosis and Prediction

The proposed model for Web-Based medical diagnosis and prediction consists of four components, they are databases, prediction module, diagnosis module and user interface. The databases consist of patients database and patients-disease database. Patients database will be used to store patient’s information such as name, addresses, and others particulars details. Patients-disease database stored all the information about patients and their illness.

The information stored in the database includes types of diseases, the treatments and other details about the test and administering therapy. Patients information are separated in a different database to enhance the patients records storage, so that other departments could use the records when the patients are referred to them. This method could prevent other departments or unauthorized users from accessing the information about patients diseases and provide a centralized information access for the patients records.

Prediction module and diagnosis module are two of the main features in Web-Based Medical Diagnosis and Prediction. Prediction module utilizes neural networks techniques to predict patients illness or conditions based on the previous similar cases. Data from the patients and patients-disease database will be used for training and testing. The weight from the training will be stored to predict a new data fed into the system. Diagnosis module consists of expert system and fuzzy logic techniques to perform diagnosis tasks.

A set of rules will be defined using the patients and patients-disease databases as well as the expert knowledge on the disease domain. Expert system uses the rules to diagnose patient’s illness based on their current conditions or symptoms. In addition, fuzzy logic is integrated to enhance the reasoning when dealing with fuzzy data. The combination of expert system and fuzzy logic that forms a hybrid (expert-fuzzy) system could increase the system performance. In the proposed model, WWW acts as the user interface for the interaction between the users and the systems. Several processes involve in the models are collection data (patients information and patients illness), diagnosis, prediction and managing databases or systems administering.

Amazing results

The future for medicine will be better and better. The use of computer and communication tools can change the medical practice into a better implementation. Consolidation in health-care provider will happen by focusing on cost and later on quality of services. Advancement in technology will form a platform for development a better design of telemedicine application.

Telephone line and Internet will be the most important tools in medical applications. The main features in medical diagnosis and prediction using artificial intelligence techniques will make the consultation to be more interactive.

As clinical decision making inherently requires reasoning under uncertainty, expert systems and fuzzy logic will be suitable techniques for dealing with partial evidence and with uncertainty regarding the effects of proposed interventions. For the prediction tasks, Neural Networks have been proven to produce better results compared to other techniques (such as statistics) (Such techniques are worth to explore and integrate in the system for medical diagnosis and prediction.

The Internet or the www will be used as the medium to provide tele-healthcare to the clinician or to the public. Centralized databases over the www have many advantages. Information sharing, collaboration between medical practitioners, on-line discussion, on-line treatment and diagnosis are among the main features which enable the doctors from around the world to share their knowledge and expertise. Centralized medical record helps doctors to improve the quality of treatment and provide a better diagnosis based on patients medical history. In addition, researchers in medical applications could use the data in their investigation of a new medical solution, patient’s management and treatment.

  • Writer is Dr Tarun Lall, Professor, Medical Officer Incharge Trauma Centre, OT, SMS Hospital, Jaipur
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