Virtual Telemedicine using Natural Language Processing Imran Sarwar Bajwa Department of Computer Science & IT The Islamia University of Bahawalpur firstname.lastname@example.org Abstract way of getting medical treatment at home. Health care facilities can be improved for a specific community:Conventional telemedicine can be inept due to the existing time children, old people, plague disease, etc. Telemedicineconstraints in response of the medical specialist. One major can become moiré effective in emergency cases and areasreason is that telemedicine based medical facilities are subject of natural disasters. Still, this is cost effective andto the availability of the medical expert and telecommunicationfacilities, when they are required. On the other hand, efficient way of providing high level and skilled medicalcommunication using telecommunication is only possible on facilities to the people living in remote areas , who canfixed and appointed time. Typically, the field of telemedicine easily access the physicians and medical specialist.exists in both medical and telecommunication areas to providemedical facilities over long distance especially in remote areas. 1.1 Conventional TelemedicineIn this paper, a solution ‘virtual telemedicine’ is presented tocope up with the problem of long time constraint that is faced in Telemedicine typically works in two ways : store andconventional telemedicine. Virtual Telemedicine is the use of forward method and real time method. Store and forwardtelemedicine with the methods of artificial intelligence to over method gathers patient’s medical information locally andcome the problems of telemedicine. Virtual medicine uses a then patient query is emailed to a physician. Afterwards,virtual physician that can treat patients anywhere, any time in physician prescribes a treatment and then emails theremote areas as well. Virtual telemedicine can be accessed response of the medical query in 24 to 48 hours. On theonline as well. other hand, in real time telemedicine, video conferencingKeywords – Telemedicine, Telecommunication for health, and live data transmission methods are involved forInformation retrieval, Text Processing, Expert system communication between patient and medical expert. UCD Health system  is one of the examples of video conference based health systems.1. INTRODUCTIONTelecommunication is the most used technology all overthe world in current age and still establishing a long way.This technology has made things to do in an easy and fastmanner. Now enhancements in technology have made ourthoughts to drag fields of life into advance technology.From last few years, alphabet ‘e’ is being used withalmost everything i.e. e-mail, e-learning, e-commerce, e-banking and e-services. The proposal of ‘e-health’ is stillnew and asks for more development. Medical is the fieldthat is emerging continuously to make health facilities Figure-1.1: UCD Health system – Patient side more affective and facilitating. Telemedicine  is theneed of current age to provide health facilities in theremote areas where medical experts, doctors andphysicians are not available. Telemedicine usestelecommunication technology to provide medicaltreatment and services. Telemedicine connects patientswith doctors where distance is a critical factor andexchanges the information of diagnosis, treatment andother health care activities.Telemedicine becomes more significant if the patient isfar away from the medical experts and facestransportation challenges. On the other hand it is helpful Figure-1.2: UCD Health system – Physician side 
In store and forward method is quite approving solution researchers who realized the importance and need of thebut it requires lot of time to get diagnostic results in telemedicine based medical facilities. His studyreturn. Time constraint can be up to 24 to 48 hours. In elaborated the use of store and forward method ofreal time telemedicine, there are so many constraints that medical information transformation. His work alsomake its effective usability difficult. While in countries emphasizes the need of efficient use of the resources tolike Pakistan  where video conferencing is a pricey make the telemedicine based health care system moreclient, real mode is not appropriate solution. Secondly, effective and useful. Albert and Jason conducted twohigh bandwidth is required for data transmission. On the preliminary studies  in year 2007 to examine theother hand, the availability of the medical expert is also performance of remote display protocol (RDP) used inrequired, when the patients need. Virtual telemedicine is telemedicine systems. In first study, RDP was deployedthe process to provide the telemedicine features online in a wide-area network  and in second one, theusing a virtual physician in place of the real doctor. Other performance of RDP was analyzed over Wi-Fi . Theyfamous telemedicine types are home telemedicine and also presented a thin client based home telemedicineindividual telemedicine . architecture that was providing remote training for patients on broadband.1.2. Virtual Telemedicine Dena S.  discussed uses and benefits of telemedicineAs we have discussed in the previous section, that store typically for rural areas in America. She presented thatand forward method is reasonably practicable but the considerable technical, organizational, and financialtime constraint of store this method is not realistic. As obstacles have kept the rural communities deprived ofsome times due to the serious condition of the patient, benefits of the technology. This paper focuses on thesehe/she may not wait for up to 48 hours . Some issues and suggests a feasible solution for establishingintelligent mechanism is required to improve the usability successful rural telemedicine programs. DIABTel and affectivity of the conventional telemedicine process. Telemedicine Service is another telemedicine basedAn intelligent system is required that may provide system that provides daily care to diabetic patients. Majorimmediate response. In conventional telemedicine, an concern of the research was to provide telemonitoring ofadditional component is proposed in this research: virtual patients blood glucose data and also support remote carephysician. Virtual physician is a web-based application from doctors to diabetic patients. Tayab D.  proposed athat answers without delay the medical queries. To make cost effective and multipurpose model of thethis facility more comprehensive, an additional telemedicine system. The proposed system had two majorfunctionality of consultation is also involved. In this parts: a telemedicine unit for the patient side and anotherfacility, if the knowledgebase of the virtual physician base unit for medical expert side. Major issue ofcannot answer a medical query an automatic email is sent discussion was the use of high-speed network forms forto a medical expert and the response of the query is interconnectivity of the complete system. Dena Puskin,updated in the knowledgebase for future queries. Barbara and Stuart presented a framework  of a telehealth system that was able to identify and understandIn this article, the section 2 presents the review of related the interaction between telemedicine services.work done be the various researchers in the field of Exploration of health information technology  (HIT)telemedicine and its applications in different areas of applications on local, regional and national levels was thehealthcare. Section 3 highlights the architecture of the major emphasis of this research.designed medical expert system and the NLP basedalgorithm that process the textual information. Section 4 UC Davis used a telepharmacy program in UCD Healthdescribes the implementation details and the section 5 System  that was based on a video conferencing. Thepresents a case study to elaborate the use of the designed author cites many challenges to telemedicine in the recentsystem and the results of the performed experiments with times i.e. system expertise, imprecise administration,the analysis are also provided in later half of the same contractual organization, etc. Tele-echocardiology  issection. another field of major research in telemedicine. This field of research deals with the real time diagnosis if heart diseases without the support of in-house pediatric2. LITERATURE REVIEW cardiologists. The major emphasis of the research was toField of telemedicine is being proved the technology of evaluate the impact of the telemedicine in providing thethe electronic age. Although the telemedicine was first health care facilities to the cardiac patient in communitytime used in 1959 but major development work was hospitals where cardiac specialists are not availableinitiated in this field for the last 8 to 10 years. frequently.Telemedicine has been used for the e-health solution of In the recent times where wireless technologies arediseases: diabetes, cardiac, trauma, and general physician grasping their roots in other fields of life, at the samerelated diseases. P. Douglas  was one of the earlier
time telemedicine is also getting benefits of it. An transmit patient’s information to the medical expert. Stilladvanced wireless sensor network (WSN)  for health there are important issues like accurate informationmonitoring is introduced by G. Virone in DCS, exchange, security, transmission bandwidth, protocols,University of Virginia. The research presents a proposal data sets etc.‘smart healthcare’ with the benefits of low cost and ad-hoc deployment of model sensors of for an improvedquality of health care. A. Diver  has recentlyintroduced his work to emphasize the significance ofimage analysis as an additional support for assure themodern telemedicine needs. A pilot study based ontwenty patients of trauma has been presented to highlightthe limited plastic surgery experience of a doctor in theserious cases. Some outcomes of the work areintroduction of user-friendly technology, clinicallyappropriate telemedicine applications, well trained andprofessional telemedicine users, etc.3. USED METHODOLOGYVirtual telemedicine is replacing the physician intelemedicine with a virtual physician. Telemedicine isdesigned for remote and rural areas  whereas virtualtelemedicine can be used in both rural and urban areas. Inconventional telemedicine, there are simply two nodes: Figure-3.2: A virtual telemedicine systempatient and doctor. Patient communicates with the doctorthrough some telecommunication medium; telephone, e- The time constraint of conventional telemedicine systemmail, internet, video-conferencing, etc. A simple is typically longer. An idea of virtusal telemedicine hasrepresentation of a conventional telemedicine system has been presented to cover up this time constraint and makebeen shown in figure 3.1. telemedicine more effective and efficient. Virtual telemedicine is the extension of conventional. A new component ‘medical expert system’ has been deployed in the conventional telemedicine system. This medical expert system is a natural language processing based expert system. In this research this expert system has been named ‘Virtual Medical Expert System’. 3.1. Designed System Architecture This virtual medical expert based system is shown in figure 3.2. This system has robust ability of reading the patient’s symptoms and immediately diagnosing the disease and also prescribing the appropriate medication for the patient. A natural language processing (NLP) based medical expert system is the base of the proposed health care system. The designed rule based expert system has following major components . a- Graphical User Interface Figure - 3.1: A simple telemedicine system b- Medical Expert Knowledge baseMajor issues that are concerned with the development of c- Medical Inference enginea conventional telemedicine system can be divided into d- Medical Explanation Modulefour categories . First of all there is need of a. Graphical User Interfaceinfrastructure that is based on hardware, software andconnectivity mechanism of multiple nodes (patient and A graphical user interface is a facility for the user todoctor). On the other hand basic medical equipment is interact with the Expert system. A wizard of forms isrequired at the patient end where a literate person can used to get textual input from the user and then after
processing the textual information the output is shown to • Heuristic Knowledge is typically observed orthe user in the form of reports. pragmatic knowledge. This type of knowledge is extracted from the factual knowledge i.e. “if patientb- Medical Expert Knowledge base has temperature then it can be chest infection”.MEKB is an intelligent knowledge base that uses Markov c. Medical Expert SystemLogics (ML) to save domain knowledge. Markov Logicis simple extension to first-order logic. In Markov Logic, This is another very important part of the designedeach formula has an additional weight fixed with it , in medical expert system. It is the brain of the medicalvariation of first order logic. In ML, a formulas expert system. The major duty if this part is to makeassociated weight reflects the strength of a constraint. logical deductions based upon the extracted knowledgeThe higher weight of a formula represents the greater the from the medical expert knowledge base (MEKB). Thisdifference in log probability and it also satisfies the inference engine not only makes decision but alsoformula. Use of Markov Logic enables intelligent storage extracts new information on the behalf of providedand retrieval of information using logical connectives and information from MEKB. This new information can alsoquantifiers. The benefit of using Markov Logics is that become par of the medical inference engine, if required.the queries which even do match up to 80% will also beanswered as this is not the case in typical knowledgebase d. Medical Explanation Modulethat used production rules. This approach will increasethe response rate of the knowledge base and makes it This is another very important module of the designedmore effective and efficient. system. This module provides the facility of explaining and reasoning of the system to the user. User can make different queries regarding the system domain and Input Text (Patient’s Symptom Report) system. 3.2. Algorithm for Query Processing For diagnosis and treatment of the patient, two techniques Morphological POS Tokenization are used in the proposed system. First and major Analysis Tagging technique to develop virtual telemedicine is “Rule Based Approach” in which is the most efficient way to represent human activity in the form of rules. Used algorithm has Pragmatic Semantic Lexical two major parts. First part has been designed to read the Analysis Analysis Analysis patient’s symptoms of diseases and analyze according to the given knowledge base and diagnose the accurate disease. Second part of the designed algorithm prescribes the suitable medicine to the patient. Following steps are Medical Inference Engine MEKB followed by the algorithm to diagnose a particular disease: Step –I Health care person collects the patient’s disease Diet Details Medication Exercise Details information along with the symptoms of disease and records in the simple English form. Step –II The patient’s case information in the textual Dose form is given to the designed virtual telemedicine system. Side Details Effects Step – III Natural language processing is performed to read the given text and extract the related information. Figure 3.3 - Working of the designed system Used NLP steps to analyze text are : o Tokenization (Separating tokens): The inputMEKB handles two types of rules Factual knowledge and sentence is tokenized into complete wordsHeuristic Knowledge . o Morphology (to identify and analyze morphemes).• Factual Knowledge is the descriptive information. It The input of the previous step is further processed is basic knowledge related to the domain i.e. to identify the complete words and then identify Bacterial causes flue” Or “Dust allergy causes their parts of speech (POS) category. cough”.
o Lexical analysis (to identify grammatical types of based wireless internet work or WiFi system supporting the tokens): The POS tagged words are further speed of 1.0 Gbps or above is required. 3G cellular processed to identify their particular role in the technology is also getting very popular these days  in sentence and grammatical rules also assist this the field of telehealth. This technology can help out in type of analysis. fast video sharing, video male and video conferencing. On the other side, a telemedicine center at the remote o Semantic analysis (To understand the meanings of area needs basic eequipments  i.e. the sentences): Different constituents of a sentence are analyzed here to extract the both implicit and • Virtual telemedicine software explicit meanings of the input text. • Camera (s), lights, projector o Pragmatic analysis (to find out meanings in a • Digital X-Ray System particular context): This is an additional step that • UPS system is used if the meanings of the input text are not • Computer hardware, system and application software clear it is analyzed into its particular context to and accessories make the things more clear and concise.Step –IV Pattern matching of the extracted information is 5. EXPERIMENTS AND RESULTSperformed through the medical inference engine with theinformation in medical expert knowledge base to find out A number of experiments were performed to test thepatient’s disease. designed health care system. A medical assistant wasStep –V If a match is found then treatment of the disease involved to use the system. A multiple step procedure isis recommended with the appropriate medication and involved to use the designed medical health care system.health instructions. If match is not found then the query The steps are following:is forwarded to the medical expert. 1. Patient RegistrationIn a case, if the virtual medical expert does not find any 2. Patient Record File Generationparticular solution of the patient’s query from its 3. Processing User Detailsknowledge base, rather related to the disease diagnosing 4. Generating Patient Reportor prescribing medicine, an automatic email is sent to themedical expert. Medical expert examines the query with Brief description of all these phases with the help of aavailable facts and makes some decisions and replies the case study has been provided in the later part of thelocal medical assistant. The designed system also updates section.the Medical Expert Knowledge Base (MEKB) so that if 5.1. Patient Registrationthe same query comes in future, it may be resolvedlocally. A patient is needed to register with his personal details i.e. name, age, sex, address, family history, previous cases, etc for using the proposed virtual telemedicine4. IMPLEMENTATION DETAILS system. Figure 4.1 shows the form that is used to registerRural areas of Pakistan  are relatively backward in the patient first.terms of technology. A number of challenges are to facein setting up a system for virtual telemedicine. Some ofthe major challenges are following:• Budget and financial constraints are more significant . First of all expensive medical equipments are required at the telemedicine centers. High bandwidth for communication is also an expensive solution.• At the site, adequate human resources are required  i.e. technicians to implement the proposed virtual Telemedicine system, a medical assistant having medical training to perform basic tests of the patients and some health workers having basic literacy of computer and capable of using computers.A complete infrastructure is required to actually set up Figure 5.1 – Form to register a patientthe proposed virtual telemedicine framework. A satellite
5.2. Patient Record File Generation SAfter registration, the medical expert performs basic testsof a patient to get the reading of temperature, bloodpressure, blood group, sugar level and ESG (if required). NP VPThen he records the common symptoms of the patient inthe system. Besides these tests, the data i.e. color of Det. Verb NP Nountongue, color of eyes, heart beat, face color, etc is alsocaptured and is updated in the system. The data form isshown in the figure 4.2 The patient H.V. Adej Noun has high fever Figure 1.0- Parse tree generated for the example There are two rationales for performing the syntactic analysis; to validate the phrases and sentence according to grammatical rules defined by the English language and finding out the semantical constituents of natural language. Moreover, the semantical analysis helps in identifying the main parts of a sentence i.e. object, subject, actions, attributes, etc. [The] [patient] [has] [high] [fever] [.] Subject Verb Object In this step, associations are identified by doing semantic Figure 5.2 – Form to update patient’s status analysis. It is determined in this specified that which actions have been performed by which object and a set ofMedical assistant can also use digital stethoscope and attributes belong to which object e.g. in the aboveelectrocardiograph file with ECG recorder or images with example it is extracted that a person is having a highthe examination camera . A text file containing the fever.patient’s case details is prepared. 5.4. Generating Patient Report5.3. Processing User Details Afterwards, the extracted information of the last phase isThe input text file containing the patient’s history and matched with the knowledge in MEKB. Inference enginesymptoms is given to the designed system foe processing. extracts the desired information and processes theIn first step, input is read and tokenized e.g. the output of patient’s symptoms to infer the disease. If disease isa sentence “The patient has high fever.” is found then the respective medication of the disease and additional information i.e. diet and exercise details are [The] [patient] [has] [high] [fever] [.] also provided. The designed system will not only provide the treatment strategy of the disease but also recommendsAfter tokenizing the text, morphological analysis is tests if necessary for the confirmation of the disease. Ifperformed of given text to define the structuring and the tests were recommended by the system to the patient,transformation of the words. POS Tagging is also patient/administrator will have to provide the results ofperformed to identify different parts of speech e.g. the tests to the system so that system may recommend the [The] [patient] [has] [high] [fever] [.] right treatment of the disease. If the system is not able to answer the patient then an automatic e-mail will be forwarded to the medical expert. The medical expert willDeterminer Noun Verb adjective Noun carefully examine the case by consulting all the test reports and data sent by the local medical assistant andAfter POS tagging, the text is lexically and syntactically diagnosis the disease and also prescribes the appropriateanalyzed and a parse tree is generated for semantic medication. When the medical assistant receives theanalysis. Figure 5.3 shows the generated parse tree of the response, the medical expert’s opinion is also updated inabove example. the knowledge-base of the system.
A prescription will be generated after the patient’s data is usability of the Virtual telemedicine will be more usefulsubmitted. If the knowledge base cannot reply then the and beneficial. The experiments were performed on apatient’s data will be emailed to expert. The correctness simulator and it is acceptable that these results may varyof the decision made by the software and the medical when the system will be run real time. In futureexpert is based on the accuracy of the data captured by enhancements the algorithms is needed to be improved tothe medical assistant. The quality and accurateness of the increase the accuracy level of the system. Medicalimages and video of the patient is also quite important. explanation module is also needed to enhance its usability.To validate the precision and affectivity of the designedsystem symptom reports of three groups of ten patients Referenceswere defined. For each group three reports i.e. easy,average an difficult were generated for each group. The  M. Z. Khalid, A. Akbar, A. Kumar , A. Tariq, M. Farooq,symptom reports were carefully prepared and processed  “Using Telemedicine as an Enabler for Antenatal Carefor each patient using the designed health care system. in Pakistan”, Proc. 2nd International Conference: E-MedicalFor correct and wrong diagnosis of a symptom report System, Oct 2008, Tunisia, pp 1-8various points were given. Table 5.1 shows the details of  Tayab Din Memon, BS Chowdhry, AK Baloch, “Design andthe results. Implementation of a Telecardiologic System”, MUET, Research Journal, Volume 23, No. 4, Oct 2004. Group 1 Group 2 Group 3 Total %  William R., David H., Susan M., Tracy L., Kathryn Pyle,Easy 10/10 9/10 9/10 2.8 93.33 Mark Helfand,  “Telemedicine for the Medicare Population: Update”, AHRQ publication No. 06-E007Average 9/10 8/10 9/10 2.6 86.66Difficult 7/10 8/10 8/10 2.3 76.66  Thomas S. Nesbitt,  “Meeting the Health Care Needs of California’s Children: The Role of Telemedicine”, Digital Average Accuracy: 85.5% Opportunity for Youth Issue Brief, Number 3: September 2007Table 5.1 Virtual Telemedicine Based Healthcare System  Albert M. Lai, Jason Nieh, Justin B.,  “REPETE2: AFollowing are some benefits over using the proposed Next Generation Home Telemedicine Architecture”, AMIAframework virtual telemedicine. 2007 Symposium Proceedings, pp 1020-1022• Improved and immediate access the specialty care  Lai AM and Nieh J.,  “On the Performance of Wide-• Upgraded emergency medical services Area Thin-Client Computing”, ACM Transaction on Computer Systems. May 2006, p. 215-209• Reduction in un-necessary duplication of services• Less dependency on the medical expert . Lai AM and Nieh J.,  “Web Content Delivery Using• Easier diagnostic consultation Thin-Client Computing”, In: Chanson ST, Xu TJ, editors.• Expanded disease cure education Web Content Delivery. Springer; 2005. pp. 325-346• More patient health queries• Remote medical consultation  Dena Puskin, Barbara and Stuart,  “Telemedicine,• Reduction in health care cost Telehealth, and Health Information Technology”, An ATA Issue Paper, The American Telemedicine Association, May• Automated patient record keeping 20066. CONCLUSION & FUTURE WORK  Dena S. Puskin,  “Opportunities and challenges to telemedicine in rural America”, Journal of Medical Systems,Virtual Telemedicine is the new concept which actually Volume 19, Number 1 / February, 1995, pp 59-67works faster than that of the traditional telemedicinesystems. An expert system has been deployed in place of  E. J. Gomez, F. Del Pozo, M. Hernando, “Telemedicine fora medical expert that has ability to immediate respond. diabetes care: The DIABTel approach towards diabetes telecare”, Informatics for Health and Social Care, VolumeThis immediate response can help to treat patients in time 21, Issue 4 October 1996 , pages 283 – 295and more effectively. 90% queries can be entertainedlocally. The accuracy achieved with the designed system  D A Perednia and A. Allen, “Telemedicine Technology andis 85.5%. The Virtual expert system becomes more robust Clinical Applications”, The Journal of the American Medicaland intelligent with the passage of time as the Association, 273(6), 8 Feb 1995, pp. 483–88.knowledge-base grows and the level accuracy will alsoimprove. For the under developed and developingcountries like Pakistan, Bangladesh, Sri Lanka etc the
 C A Sable, et al., “Impact of Telemedicine on the Practice Report”, Telemedicine Journal and e-Health, Vol 8, No 2, of Pediatric Cardiology in Community Hospitals”, Pediatrics, 2002. January 2002, Vol. 109, No. 1 pp. e3. Rashid E, Ishtiaq O, Gilani S, et al. “Comparison of store and forward method of teledermatology with face-to-face consultation” J Ayub Med Coll Abbottabad 2003 Apr-Jun;15 (2):34-6. J. Jiehui, Z. Jing,  ‘Remote patient monitoring system for China.’ IEEE Potentials, Vol. 26, Issue 3, pp 26- 29, IEEE, May/June 2007. Bajwa I., Choudhary I.  “A Rule Based Paradigm for Speech Language Context Understanding”, J Donghua University (English Edition) Jun 2006, 23(6), pp.39-42 Hersh W, Helfand M, Wallace J,  “A systematic review of the efficacy of telemedicine for making diagnostic and management decisions”, J Telemed Telecare 8(4):197- 209. Andrew J Diver, Harry Lewis, Derek J Gordon, (2009), “Telemedicine and Trauma Referrals – a Plastic Surgery Pilot Project”, Ulster Med J 2009 78(2), pp. 113-114 Demartines N, Otto U, Mutter D, Labler L, van Weymarn A Vix M, et al. “An evaluation of telemedicine in surgery: tele-diagnosis compared with direct diagnosis” Arch Surg 2000 135(7), pp. 849-53. Shahbaz F., Maqbool F., Razzaq S., Irfan K., Zia T.,  “The Role of Medical Expert Systems in Pakistan”, World Academy of Science, Engineering and Technology 2008 Vol. 37 pp. 296-298 Whitten PS, Mair FS, Haycox A, May CR, Williams TL, Hellmich S. et al. “Systematic review of cost effectiveness studies of telemedicine interventions” BMJ 2002; 324(7351) pp. 1434-1437. Yellowlees P. “Successful development of telemedicine systems – seven core principles” J Telemed Telecare 1997; 3(4), pp. 215-222. M P Cutchin, “Virtual Medical Geographies: Conceptualizing Telemedicine and Regionalization”, Progress in Human Geography, 26, 1, 2002, pp. 19–39 G Zahlman and S Laxminarayan, “Special Issue on Telemedical Systems, Guest Editorial”, IEEE Transactions on Information Technology in Biomedicine, Vol 3, Number 2, June 1999.L Kun (1998), “Biomedical Information Technology: Opportunities for the Future”, Proceedings ITAB ‘98, Washington, DC, (Ed S Laxminarayan and E M Tzanakou), IEEE Press. A Lacroix, et al., “International Concerted Action on Collaboration in Telemedicine: Recommendations of the G-8 Global Healthcare Applications Subproject-4, Special