Download Towards Smart E-Health Sensor Networks

Sensor Networks for E-Health: A Survey
Rabie A. Ramadan, Ahmed Y. Khedr, Alaa Hamouda, Ashraf Almrakby
Systems & Computer Department
Al-Azhar University in Cairo
Cairo, Egypt
Abstract
Health care – or sometimes referred to as E-health- has recently been a hot topic for
researchers in computer science. The availability of new sensing technologies helps
develop different types of health care applications and raised the concept of e-medicine as
well. This paper surveys the state of the art of health care sensing technologies, devices,
and applications. In addition, the use of expert systems in health care is presented. This
includes diagnosis, suggestion for treatment, and prediction possible effects of different
therapeutic interventions.
Keywords
Heath care, E-health, E-medicine, sensor network, health management, patient monitoring.
1. Introduction
Health care is an essential topic that includes many issues. Some of these issues are
related to the patient health and his/her protection, others are related to the patient
information, and some others are associated to the used technology. In fact, these issues
cannot be totally separated. Patient health is the responsibility of the hospital staff that
they might depend on smart software and hardware for diagnosis as well as monitoring the
patient’s health; Artificial Intelligence (AI) and expert systems play major roles in such
cases which is included as a core of many of the health care applications. At the same
time, patient information is kept secure on a computer at the hospital. Staffs are the only
persons that can access such information.
As can be seen from the previous scenario, there are many restrictions on such
approach of handling patients’ information. For instance, if a patient in a critical health
condition is admitted to another hospital that does not have his/her health record; such
information might cost the patient his/her life. Nowadays, the advances of the sensing
technology and networking lead to a revolution in the health care application. Electronic
health (E-health) is named after the technology took place in the health applications.
Patients are not required to stay at hospitals for continuous monitoring any more. They can
stay at their comfort zones and their status is continuously reported to the hospitals where
staff can see such information. Small but smart sensing devices such as pressure sensors
play a critical rule in such case.
Throughout this paper, we give the reader the state of the art in the e-health technology
and applications. Up to our knowledge, this is the first work that surveys the sensing
technology along with e-health applications. We think that this survey will be beneficial to
all of the researchers that are working on sensors, sensor networks, and e-health. It is the
also, the first step towards deep investigation in e-health solutions.
The paper is organized as follows: Health Care Medical and Sensing Technology are
investigated in the next section. Section 3 looks at some of the current sensors prototypes
as well as commercial sensors that are used in the field of e-health. Section 4 presents the
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up-to-date e-health applications; some of these applications are very simple; others are
very complicated in terms in sensing and networking. The current E-Health expert
systems are categorized for the benefit of the reader in section 5. Section 6 gives many
open problems in e-health. Finally, the paper concludes in section 7.
2. Health Care Medical and Sensing Technology
Sensing technology has come a long way. Many devices have been produced in the
last few years due to the high demands on such technologies as well as their applications.
For instance, there are many new sensors for location identification, e.g. GSM cell and
Wi-Fi, proximity, e.g. RFID tags, movement, e.g. movement sensors, and health sensors,
e.g. basic vital signs and glucose. A sensor is used to measure the physical environment
such as temperature, pressure, glucose level, heart rate, etc. The signal measured by the
sensor is transferred to the processing unit via I/O ports. After processing, and recording
these signals, they can be sent to a remote server. Some devices can display this
information to the patient via LCD displays.
In this section, we provide the reader with the state of the art sensing technology and
sensors in the field of e-health including the commercial sensors. Such survey is intended
to provide the functionalities of each sensing device as well as its applicability in fitting a
real life sensor networks.
2.1. Sensors Architecture
Sensors are usually consists of four main units in addition to the two optional units
which are mobility support and location finding units. The four main units are, as shown
in Figure 1, sensing, processing, communication, and power units.
Figure 1: Sensors Units
Sensing Unit
A sensor is used to convert a physical parameter into electrical signal. Most of these
signals are analog and week signals. Therefore, the main components of the sensing unit
are the sensors which differ from application to another. The second component is the
analog to digital (ADC) converter. Depending on the application a variety of sensors are
available. These sensors include but not limited to [12]:
• an ECG (electrocardiogram) sensor for monitoring heart activity
• an EMG (electromyography) sensor for monitoring muscle activity
• an EEG (electroencephalography) sensor for monitoring brain electrical activity
• a blood pressure sensor
• a tilt sensor for monitoring trunk position
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• a breathing sensor for monitoring respiration
• movement sensors used to estimate user's activity
• a "smart sock" sensor or a sensor equipped shoe insole used to delineate phases of
individual steps
A common requirement for these sensors is that they must be small and light. Most of
sensors are passive elements which indicate no need for power. However in the case of the
need of power another restriction is added to these sensors. The power consumption of
these sensors must be as small as possible.
Processing Unit
The processing unit is considered as the main core for sensor device. Its function is to
process data and control all other modules. From many processing devices such as
microprocessors, microcontrollers, DSPs, and FPGA, microcontrollers (MCUs) are
considered the most common devices used in health care sensors. Microcontrollers
represent the integration of many functions in a single small, cheap, and low power chip.
In addition, the producing of specific microcontrollers for health care applications makes
them more suitable for this task. Microcontrollers used in health care are ultra low power
devices which lengthen battery life. They contain some special peripherals in addition to
the normal peripherals contained in normal microcontrollers. A precise ADC (analog to
digital converter) is an important component which receives the sensor signal. LCD
driver can be used to drive LCD display to show information and measurements. DACs,
timers, and counters are normal components contained in most microcontrollers. Also
integrated flash and RAM memory are important to store data and programs. Interfaces
such as I2C, and SPI are used to easily connect microcontrollers to other components.
Wireless connection can be established via RF converter contained in some health care
microcontrollers.
Sensors storage as a part of the processing unit is usually built in flash and RAM
memories. However in many cases they are not enough for storing programs and data.
External memories are needed in these cases to compensate small memory capacities.
Between many memory technologies, flash memory is considered as an optimum selection
for health care sensor devices. Flash is light, compact, energy-efficient, and ever less
expensive. There are two kinds of flash namely: linear flash and ATA flash. Linear flash,
as the name implies, is laid out and addressed linearly, in blocks. The same address always
maps to the same physical block of memory, and the chips and modules contain only
memory plus address decoding and buffer circuits. This makes them simple, cheap, and
energy-efficient. Linear flash is the obvious choice for nonvolatile memory that's built
permanently into an embedded system. ATA flash memory appears as if it was sectors on
a hard disk and is accessed via the same register interface used by the original IBM
PC/AT's hard disk controller (and, more recently, IDE disk drives).
The main advantages of ATA flash, from the embedded system developer's
perspective, are flexibility and interchangeability with hard disks. Flash memory offerings
vary widely in capacity, price, speed, and features. This makes designing with flash a nontrivial exercise; the effective embedded system designer must know the full range of
available products in order to choose a cost-effective solution. One of the first tradeoffs
the designer must consider-as with all embedded systems components-is the inevitable
compromise between power and speed. Some flash memories can run at lower voltages
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(as low as 3V or less), which saves power, but works more slowly. Others run at higher
speeds but require five or even 12V.
Communication Unit
Communication module is a vital component in health care sensor to send data to a
server to monitor data, store information, or take an action. This server can be mobile
phone, laptop, internet, or a hospital. Many technologies are available with different
characteristics of power, communication distances, bandwidths, and transfer rates.
Bluetooth technology has a range of 100 meters and not necessarily is in sight and the
power used by Bluetooth is very less. The nearest competitor of Bluetooth is infrared.
Infrared has many additional features but it loses on one point with Bluetooth, as infrared
rays cannot pass through the walls and other obstacles. Infrared technology is up in rate of
data, Bluetooth has 1MBps whereas infrared has 4 MBps. Infrared is faster than Bluetooth
technology. Wi-Fi uses RF waves to exchange data; however Wi-Fi has a larger range
than blue tooth. Communicating to a distant server such as hospitals requires another
technology. GSM is the best solution for this task.
Power Unit
Many technologies are being focused on how to operated devices with reduced power
consumption, but at the same time battery technologies need to catch up with application
requirements. There is certainly no shortage of battery- and chemistry-related
technologies, ranging from regular lithium-ion batteries to portable rechargeable batteries
to fuel cells. Portable rechargeable cell chemistries include Alkaline, Nickel Cadmium
(NiCd), Nickel Metal Hydride (NiMH), and Lithium Ion (Li-ion). The Li-ion cells have
the highest energy density by both weight and volume. With the appropriate level of safety
designed into a Li-ion pack, Li-ion offers the most attractive method of portable battery
power.
3. Heath Mentoring Sensors
Before describing some of the sensors applications in the field of health monitoring, it
is appropriate to look at the current sensing devices that are used in this field. The
following are some of the commercial devices that used separately or part of an
application.
Pocket PC
Smart Pocket PCs play an important role in different application. Since they are
always carried by a human, it can be used to help people in many different directions. The
usage of the Pocket PC in e-health could be essential in applications such as cutting
smoking. One of the real example is “ My last Cigarette” project [20]; such project , as
shown in Figure 2, displays nicotine level readout , expected cravings readout , daily
motivational quote or medical fact, deaths since you quit readout , daily motivational
message, and many other features. These readings are extracted through a simple nicotine
patch that is connected to the pocket PC holder. It is really impressive how such device
can help saving his/her holder life since the holder could be dying for data; the reader is
referred to this story to realize the importance of keeping the medical information along
with the patient
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Pocket PC
My last Cigarette Software
Figure 2: Pocket PC [2]
Medical Alerts and Recording Devices
Nowadays, human accessories became important medical devices. For instance, the
simple bracelets or necklaces, shown in Figure 3, could hold the holder person’s medical
information in forms like RFID tag, barcode, or the patient ID is just engraved on the back
of it. One of the real applications is the CADEX watch [21]. This watch is used to save the
patient critical information such as his/her hospital, ID, and/or insurance information. It
can also set different alerts for different medications and times.
Figure 3: Simple medical alerts and recording devices [3]
Blood Pressure/Pulse Monitors
Another device, as shown in Figure 4 that can help in early detection of patients’ heart
problems is the blood pressure reader or pulse monitor device. Such device is designed
with high accuracy and error detection techniques as well as fuzzy logic measurements. In
addition, it contains a memory for keeping the measurements history for some time. It is
obvious that such device can give an indication to his/her holder by the current situation
especially elder people.
Figure 4: Blood pressure monitoring device [4]
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Wearable Insulin Pumps
Persons with Diabetes are given special care due to the criticality of their cases. A
wearable insulin pump device is invited especially for them, e.g. see Figure 5. Such
device has a catheter at the end of the insulin pump that is inserted through a needle into
the abdominal fat of a person with diabetes. Dosage instructions are entered into the
pump's small computer and the right amount of insulin is injected in a controlled manner.
It will be more beneficial of such device can transfer the patient’s information to its
treatment hospital at the same time to keep his/her record updated.
Figure 5: Wearable insulin pump device
AMON - Advanced Telemedical Monitor
AMON is wireless monitoring system that is described at [5]. The system includes a
wrist-mounted Monitoring Device (WMD), as shown in Figure 6, with different sensors
such as heart rate, heart rhythm, 2-lead ECG, blood pressure, O2 blood saturation, skin
perspiration and body temperature sensors. The device is a part of a system that uses these
advanced bio-sensors to gather vital information, analyze it automatically using a built-in
expert system, and transmit the data to a remote telemedicine centre, for analysis and
emergency care, using GSM/UMTS cellular infrastructure.
Figure 6: The wrist-mounted Monitoring Device
The “Digital Plaster”
The digital plaster shown in Figure 7 is a device meant to be embedded in ordinary
plaster that includes sensors for monitoring health-related metadata such as blood
pressure, temperature and glucose levels. The “digital plaster” contains a Sensium silicon
chip, powered by a small battery, which sends data via a cell phone or PDA to a central
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computer database. If the results show any worrisome signs, patients and doctors alike
would be notified of the change in the data patterns. This also planned to add a motion
sensor to the device so it could additionally serve in the role of “granny monitor” by
detecting things like falls or complete inactivity.
Figure 7: Digital plaster device [6]
4. Health Care Applications/Prototypes
In this section, we will survey some of the current e-health applications. Some of
these applications are very simple to implement. However, many others are complicated
since they combine different types of networks and sensing devices.
4.1 Baby Care - Sleep Safe
Numerous studies have found a higher incidence of Sudden Infant Death Syndrome
(SIDS) among babies placed on their stomachs to sleep [8][9]. Stomach sleeping puts
pressure on a child's jaw, therefore narrowing the airway and hampering breathing. A
simple prototype (called SleepSafe) [7] detects the sleeping position of the infant. It alerts
the parents when the infant is detected to be lying on its stomach, offering them peace of
mind without having to constantly watch their child while it sleeps. This prototype
architecture is shown in Figure 8(a).
Figure 8: SleepSafe baby monitor for detecting infant sleeping position [7]
The sensor mote attached to the infant’s clothing is a SHIMMER mote [14]. This mote has
a 3-axis accelerometer; a single axis is used to sense the infant’s position relative to
gravity. Three discrete positions (back, side, and stomach) are measured as anti-parallel,
perpendicular, and parallel to the force of gravity. Figure 8(b) illustrates how these
positions are measured relative to gravity.
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4.2. Baby Care - Baby Glove
As the weight of children decreases, the mortality rate increases [10]. Many of these
statistics are due in part to their extreme sensitivity to temperature fluctuations, which
must stay within a consistent range of 36◦C to 38◦C. An integrated health monitoring
device has been developed to closely monitor vitals, contained within a swaddling baby
wrap (called The Baby Glove) [7].
Fig. 9 The Baby Glove prototype [7]
The Baby Glove prototype, as seen in Figure 9, includes two sensor network motes, one
connected to the swaddling wrap and the other to a base station computer. The first mote,
connected to the wrap, is a SHIMMER mote. It monitors the vital information coming
from the sensors via an ADC, organizes the measurements into packets and transmits them
wirelessly to the second mote, connected to the base station computer, for processing.
4.3. LISTSENse
Hearing impaired people represent a growing percentage of the nation’s population
[11]. LISTENse is a prototype that gives the deaf people with the perception ability of
critical audible information in their environment (e.g. doorbell, smoke alarm, crying
child.) It is comprised of at least two wireless sensor network motes. User carries one
mote – the Base Station – on his wrist, belt, etc. and each of the other motes – the
Transmitters – is placed close to the sound source that it is to be “heard.” Figure 10(a)
shows a simple communication scheme of the LISTENse basic network. Once the
measured signal surpasses the reference value, an encrypted activation message is sent to
the Base Station. As soon as the Base Station receives the activation message, it extracts
the Transmitter address, turns on the vibrator and lights up the corresponding LEDs to
warn the user. Figures 10(b) and 10(c) shows the manufactured Base Station and
Transmitter prototype[7].
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Figure 10 LISTENse prototype [7]
4.4. CodeBlue
CodeBlue is a working prototype of a wireless, web-enabled, health monitoring
system. CodeBlue consists of three functional components: a wearable unit, a base station,
and a web server. Figure 11 illustrates the CodeBlue software platform.
Figure 11: CodeBlue architecture for emergency response[15].
The wearable unit collects the patient’s physiological parameters via sensors attached
to the patient’s body. It then transmits this data wirelessly to a base station where it is
analyzed and stored. The base station automatically contacts the patient’s nurse and/or
paramedics when measured value surpass abnormal. The web server component allows
medical professionals to remotely access the patient’s physiological data over the Internet,
and provide feedback to the patient by phone or text-messaging [15][16][17].
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4.5. Smart Home Care
Smart homecare may assist residents by providing memory enhancement, medical data
lookup, and emergency communication. The data collected from automatic monitoring via
wireless sensor network can be stored and integrated into a health record of each patient,
which helps physicians make more informed diagnoses. Also, quickly notifying doctors of
any changes in vital signals may save human lives [18][19].
Figure 12. Smart home and personalized health monitoring architecture
Figure 12 illustrates the architecture of the smart home care system in [13][13]. It
consists of two parts namely the smart phone application and the healthcare centre server.
The smart phone communicates with the home server via WiFi or cable. The home server
runs software to control the webcams and controls the information exchange with the
health care server via an ADSL connection.
5. Health Care and Expert Systems
Expert systems play a great role in health care field. It depends on knowledge
representation and inference engines. Two different approaches to represent knowledge in
expert systems have been followed. In the first approach, diseases and clinical,
physiological, or pathophysiological states are each characterized by a stored pattern of
anticipated findings to be matched with the clinical findings observed in the patient. These
patterns, referred to as nodes or frames, are interconnected by links representing various
relations and dependencies. Such networks of nodes and frames are excellent to represent
hierarchical knowledge structures. Newer programs (second-generation programs) are
employing networks where the use of causal pathophysiological knowledge is
emphasized. A typical feature of these programs is a taxonomic structuring of the nodes
and links that allows diagnostic and other types of reasoning to proceed at various levels
of detail.
The second approach is based on a representation of the knowledge in the form of
rules (production rules, IF-THEN rules), which are applied in an orderly manner to
produce the solution of a diagnostic problem.
A production rule consists of a set of preconditions, referred to as the premise, and an
action part. If the premise is true, the conclusion in the action part is justified. A prominent
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example of a system based on the production rule formalism is the MYCIN system [22],
which provides consultation about infectious disease, diagnosis, and choice of therapy.
Each conclusion includes a certainty factor (CF) ranging from 1 (complete belief) via
0 (nothing known) to -1 (complete disbelief), and each assertation is associated with a CF.
If the CF on a premise is positive above a certain threshold (0.2), the corresponding
conclusion is drawn with a certainty that is equal to the premise’s CF times the
conclusion’s own CF. Evidence confirming a hypothesis is collected separately from that
which disconfirms it, and the truth of a hypothesis is the sum of the evidence for and
against the same hypothesis. MYCIN knows that its goal is to undertake a number of tasks
such as (e.g.) to determine if there is a substantial infection in the patient.
To accomplish a goal, MYCIN evaluates all rules relevant for this goal. This creates a
need to evaluate the premises of these rules, and these then become new subgoals, which
are treated in the same way-i.e., the rules relevant for these subgoals are evaluated. When
no rules are found that apply to a subgoal, the user is asked to supply the needed
information. This way of searching for a solution is referred to as “backwards chaining”.
A “forward chaining” or “data driven” approach is used when the patient’s data are
entered without guidance by the computer. Those rules whose premises match the data are
then applied, and new rules that use the conclusions in their premise conditions are
subsequently applied, etc. Instead of using one of the two strategies, it is also possible to
combine them into a mixed strategy.
5.1. E-health Expert Systems Classification
Table 1 lists some examples of expert systems. The majority of systems are diagnostic
systems that output a diagnosis and, possibly, suggestions for treatment. In addition to
diagnoses, some systems such as the ABEL [23] make predictions, e.g., about possible
effects of different therapeutic interventions. Of particular relevance for the clinical
pathologist is the planning involved in the patient workup, because this has a bearing on
the efficient utilization of the laboratory services.
PHEO-ArFENDING is an expert system that assists the physician in this planning
[30]. It is designed to critique a physician’s workup of a patient with suspected
pheochromocytoma. In contrast to more traditional systems, this system first asks the
physician to describe his (or her) patient and to outline the approach planned. The system
then critiques that plan to help the physician make the workup as rational and efficient as
possible. A recommended sequence of work-up is built into the system’s knowledge base,
which is organized as expressive frames associated with each test or procedure. Each
frame contains a list of comments that may be output in discussing the use of the test or
procedure. Each comment has an associated condition that indicates when it is to be output
as part of the critique. For instance, if a CT scan is ordered without prior screening tests, a
comment in the CT scan frame suggests that these should be ordered first and that a CT
scan is not indicated. The various comments generated by activating the various frames
during a consultation are combined into a smooth narrative text, which is then output as a
final critique.
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Table 1: Some Expert Systems Emphasizing the Use of Laboratory Data
System reference
ABEL
ANEMIA
Consult-I
PAThFINDER
EMYCIN
RED
PHEOAUENDING
EXPERT
PRO.M.O.
LIThOS
EXPERT
LIVER
SMR
Domain
Acid-base, electrolytes
Anemia
Anemia
Lymph-node histopathology
Leukemia
Erythrocyte antibodies
Pheochromo-cytoma
Serum proteins
Lipoprotein metabolism
x-ray anal. of renalstones
Outpatient testing
100 diseases
Multiple
Use
Reference no.
Diagnosis, prediction
Diagnosis
Diagnosis
Diagnosis
Diagnosis
Diagnosis
Critique of patient work-up
Diagnosis
Diagnosis
Diagnosis (of stone content)
Diagnosis, planning test requests
Diagnosis
Diagnosis, interpretive comments
[23]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
[34]
[35]
[36]
Van Lente et al. [34] used the EXPERT system [24] for sequential laboratory testing
and interpretation in an outpatient setting. Abnormalities in the initial test profile initiate a
sequential laboratory testing of the specimen already collected. The incremental charge to
a patient evaluated by this sequential testing program was only slightly more than the
phlebotomy charge that would be incurred if one additional specimen were required for
the physician to investigate an abnormality independently. Some expert systems have been
limited to instruments.
Weiss and Kulikowski used the EXPERT building tool [24] to construct a system that
interprets serum electrophoresis patterns [31]. This was later incorporated in a
densitometer. Wulkan and Leijnse [33] reported a system in which a PC connected to an
x-ray diffraction analyzer system preprocesses the diffraction data on-line and stores
selected data in a LISP format. These data are subsequently interpreted by a rule-based
expert system LIThOS, which reports the components and relative content of renal stones.
The system was developed because of a shortage of the specialists needed to interpret
diffractograms.
5.2. Online Community with Ask the Expert System
There is an increasing number of online health communities on the Internet for people
with different health conditions [37][38][38]. Studies have shown that patients who
interact online with people who have similar health problems benefit from this [39]. Some
of the health-communities are for people who face problems caused by their life-style,
who want to change behavior. Empathy and advice are communicated, and to some extent
are also questions concerning ideas and beliefs raised [40][41]. Examples of healthcommunities for lifestyle problems are communities for people, who try to quit smoking,
lose weight or give up drugs or alcohol.
In online communities people can think together, ask questions, guide each other, and
share ideas and insights [42]. Through interaction with others, new ideas and knowledge
can be developed [43], and we are able to understand our situation better [42].
Learning is always a challenge. Especially challenging is learning how to change
behavior, to unlearn a bad habit and to develop a healthier way of living.
Another type of online system used for gaining new knowledge and to get help in
developing new practices is the so-called Ask the Expert systems. They facilitate learning
through giving recommendations, advice, etc. from a medical expert [44], and they offer a
new type of continuous relationships between patients and medical experts [45]. During
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recent years, studies have been conducted of the usage of different knowledge
management tools, such as online communities for different purposes.
5.3. Health Care Management
The goal of health care management is to ensure that high quality patient care is
delivered in the most affordable way possible. Health care management attempts to
achieve this goal by stressing the importance of preventive care and trying to reduce the
over-utilization of surgery and hospital admissions. Another important component of this
care is medical case management, in which potentially catastrophic or chronic cases are
given special attention. Health care management services are delivered by a team of
medical review specialists, case managers, and physicians. The medical review specialists
and case managers are registered nurses by training. The health care management
providers combine general clinical expertise with the specialized skills needed to
effectively deliver affordable health care.
To provide support for delivering health care management services, several expert
systems (e.g. INFER, PsychINFER, Procedure Necessity, and Alternatives to NonSurgical Admissions (ANSA) [46] have been developed. INFER and PsychINFER help a
medical review specialist to identify potentially catastrophic or chronic cases so that they
may be aggressively managed. Procedure Necessity helps to decide whether a surgical or
diagnostic procedure is necessary on a case-by-case basis. ANSA gives advice about
whether a hospital admission is appropriate in cases where a patient is being admitted to a
hospital for a reason other than to have surgery.
6. Open Problems
There are many open problems in e-health; these problems share some of the ad hoc
and sensor networks problems. A sublist of these problems is introduced as follows:
1- Patients’ Privacy: this a social issue that is heavily expressed in Europe and
United State. Patients are always worried about their health information.
2- Security: As in any field, information security is essential task. However, in case
of health information, patients’ lives depend on such information. Altering or
playing with patients information may lead to a disaster.
3- Sensors Integration: The current e-health applications use many of the sensing
devices that are made available by different vendors. Some of these sensors are not
standardized. Therefore, integrating such sensors in one application still an open
problem.
4- Data Mining: Patients’ information is expected to be huge which leads to data
mining problem. This problem needs to be investigated. Smart storage is required
for retrieval purposes.
7. Conclusion
In this paper, we surveyed the e-health technology, application, as well intelligent and
expert health care applications. As can be seen, the field of e-health still young and needs
a lot of research effort. This paper is intended to guide the reader to the current as well as
weakness or open problems in this field. Our future work focuses on benefiting from the
sensor networks and its intelligence in collecting the information towards the e-health and
data manipulation.
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