trigram

This is a table of type trigram and their frequencies. Use it to search & browse the list to learn more about your study carrel.

trigram	frequency
as well as	744
the number of	693
in order to	545
the use of	541
based on the	432
of the data	414
one of the	353
can be used	335
be used to	299
in terms of	289
such as the	268
need to be	267
there is a	265
the development of	262
due to the	254
the impact of	242
the effect of	216
the need for	200
some of the	199
the context of	198
the role of	194
that can be	194
in addition to	190
part of the	182
the spread of	177
the risk of	175
a set of	170
the case of	169
a number of	166
design and methods	165
in the context	163
in the ed	156
the united states	156
in this paper	156
in the case	155
to assess the	155
a total of	153
according to the	153
to determine the	152
well as the	151
was used to	150
the importance of	148
the quality of	148
data from the	147
the presence of	144
most of the	142
a variety of	142
data can be	139
the ability to	136
as a result	136
on the other	131
as part of	131
the results of	130
needs to be	129
related to the	129
to evaluate the	127
analysis of the	124
the fact that	123
the purpose of	122
the effects of	120
mobile phone data	120
is based on	119
be able to	119
it is not	119
internet of things	118
more likely to	118
in the field	118
the other hand	116
the field of	116
in the data	114
each of the	114
the relationship between	112
of this study	112
the analysis of	111
to estimate the	111
included in the	111
in patients with	110
understanding of the	109
compared to the	108
in the united	108
because of the	107
as shown in	104
of the disease	104
the amount of	103
of the population	103
of the pandemic	103
may not be	103
of the covid	102
the lack of	102
total number of	102
depending on the	102
to improve the	101
mental health services	101
the prevalence of	101
there is no	101
in the same	101
be used for	100
the proportion of	99
the process of	99
the potential to	99
of the virus	97
to identify the	96
of the most	95
the effectiveness of	95
with respect to	94
the level of	94
associated with the	94
of infectious diseases	93
the performance of	93
large number of	92
based on a	92
information about the	92
a systematic review	92
it is important	91
of machine learning	90
and machine learning	89
data in the	89
in this study	89
used in the	89
quality of life	89
spread of the	89
can also be	88
is important to	88
to be a	88
it is possible	88
use of the	88
the application of	87
the data set	87
there was no	87
the total number	87
the probability of	87
of patients with	86
the need to	86
is used to	86
of the model	86
the aim of	85
of the epidemic	85
at the same	85
the form of	85
a lot of	85
are used to	85
the absence of	85
it can be	84
in the future	84
the time of	84
the association between	84
were used to	84
the same time	84
found to be	84
of personal data	84
which can be	83
likely to be	83
been shown to	83
and public health	83
the value of	82
the accuracy of	82
to ensure that	81
in the first	81
to the data	81
the majority of	81
this is a	80
that there is	80
of the study	79
at the time	79
study was to	79
in the us	79
this study was	79
the implementation of	79
number of cases	78
of data and	78
in the form	78
the goal of	77
shown in figure	77
the course of	77
an example of	77
the integration of	77
to provide a	77
to reduce the	77
there was a	76
different types of	76
shown in fig	76
the basis of	75
the availability of	75
the most common	75
refers to the	75
social media platforms	75
have to be	75
to investigate the	74
in this case	74
quality of the	74
data and the	74
is one of	74
to understand the	74
world health organization	73
it has been	73
is necessary to	73
machine learning algorithms	73
in real time	73
the design of	72
in the past	72
at least one	72
are able to	72
in this section	72
of big data	72
the data is	72
in which the	72
the state of	72
many of the	71
that have been	71
the concept of	71
the emergency department	71
time series data	71
the type of	71
results of the	71
a result of	70
all of the	70
there is an	70
a review of	70
it is also	70
the end of	69
addition to the	69
was associated with	69
a large number	69
a series of	68
the distribution of	68
it is a	68
in this work	67
amount of data	66
types of data	66
for public health	66
to deal with	66
an increase in	66
have been developed	66
the copyright holder	66
performance of the	66
to the ed	65
is associated with	65
characteristics of the	65
to predict the	65
the size of	65
a lack of	65
the scope of	65
we need to	65
to display the	64
to be used	64
can be found	64
is possible to	64
of infectious disease	64
on the basis	64
of public health	63
the most important	63
wide range of	63
a range of	63
between the two	63
obtained from the	63
that it is	62
be used in	62
can lead to	62
has been shown	62
and can be	61
nature of the	61
an overview of	61
is the author	61
the data from	61
of the first	61
before and after	61
the identification of	61
to determine whether	61
length of stay	61
the characteristics of	61
the evolution of	61
associated with a	61
is that the	61
copyright holder for	60
of this paper	60
this is the	60
big data analytics	60
holder for this	60
conclusion and discussion	60
granted medrxiv a	60
the preprint in	60
a list of	60
display the preprint	60
a license to	60
the author funder	60
license to display	60
with regard to	60
medrxiv a license	60
who has granted	60
in the study	60
has granted medrxiv	60
increased risk of	59
patients in the	59
the data and	59
size of the	59
security and privacy	59
in the following	59
this was a	59
provided by the	59
of the outbreak	59
a wide range	58
has the potential	58
a combination of	58
data in a	58
the rate of	58
in relation to	58
of social media	58
data on the	57
to address the	57
the public health	57
is shown in	57
review of the	57
been used to	57
to develop a	57
access to the	57
to be the	57
in the netherlands	56
impact of the	56
the problem of	56
of the world	56
be applied to	56
this version posted	56
the ability of	56
in response to	56
of altmetric data	56
has to be	56
has not been	56
and in the	56
data collection and	55
have been used	55
the university of	55
of health and	55
the data are	55
the age of	55
there are many	55
social media data	55
of the system	55
around the world	55
differences in the	55
in the last	55
in the next	55
the emergence of	55
to have a	55
the potential of	55
a case study	54
and it is	54
in other words	54
were able to	54
depends on the	54
to determine if	54
in this context	54
and use of	54
information on the	54
data were collected	53
public health surveillance	53
this study is	53
structure of the	53
referred to as	53
the introduction of	53
and analysis of	53
the structure of	53
purpose of this	53
the possibility of	53
acute respiratory syndrome	53
shown to be	52
electronic health records	52
data from a	52
the nature of	52
in mental health	52
a group of	52
may lead to	52
the supply chain	52
it should be	52
to compare the	52
which is a	52
the incidence of	52
severe acute respiratory	52
the utility of	52
can be applied	52
of the same	51
the general population	51
the right to	51
as an example	51
cases and deaths	51
they can be	51
we hypothesized that	51
were associated with	51
the needs of	51
in the absence	51
aspects of the	51
were more likely	51
figure shows the	51
natural language processing	51
some of these	50
on the data	50
to study the	50
use of a	50
the beginning of	50
are associated with	50
it will be	50
should not be	50
in the area	50
is an important	50
the complexity of	50
the rest of	50
in the early	50
used for the	49
of the patients	49
to be able	49
the user can	49
increase in the	49
there were no	49
that there are	49
for disease control	49
as compared to	49
is made available	49
in the literature	49
patients with a	49
was defined as	49
u r n	49
risk factors for	48
is needed to	48
impact on the	48
be found in	48
terms of the	48
changes in the	48
of the proposed	48
each of these	48
the data in	48
data will be	48
used as a	47
the future of	47
the detection of	47
in recent years	47
the combination of	47
the model is	47
hot research topics	47
study is to	47
data of the	47
are likely to	47
this is not	47
a part of	47
to examine the	47
role in the	47
control and prevention	46
to describe the	46
and mental health	46
in case of	46
were included in	46
the data that	46
disease control and	46
and so on	46
of data that	46
is able to	46
this paper is	46
to analyze the	46
on social media	46
amounts of data	46
patients presenting to	46
are based on	45
r o o	45
factors associated with	45
this type of	45
o o f	45
in the emergency	45
data that are	45
p r o	45
used to identify	45
n a l	45
state of the	45
the internet of	45
is difficult to	45
a subset of	45
a l p	45
goal is to	45
l p r	45
the occurrence of	45
p r e	45
assessment of the	45
the degree of	45
of the total	45
that could be	45
have also been	45
j o u	45
r n a	45
o u r	45
this can be	45
in public health	45
to account for	45
end of the	45
in some cases	44
part of a	44
the processing of	44
have not been	44
it would be	44
for each of	44
knowledge of the	44
of the research	44
that the data	44
it is necessary	44
will not be	44
parts of the	44
the influence of	44
and for the	44
that are not	44
we conducted a	44
the choice of	43
of the two	43
in clinical trials	43
they do not	43
with the same	43
context of the	43
data for the	43
of the time	43
and of the	43
as it is	43
an important role	43
the world health	43
be considered as	43
the creation of	43
to the public	43
evaluation of the	43
the cost of	43
of mental health	43
has been used	43
sensitivity and specificity	43
of the number	43
west african ebola	42
is the most	42
new york city	42
the training data	42
spread of covid	42
found that the	42
from social media	42
likely to have	42
along with the	42
to support the	42
the success of	42
we used the	42
on how to	42
of artificial intelligence	42
the study of	42
more than one	42
do not have	42
the start of	42
overview of the	42
from the data	42
the types of	41
of data from	41
on the use	41
in the development	41
dna data storage	41
but it is	41
the likelihood of	41
the frequency of	41
is not a	41
of the new	41
it is made	41
the data protection	41
for the data	41
as one of	41
data should be	41
in a single	41
different kinds of	41
of the three	41
during the covid	41
to build a	41
the real world	41
rest of the	41
it comes to	41
to capture the	41
used to estimate	40
is likely to	40
the first step	40
was found to	40
in the world	40
based on their	40
the west african	40
the dynamics of	40
in contrast to	40
the area of	40
also be used	40
the data collection	40
early detection of	40
years of age	40
focus on the	40
the development and	40
the raw data	40
aim of this	40
in the current	40
number of groups	40
the set of	40
implementation of the	40
when it comes	40
in the previous	40
respect to the	40
in the u	40
small number of	40
the power of	40
there are no	40
the input data	39
there have been	39
can be seen	39
be associated with	39
we found that	39
did not have	39
license it is	39
are needed to	39
can be considered	39
systematic review of	39
be used as	39
the collected data	39
big data and	39
lead to a	39
the reliability of	39
the collection of	39
to measure the	39
of mobile phone	39
value of the	39
made available under	39
out of the	39
access to data	39
of a pandemic	39
used to determine	39
the objective of	39
a is the	39
the social media	39
the adoption of	39
international license it	39
available under a	39
will need to	38
such as those	38
the validity of	38
appears to be	38
use of ai	38
such as a	38
the health care	38
to make the	38
a comparison of	38
number of people	38
in developing countries	38
accuracy of the	38
which was not	38
could be used	38
of a new	38
version of the	38
health care system	38
of breast cancer	38
it does not	38
data to be	38
defined as the	38
the benefits of	38
that they are	38
large amounts of	38
there are several	38
one or more	38
of health care	37
in this way	37
the time series	37
have been proposed	37
data that is	37
supported by the	37
in the real	37
in such a	37
data collected from	37
is expected to	37
similar to the	37
in comparison to	37
in the analysis	37
to explore the	37
is essential to	37
presenting to the	37
the study period	37
not associated with	37
for the purpose	37
compared with the	37
were obtained from	37
of confirmed cases	37
considered to be	37
the control group	37
from the same	37
available in the	36
deep neural networks	36
data such as	36
the efficacy of	36
we do not	36
representation of the	36
the result of	36
that do not	36
a deep learning	36
resulted in a	36
we believe that	36
the potential for	36
in the community	36
in the population	36
little is known	36
at the end	36
carried out in	36
in this regard	36
prevention and control	36
the next step	36
in the number	36
the inclusion of	36
description of the	36
that they have	36
for this preprint	36
development of a	36
the range of	36
preprint in perpetuity	36
extent to which	36
on the platform	36
ensure that the	36
used to predict	36
and implementation of	36
and quality of	36
a survey of	35
to be more	35
is defined as	35
of more than	35
which is the	35
estimation of the	35
were asked to	35
the study was	35
that of the	35
of the public	35
the treatment of	35
were found to	35
the extent to	35
relationship between the	35
prior to the	35
serve as a	35
was able to	35
of all the	35
to use the	35
centers for disease	35
a way that	35
a framework for	35
values of the	35
and the number	35
change in the	35
the values of	35
important role in	35
the proposed system	35
for this purpose	35
of the main	35
as long as	35
the health of	35
public health authorities	35
is a need	35
are more likely	35
can be a	35
of the current	35
the percentage of	35
should be considered	35
risk factor for	35
response to the	35
systematic review and	34
we did not	34
the source of	34
of the models	34
to meet the	34
is used for	34
members of the	34
it may be	34
of the european	34
the case for	34
there are a	34
less likely to	34
are shown in	34
to the same	34
data from different	34
in the process	34
of the information	34
the scientific community	34
stored in the	34
is to provide	34
this kind of	34
significantly associated with	34
of data to	34
purpose of the	34
can be made	34
when compared to	34
of the internet	34
is known about	34
to better understand	34
by the data	34
the definition of	34
estimates of the	33
found in the	33
our understanding of	33
to find the	33
we sought to	33
men and women	33
from different sources	33
and social media	33
a public health	33
there has been	33
for the first	33
of digital health	33
showed that the	33
not possible to	33
is designed to	33
being able to	33
with each other	33
results show that	33
to increase the	33
digital contact tracing	33
involved in the	33
of the key	33
of the human	33
drug sales data	33
can then be	33
data must be	33
the help of	33
case of covid	33
under a is	33
data to the	33
contribute to the	33
development of the	33
into account the	33
objective of this	33
is also a	32
many of these	32
of deep learning	32
with the data	32
during a pandemic	32
so that the	32
this is an	32
was not certified	32
to be considered	32
by peer review	32
intensive care unit	32
ebola virus disease	32
that they can	32
models can be	32
preprint this version	32
by means of	32
open source data	32
needs of the	32
of these data	32
number of patients	32
evolution of the	32
used to evaluate	32
a need for	32
in this area	32
shown in the	32
seems to be	32
may also be	32
this preprint this	32
type of data	32
for the development	32
the novel coronavirus	32
large amount of	32
view of the	32
certified by peer	32
the location of	32
not certified by	32
over the past	32
result of the	32
a machine learning	32
have been shown	32
in this article	32
show that the	32
present in the	32
of the underlying	32
the management of	32
any of the	32
focused on the	32
would like to	32
for patients with	31
the issue of	31
in new york	31
for a given	31
data sharing and	31
of such a	31
of the different	31
beginning of the	31
with more than	31
could not be	31
may be a	31
associated with increased	31
a function of	31
in combination with	31
privacy and security	31
data provided by	31
time series of	31
different levels of	31
african ebola epidemic	31
an analysis of	31
have access to	31
was carried out	31
taken into account	31
depicted in fig	31
has led to	31
and specificity of	31
need for a	31
the face of	31
is not possible	31
processing of personal	31
body mass index	31
study was conducted	31
is the first	31
might not be	31
who did not	30
machine learning in	30
during the study	30
majority of the	30
to the hospital	30
electronic health record	30
led to the	30
was used for	30
of the original	30
due to a	30
none of the	30
in health care	30
depend on the	30
ed patients with	30
machine learning and	30
the evaluation of	30
use of data	30
the two groups	30
it is difficult	30
number of the	30
there are some	30
of the network	30
in the public	30
an opportunity to	30
number of clusters	30
of data collection	30
of the following	30
that has been	30
which may be	30
prospective cohort study	30
of the literature	30
features of the	30
stages of the	30
not only the	30
the protection of	30
social media post	30
it is clear	30
of the patient	30
with the help	30
shown in table	30
to ensure the	30
we developed a	30
we use the	30
due to their	30
the opportunity to	30
supply chain management	29
artificial intelligence and	29
of patients who	29
and the data	29
in the face	29
component of the	29
the provision of	29
applied to the	29
a need to	29
data set is	29
the primary outcome	29
patients were enrolled	29
data set of	29
in light of	29
to control the	29
all of these	29
a cohort of	29
in line with	29
the burden of	29
the data to	29
in the present	29
from the ed	29
convenience sample of	29
the city of	29
data from multiple	29
back to the	29
on the one	29
fact that the	29
point of view	29
of the paper	29
to do so	29
to test the	29
are used for	29
in the medical	29
allows users to	29
of the compounds	29
infectious disease epidemiology	29
of the global	29
a survey on	29
of the project	29
the difference in	29
to the number	29
patients who were	29
shows that the	29
of the individual	29
based on data	29
distribution of the	29
have the potential	29
of data for	29
deal with the	29
in each of	29
the original data	29
a measure of	29
we used a	29
over the last	28
on the internet	28
used to assess	28
the outcome of	28
time of the	28
as a whole	28
taking into account	28
with at least	28
in the food	28
is a major	28
in social media	28
better understanding of	28
mean age was	28
effects of the	28
there are two	28
sharing of data	28
is required to	28
effectiveness of the	28
and development of	28
during the first	28
as described in	28
health and medical	28
were analyzed using	28
more and more	28
are related to	28
an infectious disease	28
in a large	28
using data from	28
public health and	28
status of the	28
a better understanding	28
the focus of	28
and big data	28
and how they	28
to use a	28
is important for	28
suggested that the	28
the possibility to	28
the results are	28
in a way	28
the assessment of	28
it is the	28
insights into the	28
predictions of the	28
we performed a	28
well as to	28
of the health	28
health and social	28
in the database	28
the relationships between	28
be taken into	28
that need to	28
and medical research	28
to public health	28
the difference between	28
the outbreak of	28
aim of the	28
case of the	28
example of a	28
derived from the	28
to each other	28
implementation of a	28
to be addressed	28
extracted from the	28
the change in	27
is clear that	27
to track the	27
a history of	27
application of the	27
to model the	27
morbidity and mortality	27
of the process	27
on the number	27
of coronavirus disease	27
it possible to	27
a focus on	27
in the control	27
and how to	27
with the use	27
systematic literature review	27
based on these	27
a period of	27
review and meta	27
in the second	27
more than a	27
can be achieved	27
mental health and	27
information from the	27
is the number	27
convolutional neural networks	27
the extent of	27
in the system	27
so that they	27
we want to	27
may need to	27
described in the	27
the addition of	27
there is also	27
of missing data	27
patients who received	27
machine learning for	27
have been made	27
from the national	27
half of the	27
can be done	27
without the need	27
level i trauma	27
complexity of the	27
to be associated	27
and artificial intelligence	27
a convenience sample	27
goal of this	27
results in a	27
and did not	27
the machine learning	27
the authors declare	27
comparison of the	27
so as to	27
known as the	27
the one hand	27
and control of	27
in the covid	27
is not the	27
were calculated using	27
as in the	27
the fight against	27
is due to	26
data obtained from	26
broad range of	26
a way to	26
data may be	26
proportion of the	26
patients were included	26
and after the	26
known to be	26
artificial intelligence in	26
are presented in	26
is that it	26
goal of the	26
research and development	26
the advent of	26
to facilitate the	26
is the case	26
participated in the	26
analysis was performed	26
the incubation period	26
there are also	26
of human mobility	26
information can be	26
across the globe	26
as opposed to	26
the list of	26
to share their	26
no difference in	26
in the digital	26
in the two	26
relevant to the	26
and on the	26
information related to	26
of a given	26
the onset of	26
in the blockchain	26
during the pandemic	26
high level of	26
and evaluation of	26
the authors of	26
to create a	26
are available for	26
parameters of the	26
with the highest	26
the estimation of	26
be difficult to	26
the next section	26
of such data	26
for the analysis	26
the limitations of	26
are the most	26
course of the	26
for the same	26
of the major	26
of the four	26
connected to the	26
is used in	26
the integrity of	26
of gs standards	26
to achieve the	26
and health care	26
we studied the	26
analysis of a	26
over the course	26
of the infection	26
of data collected	26
control of the	26
for infectious disease	26
focuses on the	26
to obtain the	26
that will be	26
tend to be	26
if it is	26
can be identified	26
is a key	26
data are available	26
the sum of	26
the diagnosis of	25
results indicate that	25
is hard to	25
different from the	25
that may be	25
were used for	25
health services research	25
machine learning methods	25
the output of	25
a matter of	25
of an outbreak	25
in west africa	25
an understanding of	25
dynamics of the	25
to the user	25
start of the	25
data sharing in	25
been developed to	25
was a prospective	25
as a function	25
the research community	25
sent to the	25
of data in	25
may be used	25
the department of	25
role of the	25
health care providers	25
the interpretation of	25
the date of	25
to be an	25
in the network	25
the idea of	25
allows us to	25
of the analysis	25
what are the	25
note that the	25
infectious disease outbreaks	25
in a population	25
not able to	25
data sets and	25
has become a	25
coronary heart disease	25
has been developed	25
on top of	25
the usage of	25
a sample of	25
a collection of	25
to allow for	25
admitted to the	25
difference between the	25
the data on	25
in the use	25
presented to the	25
for use in	25
the data controller	25
a broad range	25
to develop and	25
at least a	25
data are collected	25
two types of	25
we propose a	25
used by the	25
of the results	25
objective was to	25
the ratio of	25
if there is	25
have shown that	25
be useful for	25
they are not	25
would have to	25
electronic medical record	25
wide variety of	25
the time to	25
linked to the	25
to protect the	25
determined by the	25
associated with an	25
to share data	25
the new york	25
to quantify the	25
on the same	25
an increased risk	25
related to covid	25
to enhance the	25
can be obtained	25
defined by the	25
a wide variety	25
with and without	25
the data for	25
the shivom platform	25
the general public	25
of cases and	25
which has been	24
factors such as	24
data have been	24
should be noted	24
i trauma center	24
for which the	24
of the studies	24
it is essential	24
by the user	24
results suggest that	24
the establishment of	24
preprint the copyright	24
of the above	24
of blockchain technology	24
a random sample	24
for more than	24
we examined the	24
in many cases	24
approved by the	24
version posted september	24
the contribution of	24
take into account	24
were randomized to	24
does not have	24
is related to	24
these data are	24
scope of the	24
their ability to	24
was conducted in	24
in the icu	24
the work of	24
the growth of	24
methods such as	24
issues related to	24
social big data	24
the control of	24
through the use	24
difference in the	24
is provided by	24
the data of	24
in different countries	24
this this version	24
system based on	24
the present study	24
be seen as	24
effect of the	24
to access the	24
the demand for	24
to detect the	24
the data collected	24
of this article	24
prospective observational study	24
presented in this	24
from multiple sources	24
the data subject	24
the deployment of	24
significant differences in	24
respiratory syndrome coronavirus	24
the basis for	24
is composed of	24
of an epidemic	24
can be easily	24
are used in	24
be due to	24
study of the	24
at an urban	24
regardless of the	24
infectious disease transmission	24
to this end	24
results from the	24
diseases such as	24
the reproduction number	24
the proposed approach	24
data is a	24
but also the	24
only a few	24
the usefulness of	24
the same way	24
the latent space	24
the mean age	24
we discuss the	24
for this reason	24
methods have been	24
the duration of	24
ffi ffi ffi	24
of the various	24
would not be	24
for this this	24
the prediction of	24
data analysis and	24
of the coronavirus	24
in the near	24
of data sharing	24
a small number	24
illustrated in fig	24
we assume that	23
the paper is	23
in the general	23
the association of	23
the rise of	23
for research purposes	23
much of the	23
the name of	23
a systematic literature	23
the public interest	23
ease of use	23
cases in the	23
the severity of	23
can help to	23
indicate that the	23
to monitor the	23
the impacts of	23
and effectiveness of	23
identification of the	23
a large amount	23
phase of the	23
using mobile phone	23
a data set	23
data collected by	23
position of the	23
there is the	23
challenges and opportunities	23
one of these	23
when they are	23
in this field	23
interpretation of the	23
the european union	23
the existence of	23
the numbers of	23
department of health	23
is to be	23
of data is	23
and the use	23
what is the	23
that it can	23
the first time	23
is an open	23
fraction of the	23
the human genome	23
across the world	23
a prospective observational	23
spatial and temporal	23
at the level	23
the security of	23
is not only	23
number of infected	23
of them are	23
we focus on	23
the gold standard	23
at high risk	23
been used for	23
the benefit of	23
his or her	23
was based on	23
could have been	23
the available data	23
be included in	23
the behavior of	23
be achieved by	23
to the pandemic	23
led to a	23
in which a	23
assume that the	23
to that of	23
outside of the	23
will be used	23
that would be	23
in most cases	23
in a timely	23
the task of	23
for the study	23
be noted that	23
target data stream	23
by using the	23
from the covid	23
it is worth	23
the data were	23
recurrent neural networks	23
as a part	23
positive and negative	23
objective is to	23
high levels of	23
appear to be	23
have focused on	23
and other data	23
to consider the	23
stage of the	23
this approach is	23
added to the	23
enrolled in the	23
able to identify	23
data related to	23
machine learning models	23
differences between the	23
are to be	23
an average of	23
for the future	23
be useful in	23
machine learning to	23
to manage the	23
in infectious disease	23
of the art	23
recent advances in	23
ability of the	23
also known as	23
that might be	23
most of these	23
of novel coronavirus	22
and deep learning	22
deep learning for	22
data as a	22
the posterior predictive	22
using machine learning	22
two or more	22
the user to	22
of the present	22
are available in	22
insight into the	22
to calculate the	22
of a large	22
number of individuals	22
can only be	22
and challenges of	22
after adjustment for	22
consists of a	22
that are used	22
the metabolic syndrome	22
in the hospital	22
health care and	22
could lead to	22
have a significant	22
details of the	22
the news articles	22
is part of	22
which is not	22
it could be	22
to health care	22
public health officials	22
will continue to	22
to obtain a	22
components of the	22
research is needed	22
can be accessed	22
corresponding to the	22
big data in	22
in primary care	22
regression was used	22
the patient and	22
as depicted in	22
to verify the	22
used in this	22
is to use	22
to respond to	22
for machine learning	22
primary outcome was	22
to work with	22
of an individual	22
this means that	22
be based on	22
a summary of	22
the construction of	22
by the model	22
and number of	22
a high level	22
to participate in	22
in the pre	22
primary health care	22
social and economic	22
of privacy and	22
will lead to	22
by the national	22
to collect data	22
were identified as	22
information in the	22
as the number	22
even though the	22
the same data	22
data at the	22
affected by the	22
in the health	22
to provide an	22
used to generate	22
logistic regression analysis	22
number of new	22
the question of	22
as a consequence	22
machine learning is	22
because it is	22
especially in the	22
for those who	22
the adequacy of	22
compared to other	22
prediction of the	22
the near future	22
the parameters of	22
to achieve this	22
of the existing	22
was no difference	22
the fields of	22
the current pandemic	22
since it is	22
using deep learning	22
the identity of	22
johns hopkins university	22
relation to the	22
a model for	22
deep learning methods	22
the focal firm	22
of the problem	22
and how the	22
whether or not	22
of a data	22
of a single	22
design of the	22
data and information	22
to solve the	22
people who are	22
the hypothesis that	22
and treatment of	22
in the presence	22
of the local	22
at the university	22
in a given	21
the data sources	21
the increase in	21
example of the	21
large numbers of	21
with a high	21
to be developed	21
could also be	21
the promise of	21
they have no	21
produced by the	21
would have been	21
at the beginning	21
the data sets	21
be useful to	21
during the outbreak	21
cohort study of	21
considered as a	21
and type of	21
in urban areas	21
have led to	21
discussed in the	21
the model parameters	21
of the dataset	21
design and implementation	21
model of the	21
participate in the	21
may be more	21
a role in	21
the ebola outbreak	21
this paper we	21
access to health	21
systems that are	21
that machine learning	21
in the post	21
various types of	21
well as a	21
to note that	21
availability of data	21
the advantage of	21
is not always	21
due to its	21
number of covid	21
dependent on the	21
total of patients	21
of this approach	21
gene expression data	21
for a single	21
to the emergency	21
to the lack	21
been associated with	21
poster sessions v	21
our ability to	21
and the risk	21
and access to	21
nodes in the	21
of which are	21
in the age	21
of a particular	21
for the use	21
analyses were performed	21
the notion of	21
data were analyzed	21
and management of	21
has been a	21
of the novel	21
be the case	21
available to the	21
for at least	21
infectious disease surveillance	21
of the training	21
values for the	21
caused by the	21
wang et al	21
there may be	21
lead to the	21
that the number	21
patients presenting with	21
number of tests	21
to cope with	21
according to their	21
after adjusting for	21
means that the	21
randomized controlled trials	21
in the healthcare	21
logistic regression was	21
will be a	21
the transmission of	21
is applied to	21
positive or negative	21
logarithm of the	21
if they are	21
the correlation between	21
result in a	21
early stages of	21
with a mean	21
are difficult to	21
with regards to	21
of data are	21
with the aim	21
the privacy of	21
are required to	21
in collaboration with	21
for big data	21
of the device	20
the details of	20
higher levels of	20
consistent with the	20
to be made	20
opportunities and challenges	20
to enable the	20
and the public	20
to identify and	20
the public good	20
data is not	20
is organized as	20
in health and	20
the point of	20
to a specific	20
followed by a	20
in the city	20
phone data for	20
of data protection	20
identity of the	20
it is still	20
data mining and	20
has shown that	20
in a more	20
of the us	20
was not associated	20
the open source	20
use of mobile	20
the challenge of	20
than in the	20
performance of a	20
that we have	20
based on this	20
is equal to	20
those who are	20
be linked to	20
the limits of	20
health and disease	20
of the user	20
calculated for the	20
properties of the	20
of access to	20
the principles of	20
to get the	20
in an urban	20
is determined by	20
in the research	20
the content of	20
to the original	20
for the detection	20
data protection directive	20
to establish a	20
as they are	20
is possible that	20
these and other	20
at the start	20
to focus on	20
used to collect	20
proportion of patients	20
is critical to	20
to the health	20
source of information	20
we provide a	20
those that are	20
period of time	20
the history of	20
has also been	20
and risk of	20
of the protein	20
to a single	20
number of deaths	20
validity of the	20
to minimize the	20
data has been	20
the interaction between	20
compared to those	20
based on an	20
significant increase in	20
a difference in	20
of ai in	20
is a common	20
of the cases	20
electronic medical records	20
may have been	20
the serial interval	20
directly from the	20
and drug administration	20
of emergency medicine	20
of ai ethics	20
development of new	20
the efficiency of	20
number of nodes	20
are included in	20
of a system	20
research in the	20
for breast cancer	20
food and drug	20
the speed of	20
the ed with	20
and there is	20
should also be	20
that should be	20
in federated learning	19
even in the	19
products and services	19
can easily be	19
privacy and data	19
and in a	19
of clinical trials	19
to address these	19
spatial distribution of	19
approach to the	19
the system and	19
there will be	19
and gs standards	19
location of the	19
and lack of	19
the system is	19
not yet been	19
of patients were	19
will also be	19
in conjunction with	19
the sensitivity and	19
of the social	19
were compared using	19
is crucial to	19
of the national	19
effect of a	19
it is often	19
according to a	19
universal health coverage	19
all the data	19
of the future	19
from a single	19
health care workers	19
the perspective of	19
to represent the	19
can have a	19
data sharing is	19
there is little	19
a tool for	19
play an important	19
of at least	19
and confidence intervals	19
in the course	19
the remainder of	19
number of parameters	19
was to determine	19
information such as	19
this work was	19
data collected in	19
in the model	19
the calculation of	19
of the input	19
to the fact	19
we compared the	19
the most effective	19
data management and	19
share their data	19
issues such as	19
in the original	19
it is now	19
of the participants	19
of the general	19
be possible to	19
the design and	19
have been identified	19
of social distancing	19
over time and	19
of genomic data	19
this data set	19
when there is	19
where the data	19
be the most	19
that this is	19
seen as a	19
such that they	19
the length of	19
look at the	19
rate of the	19
for infectious diseases	19
are stored in	19
of the state	19
used in a	19
could be a	19
social media platform	19
studies have shown	19
responsible for the	19
of ai systems	19
advantage of the	19
patients who had	19
not have a	19
model based on	19
contributed to the	19
created by the	19
they found that	19
random sample of	19
for a specific	19
focusing on the	19
regions of the	19
reported in the	19
that in the	19
data could be	19
the volume of	19
the early stages	19
data sets are	19
the differences in	19
of the ed	19
findings suggest that	19
better understand the	19
been applied to	19
the study is	19
have the same	19
a large scale	19
can be further	19
are capable of	19
of risk factors	19
we present the	19
should be taken	19
no significant differences	19
an influenza pandemic	19
we show that	19
need to develop	19
average number of	19
relies on the	19
to have the	19
a prospective cohort	19
in the paper	19
the proposed framework	19
to one of	19
also need to	19
in the table	19
may result in	19
as illustrated in	18
and does not	18
version posted june	18
to the time	18
effect on the	18
the social and	18
methods can be	18
areas of the	18
is necessary for	18
to the development	18
the disease and	18
approaches have been	18
of the sars	18
the problems of	18
to understand and	18
on a single	18
public and private	18
as a means	18
make use of	18
h n influenza	18
funded by the	18
paper is organized	18
of pandemic influenza	18
deep learning model	18
were collected from	18
people in the	18
were compared with	18
with an increased	18
data with the	18
of raw data	18
in our case	18
are essential to	18
will be discussed	18
model can be	18
represented as a	18
implemented in the	18
public health interventions	18
high risk of	18
analysis of data	18
to make a	18
when the data	18
and at the	18
sources such as	18
spread of infectious	18
available at the	18
the discovery of	18
proof of concept	18
the authors also	18
of cases in	18
from the literature	18
of epidemiological data	18
data requests and	18
to simulate the	18
source data sets	18
was performed using	18
was to assess	18
data into the	18
in many countries	18
of infected individuals	18
in the sense	18
regard to the	18
the health and	18
the odds of	18
suggest that the	18
public health professionals	18
used to detect	18
a great deal	18
not be able	18
not statistically significant	18
the reasons for	18
be used by	18
and spread of	18
has focused on	18
significant difference in	18
the relation between	18
has been the	18
the example of	18
we use a	18
to the extent	18
the training process	18
the data was	18
reported by the	18
to address this	18
is responsible for	18
in a federated	18
that is not	18
up to the	18
support vector machine	18
increasing number of	18
are known to	18
of their own	18
digital health data	18
such a way	18
were compared to	18
as much as	18
an rfid reader	18
organized as follows	18
to the use	18
the h n	18
for a more	18
we describe the	18
contained in the	18
and the corresponding	18
models have been	18
the understanding of	18
the production of	18
not limited to	18
leading to the	18
data protection and	18
data that can	18
cause of death	18
the inclusion criteria	18
other types of	18
of the final	18
using the same	18
to the current	18
no significant difference	18
known about the	18
data from other	18
the description of	18
the loss of	18
web of science	18
because they are	18
no competing interests	18
the center for	18
can be useful	18
number of days	18
personal data in	18
of food products	18
some of them	18
name of the	18
data are not	18
the big data	18
as a potential	18
data need to	18
take advantage of	18
put in place	18
this may be	18
that is a	18
will have to	18
sources of data	18
the spatial distribution	18
the results from	18
account for the	18
presence of a	18
as the data	18
with the current	18
is supported by	18
and processing of	18
of the medical	18
the relevance of	18
to be taken	18
limited number of	18
form of a	18
in the training	18
artificial neural networks	18
been developed for	18
the most recent	18
food supply chain	18
of disease outbreaks	18
an open source	18
yet to be	18
that must be	18
the feasibility of	18
a discussion of	18
content of the	18
together with the	18
in the fight	18
risk of infection	18
such as data	18
a fraction of	18
a framework to	18
is similar to	18
authors declare that	18
respond to the	18
was observed in	17
based on our	17
collection of data	17
by the european	17
seen in the	17
of ebola virus	17
study was performed	17
are interested in	17
more difficult to	17
is the ability	17
the coronavirus disease	17
the data can	17
of the industry	17
proportional to the	17
based approach to	17
as described above	17
if they were	17
along with a	17
stored in a	17
benefit from the	17
the formation of	17
with missing data	17
with the number	17
absence of a	17
which will be	17
machine learning techniques	17
in the treatment	17
may be useful	17
of the city	17
in the design	17
information of the	17
shows an example	17
for early detection	17
not only to	17
secondary analysis of	17
in published maps	17
data as well	17
of the algorithm	17
if the data	17
in the european	17
it is unclear	17
claims in published	17
severity of the	17
important to note	17
retrospective cohort study	17
to analyse the	17
as discussed in	17
attention to the	17
can be detected	17
in the initial	17
were calculated for	17
not included in	17
we analyzed the	17
the challenges and	17
a sensitivity of	17
the data processing	17
randomized controlled trial	17
will have a	17
is a significant	17
percentage of the	17
of gene expression	17
summary of the	17
in machine learning	17
such as in	17
of these systems	17
large data sets	17
refer to the	17
patterns in the	17
regard to jurisdictional	17
and many other	17
to which the	17
in ways that	17
remains neutral with	17
can be calculated	17
to the system	17
was set to	17
the results show	17
importance of the	17
on behalf of	17
this suggests that	17
used to compare	17
in the s	17
which may not	17
generated by the	17
results of a	17
the era of	17
a change in	17
bickman et al	17
the safety of	17
were based on	17
deep learning models	17
which in turn	17
well as in	17
at this point	17
of interest to	17
the movement of	17
statistical analysis of	17
and the development	17
the magnitude of	17
of the problems	17
provided in the	17
age and sex	17
in big data	17
defined as a	17
also used to	17
systolic blood pressure	17
not need to	17
nature remains neutral	17
the reference model	17
the epidemiology of	17
the goal is	17
posterior predictive data	17
the case with	17
national center for	17
participating in the	17
tools such as	17
a significant increase	17
in a similar	17
of the datasets	17
springer nature remains	17
to understand how	17
confirmed cases and	17
rely on the	17
there are more	17
the true number	17
of the next	17
technologies such as	17
research in this	17
in place to	17
for pandemic influenza	17
developed for the	17
during the early	17
this work is	17
in ed patients	17
data is the	17
quality of care	17
a reduction in	17
models that are	17
data were obtained	17
creative commons license	17
the subject of	17
attached to the	17
estimate of the	17
in the usa	17
reliability of the	17
it is very	17
developed by the	17
in the final	17
the pandemic and	17
the challenges of	17
the target data	17
to jurisdictional claims	17
in patients presenting	17
ed visits for	17
for the public	17
neutral with regard	17
a method for	17
to have an	17
example is the	17
the purposes of	17
for all the	17
close to the	17
jurisdictional claims in	17
with big data	17
for the diagnosis	17
in the period	17
the selection of	17
and blood pressure	17
the sharing of	17
and have been	17
can serve as	17
characterized by a	17
that the use	17
made available to	17
in this review	17
a system that	17
can be utilized	17
were enrolled in	17
risk of developing	17
to perform a	17
true number of	17
the intervention group	17
declare that they	17
the advantages of	17
from the perspective	17
when it is	17
the capacity to	17
paper is to	17
maps and institutional	17
approach is to	17
the current state	17
and should be	17
and institutional affiliations	17
has been proposed	17
of the target	17
is available in	17
or at least	17
we find that	17
are not available	17
be represented as	17
the model and	17
analysis and visualization	17
can be estimated	17
burden of disease	17
of the database	17
by public health	17
to get a	17
published maps and	17
that the user	17
of interest is	17
was performed to	17
lung cancer risk	16
to the best	16
such as covid	16
is considered to	16
can be represented	16
use of these	16
of data mining	16
the response to	16
starting from the	16
then used to	16
the order of	16
protein interaction networks	16
the domain of	16
analyses of the	16
number of visits	16
as to the	16
as for the	16
among patients with	16
mean age of	16
incorporated into the	16
uncertainty in the	16
fight against the	16
mentioned in the	16
the acquisition of	16
the changes in	16
a risk factor	16
of patients in	16
consent of the	16
it might be	16
for data collection	16
efforts have been	16
patients with covid	16
the mean of	16
considered in the	16
recorded in the	16
of how to	16
the coverage of	16
of the community	16
to collect and	16
retrospective chart review	16
of this work	16
large volumes of	16
in cases where	16
of social and	16
devices such as	16
to see if	16
that the model	16
for a particular	16
in at least	16
with deep learning	16
the collection and	16
not be the	16
further research is	16
day of the	16
from the field	16
at risk of	16
the population of	16
be necessary to	16
and length of	16
to show that	16
the center of	16
were used in	16
is not clear	16
at any time	16
in of the	16
the patterns of	16
to mental health	16
was significantly associated	16
the benefits and	16
the most relevant	16
remote sensing of	16
be extended to	16
the dgsaugust dataset	16
significant impact on	16
order to identify	16
all types of	16
were no significant	16
to inform the	16
an effort to	16
all of which	16
in which they	16
to learn the	16
to achieve a	16
increase the risk	16
the capacity of	16
the open commons	16
such that the	16
a sequence of	16
to be effective	16
logistic regression model	16
presented in the	16
age was years	16
observational study of	16
an attempt to	16
children and adolescents	16
during the west	16
not have the	16
the model to	16
the federated cloud	16
of our study	16
based access control	16
the progress of	16
by using a	16
on march th	16
of the day	16
percent of the	16
in view of	16
and the other	16
the medical field	16
by the same	16
to occur in	16
of a specific	16
european data protection	16
we propose to	16
taken from the	16
in this sense	16
but also to	16
the data used	16
risk of death	16
deep convolutional neural	16
in contact with	16
of all patients	16
data scientists and	16
higher rates of	16
blockchain technology and	16
a paradigm shift	16
from the posterior	16
to the patient	16
the infection rate	16
risks associated with	16
our loss function	16
for the period	16
science and technology	16
of a novel	16
to optimize the	16
standard deviation of	16
is represented by	16
used to develop	16
the last decade	16
acute myocardial infarction	16
and identify the	16
by a single	16
between the data	16
the scale of	16
the deep learning	16
power of the	16
is to develop	16
of safety data	16
to provide the	16
the individual and	16
the generation of	16
than half of	16
as is the	16
can be computed	16
and to the	16
is to identify	16
the kinds of	16
time spent in	16