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Rescue Knowledge M-learning System
by 3G mobile Phones
Shu-Chen Cheng 、Wei-Zhi Tsai 、Yun-Zhong Chen
Department of Computer Science and Information Engineering,
Southern Taiwan University, Tainan 710, Taiwan
[email protected]
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Outline
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Introduction
System Framework
Quantify Symptoms and Classification
Experimental Results
The Accuracy For Different Number Of Used
Symptoms
• Conclusions
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Introduction
1. According to 2006 Top 10 death causes announced by
Department of Health, Executive Yuan .
– Among others, top 2, 3 and 5 tend to happen in emergency and
they require urgent first aid to reduce the death before sending to
the hospitals.
2. Generally, people have insufficient knowledge with
regard to first-aid .
3. This study aims to provide a rescue knowledge mlearning system by 3G mobile Phones .
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System Framework
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Quantify Symptoms and Classification
• we generally divide the patients’ symptoms into
“Appearance” means the users can clearly find the
symptoms from observing the patients’ appearance, and
“Inquiry” means users must inquire the patients.
• Each kind of disease involve the related symptoms
(symptom 1~symptom n). Different diseases might result
in the same symptoms. Thus, the importance degrees of
these symptoms in different diseases are different. We
show the importance scores of the symptoms in diseases
by TFIDF and we also calculate the similarity of all
symptoms by cosine formula.
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Quantify Symptoms And Classification
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Quantify Symptoms and Classification
• In the symptoms represented by the degree of urgency is
divided into three
– Extremely serious(weight 2.0)
– serious situation(weight 1.5)
– general situations(weight 1.0)
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Quantify Symptoms and Classification
QDi：The scores of checking conditions with regard to i diseases.
n：Total number of symptoms checked by the users.
Wij：Weight value of j symptom in i disease.
Similar(Termj,Termik)：With regard to j symptom and i disease, the
highest similarity degree of k symptom.
u：Emergency disease weight values are divided into three levels:
Extremely serious(weight 2.0)serious situation(weight 1.5)and
general situations(weight 1.0)
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Experimental Results-Users Symptoms
Search
Select appearance symptoms
Select Inquiry symptoms
Symptoms
Symptoms
Vomiting
diarrhoea
Search results
Fever
Processing steps
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Experimental Results-Reply To The
Content
Discussions theme
Respondents
Asked content
Reply content
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The Accuracy For Different Number Of
Used Symptoms
100
90
80
accuracy
70
60
50
40
30
20
10
0
1
2
3
4
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Number of Checked Symptom
Hard Rule
Soft Rule
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Conclusions
• This research aims to provide a medical knowledge instruction platform.
• Allows users to learn the related first-aid steps by the symptoms of the varied
diseases.
• the rescue knowledge m-learning system is helpful for people’s common
medical knowledge learning.
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