Doctor Recommendation Model for Pre-Diagnosis Online in China: Integrating Ontology Characteristics and Disease Text Mining (Preprint)

2020 ◽  
Author(s):  
Zhang Shuangzhu ◽  
JU Chunhua
Keyword(s):  
Text Mining ◽  
Medical Treatment ◽  
Geographical Location ◽  
Disease Diagnosis ◽  
Medical Services ◽  
Medical Field ◽  
Location Factors ◽  
Doctor Recommendation ◽  
Health Community ◽  
Full Consideration

BACKGROUND Background: The online health community provides diagnosis and treatment assistance online so that doctors and patients can keep in touch continuously anytime and anywhere. Specifically, patients can access medical services such as disease diagnosis online, medical treatment guidance, medication guidance, etc. that are provided by doctors from all over the country at home. Due to the complexity of scenarios applying medical services online and the necessity of professionalism of knowledge, the traditional recommendation methods in the medical field are confronting with problems such as low computational efficiency and poor effectiveness. At the same time, patients consulting online come from all sides and most of them suffer from non-acute or malignant diseases, and hence there may be offline medical treatment. Therefore, this paper proposes an online pre-diagnosis doctor recommendation model by integrating ontology characteristics and disease text. Particularly, this recommendation model takes full consideration of geographical location of patients. OBJECTIVE Objective: The recommendation model takes the real consultation data from online as the research object, fully testifying its effectiveness. Specifically, this model would make recommendation to patients on department and doctors based on patients’ information of symptoms, diagnosis and geographical location, as well as doctor's specialty and their department. METHODS Methods: Utilizing crawler technique, five hospital departments were selected from the online medical service platform. The names of the departments were in accordance with the standardized department names used in real hospitals (e.g., Endocrinology, Dermatology, Gynemetrics, Pediatrics and Neurology). As a result, a dataset consisting of 20000 consultation questions by patients were built. Through the application of Python and MySQL algorithms, replacing semantic dictionary retrieval or word frequency statistics, word vectors were utilized to measure similarity between patients’ pre-diagnosis and doctors’ specialty, forming a recommendation framework on medical departments or doctors based on the above-obtained sentence similarity measurement and providing recommendation advices on intentional departments and doctors. RESULTS Results: In the online medical field, compared with traditional recommendation method, the model proposed in the paper is of higher recommendation accuracy and feasibility in terms of department and doctor recommendation effectiveness. CONCLUSIONS Conclusions: The proposed online pre-diagnosis doctor recommendation model integrates ontology characteristics and disease text mining. The model gives a relatively more accurate recommendation advice based on ontology characteristics such as patients’ description texts and doctors’ specialties. Furthermore, the model also gives full consideration on patients’ location factors. As a result, the proposed online pre-diagnosis doctor recommendation model would improve patients’ online consultation experience and offline treatment convenience, enriching the value of online pre-diagnosis data.

scholarly journals Doctor Recommendation Model Based on Ontology Characteristics and Disease Text Mining Perspective

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Chunhua Ju ◽  
Shuangzhu Zhang
Keyword(s):  
Text Mining ◽  
Medical Treatment ◽  
Geographical Location ◽  
Disease Diagnosis ◽  
Medical Services ◽  
Medical Field ◽  
Location Factors ◽  
Online Consultation ◽  
Doctor Recommendation ◽  
Full Consideration

Background. Patients can access medical services such as disease diagnosis online, medical treatment guidance, and medication guidance that are provided by doctors from all over the country at home. Due to the complexity of scenarios applying medical services online and the necessity of professionalism of knowledge, the traditional recommendation methods in the medical field are confronting with problems such as low computational efficiency and poor effectiveness. At the same time, patients consulting online come from all sides, and most of them suffer from nonacute or malignant diseases, and hence, there may be offline medical treatment. Therefore, this paper proposes an online prediagnosis doctor recommendation model by integrating ontology characteristics and disease text. Particularly, this recommendation model takes full consideration of geographical location of patients. Objective. The recommendation model takes the real consultation data from online as the research object, fully testifying its effectiveness. Specifically, this model would make recommendation to patients on department and doctors based on patients’ information of symptoms, diagnosis, and geographical location, as well as doctor’s specialty and their department. Methods. Utilizing crawler technique, five hospital departments were selected from the online medical service platform. The names of the departments were in accordance with the standardized department names used in real hospitals (e.g., endocrinology, dermatology, gynemetrics, pediatrics, and neurology). As a result, a dataset consisting of 20000 consultation questions by patients was built. Through the application of Python and MySQL algorithms, replacing semantic dictionary retrieval or word frequency statistics, word vectors were utilized to measure similarity between patients’ prediagnosis and doctors’ specialty, forming a recommendation framework on medical departments or doctors based on the above-obtained sentence similarity measurement and providing recommendation advices on intentional departments and doctors. Results. In the online medical field, compared with the traditional recommendation method, the model proposed in the paper is of higher recommendation accuracy and feasibility in terms of department and doctor recommendation effectiveness. Conclusions. The proposed online prediagnosis doctor recommendation model integrates ontology characteristics and disease text mining. The model gives a relatively more accurate recommendation advice based on ontology characteristics such as patients’ description texts and doctors’ specialties. Furthermore, the model also gives full consideration on patients’ location factors. As a result, the proposed online prediagnosis doctor recommendation model would improve patients’ online consultation experience and offline treatment convenience, enriching the value of online prediagnosis data.

Risk Factor Identification, Classification and Prediction Summary of Chronic Kidney Disease

2020 ◽  
Vol 13 ◽  
Author(s):  
Pramila Arulanthu ◽  
Eswaran Perumal
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Disease Diagnosis ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Medical Field ◽  
Data Set ◽  
Medical Issues ◽  
Health Organization

: The medical data has an enormous quantity of information. This data set requires effective classification for accurate prediction. Predicting medical issues is an extremely difficult task in which Chronic Kidney Disease (CKD) is one of the major unpredictable diseases in medical field. Perhaps certain medical experts do not have identical awareness and skill to solve the issues of their patients. Most of the medical experts may have underprivileged results on disease diagnosis of their patients. Sometimes patients may lose their life in nature. As per the Global Burden of Disease (GBD-2015) study, death by CKD was ranked 17th place and GBD-2010 report 27th among the causes of death globally. Death by CKD is constituted 2·9% of all death between the year 2010 and 2013 among people from 15 to 69 age. As per World Health Organization (WHO-2005) report, 58 million people expired by CKD. Hence, this article presents the state of art review on Chronic Kidney Disease (CKD) classification and prediction. Normally, advanced data mining techniques, fuzzy and machine learning algorithms are used to classify medical data and disease diagnosis. This study reviews and summarizes many classification techniques and disease diagnosis methods presented earlier. The main intention of this review is to point out and address some of the issues and complications of the existing methods. It is also attempts to discuss the limitations and accuracy level of the existing CKD classification and disease diagnosis methods.

scholarly journals Development of a biomarker database toward performing disease classification and finding disease interrelations

Database ◽  
2021 ◽  
Vol 2021 ◽  
Author(s):  
Shaikh Farhad Hossain ◽  
Ming Huang ◽  
Naoaki Ono ◽  
Aki Morita ◽  
Shigehiko Kanaya ◽  
...  
Keyword(s):  
Rapid Detection ◽  
Molecular Mechanisms ◽  
Clustering Algorithm ◽  
Disease Diagnosis ◽  
Disease Classification ◽  
Network Clustering ◽  
Medical Field ◽  
Database Development ◽  
Abnormal State ◽  
Comprehensive Information

Abstract A biomarker is a measurable indicator of a disease or abnormal state of a body that plays an important role in disease diagnosis, prognosis and treatment. The biomarker has become a significant topic due to its versatile usage in the medical field and in rapid detection of the presence or severity of some diseases. The volume of biomarker data is rapidly increasing and the identified data are scattered. To provide comprehensive information, the explosively growing data need to be recorded in a single platform. There is no open-source freely available comprehensive online biomarker database. To fulfill this purpose, we have developed a human biomarker database as part of the KNApSAcK family databases which contain a vast quantity of information on the relationships between biomarkers and diseases. We have classified the diseases into 18 disease classes, mostly according to the National Center for Biotechnology Information definitions. Apart from this database development, we also have performed disease classification by separately using protein and metabolite biomarkers based on the network clustering algorithm DPClusO and hierarchical clustering. Finally, we reached a conclusion about the relationships among the disease classes. The human biomarker database can be accessed online and the inter-disease relationships may be helpful in understanding the molecular mechanisms of diseases. To our knowledge, this is one of the first approaches to classify diseases based on biomarkers. Database URL:  http://www.knapsackfamily.com/Biomarker/top.php

scholarly journals Largest enterprises in the Czech Republic: A spatio-temporal perspective

2012 ◽  
Vol 60 (7) ◽  
pp. 329-336 ◽  
Author(s):  
Jana Sucháček ◽  
Petra Baránek
Keyword(s):  
Czech Republic ◽  
Geographical Location ◽  
Capital City ◽  
Location Factors ◽  
Spatial Profile ◽  
Temporal Perspective ◽  
Large Enterprise ◽  
The Czech Republic ◽  
Spatio Temporal ◽  
Large Enterprises

This article focuses on spatial structure of one hundred largest enterprises in the Czech Republic from evolutionary perspective. The location of large enterprise headquarters in the Czech Republic and its implications for country’s economic spatial profile and unevenly distributed economic power is discussed thoroughly. The whole analysis is pragmatically accomplished at the level of self-governmental NUTS III regions. As it is shown, intense concentration processes in the location of largest enterprise headquarters were observed during the analyzed period between 1995 and 2010. The capital city with its surroundings proved to be the winners of this process. Currently, the spatial pattern of afore mentioned head offices is basically stabilized. On the other hand, weight of large enterprises of many regions is almost negligible and subsequently, rank of individual regions can be rather volatile. Generally speaking, economic map of the Czech Republic is not entirely in compliance with country’s settlement system. Simultaneously, fundamental factors determining the location of large enterprise head offices are evaluated also from qualitative perspective. Traditional hard location factors, such as infrastructure, geographical location or agglomeration economies turned out to be decisive for location decision-making. Apart from Prague, headquarters of large enterprises tend to prefer other big towns in the country, such as Brno, Ostrava, Olomouc, Hradec Králové or Plzeň.

scholarly journals Socio-economic resilience of Pidie Jaya to against disaster in the terms of national security

2021 ◽  
Vol 331 ◽  
pp. 02006
Author(s):  
Riskina Tri Januarti ◽  
Heridadi ◽  
Achmed Sukendro ◽  
Rio Khoirudin Apriyadi ◽  
Dandung Ruskar
Keyword(s):  
Local Community ◽  
Insurance Coverage ◽  
Geographical Location ◽  
Infrastructure Development ◽  
Sources Of Information ◽  
Location Factors ◽  
Economic Resilience ◽  
Narrative Approaches ◽  
Farming Communities ◽  
Aceh Province

Pidie Jaya (Piday) District of Aceh Province is a newly formed district in 2007. In addition to the COVID-19 pandemic, the Pidie Jaya district also experienced a series of disasters throughout 2020. Nevertheless, the Piday District Human Development Index (HDI) in 2017 exceeded the National achievement. But piday district poverty ranks 3rd in the Province. This has the potential to make the Piday district prone to disasters in addition to geographical location factors. Therefore, the existence of such gaps in this study will be discussed. This study uses qualitative methods through descriptive narrative approaches. Sources of information and data were obtained through Focus Group Decision (FGD) from several sources and studies of some literature. The results stated that the gap is due to economic development being less evenly distributed and less felt by lower-level people. This is due to low education factors and lack of capital for farmers and fishermen as the majority of workers in Piday District. The research suggests increasing community capacity and resilience by 1) Improving the quality of human resources for farmers and fishermen in the form of education and training based on improving the economy and living standards of farming communities wrapped in local wisdom; 2) Infrastructure development both facilities and infrastructure; 3) Provision of capital and ease of access in business and insurance coverage against disasters by taking into account local wisdom; 4) Empowerment of the role and involvement of local community leaders in the formulation of policies and local government institutions.

scholarly journals Variation in the delivery of telephone advice by emergency medical services: a qualitative study in three services

2019 ◽  
Vol 28 (7) ◽  
pp. 556-563
Author(s):  
Rachel O'Hara ◽  
Lindsey Bishop-Edwards ◽  
Emma Knowles ◽  
Alicia O'Cathain
Keyword(s):  
Qualitative Study ◽  
Emergency Medical Services ◽  
Participant Observation ◽  
Geographical Location ◽  
Medical Services ◽  
Alternative Response ◽  
Response Options ◽  
Emergency Medical ◽  
Telephone Advice ◽  
On The Road

BackgroundAn emergency ambulance is not always the appropriate response for emergency medical service patients. Telephone advice aims to resolve low acuity calls over the phone, without sending an ambulance. In England, variation in rates of telephone advice and patient recontact between services raises concerns about inequities in care. To understand this variation, this study aimed to explore operational factors influencing the provision of telephone advice.MethodsThis is a multimethod qualitative study in three emergency medical services in England with different rates of telephone advice and recontact. Non-participant observation (120 hours) involved 20 call handlers and 27 clinicians (eg, paramedics). Interviews were conducted with call handlers, clinicians and clinician managers (n=20).ResultsServices varied in their views of the role of telephone advice, selection of their workforce, tasks clinicians were expected and permitted to do, and access to non-ambulance responses. Telephone advice was viewed either as an acceptable approach to managing demand or a way of managing risk. The workforce could be selected for their expertise or their inability to work ‘on-the-road’. Some services permitted proactive identification of calls for a lower priority response and provided access to a wider range of response options. The findings aligned with telephone advice rates for each service, particularly explaining why one service had lower rates.ConclusionSome of the variation observed can be explained by operational differences between services and some of it by access to alternative response options in the wider urgent and emergency care system. The findings indicate scope for greater consistency in the delivery of telephone advice to ensure the widest range of options to meet the needs of different populations, regardless of geographical location.

Demographic determinants of electronic commerce (EC) adoption by SMEs

2015 ◽  
Vol 28 (3) ◽  
pp. 326-345 ◽  
Author(s):  
Hart Okorie Awa ◽  
Don Monday Baridam ◽  
Barinedum Michael Nwibere
Keyword(s):  
Geographical Location ◽  
Factor Loading ◽  
Hierarchical Regression ◽  
Future Research ◽  
Top Management ◽  
Validity And Reliability ◽  
Location Factors ◽  
Content Type ◽  
Demographic Composition ◽  
The Relationship

Purpose – Research on the demographic characteristics of top management team (TMT) on e-commerce adoption has really advanced. Although some of such studies factored location factors as e-commerce adoption drivers, rare attempts have been made to unravel if the differences in the demographic composition of TMT and the rate of adoption may be explained by the differences in the firm’s geographical location. Therefore, the purpose of this paper is to bridge this knowledge gap by proposing a framework that conceives and measures geographical location as a contextual variable between e-commerce adoption and TMT composition. Design/methodology/approach – Data were generated from the opinions of owners/managers of 226 SMEs drawn purposefully from registered SMEs in five industries located in three geo-political zones of Nigeria. Two cities (a state capital and a commercial nerve centre) were studied and a four-step hierarchical regression (spanning factor-loading) was used to test the hypotheses. Findings – Evidence from the study shows that the hypothesized relationships between demographic factors and e-commerce adoption (main/direct effects) were statistically significant (supporting H1-H4). The two moderators (physical infrastructures and industrial specialization) that explained location factors were equally statistically significant in moderating the relationship between the demographic composition of TMT and e-commerce adoption. Research limitations/implications – Sampling the opinions of SMEs in some industries of three geo-political zones of Nigeria limits the power of generalization. Therefore, extended data and measures are required to replicate the study in order to build external validity and reliability, and possibly theories. Further, some errors seem unavoidable in the course of converting the data through SPSS procedure just as all the measures used appear subjective and prone to common method bias. Other demographic and location factors not captured in the study may be handled by future studies. Originality/value – The work will be of benefit to the academia and practitioners in terms of showing how location factors dictate the relationship between the demographic composition of top management and e-commerce adoption. The paper raises pointers that stimulate future research and advised policy-makers on even or near-even distribution of infrastructural facilities.

scholarly journals Deep learning in the field of disease diagnosis

2018 ◽  
Vol 7 (2.25) ◽  
pp. 37
Author(s):  
K S. Harish Kumar ◽  
Dijo Micheal Jerald ◽  
A Emmanuel
Keyword(s):  
Deep Learning ◽  
Medical Errors ◽  
Learning Algorithm ◽  
Disease Diagnosis ◽  
Good Treatment ◽  
Test Results ◽  
Medical Field ◽  
Deep Learning Algorithm ◽  
Medical Reports ◽  
Severity Of The Disease

A good treatment is dependent on the accuracy of the diagnosis. The cure for the disease starts with the process of diagnosis. All these years, the grade and standard of the medical field has been increasing exponentially, yet there has been no significant downfall in the rate of unintentional medical errors. These errors can be avoided using Deep learning algorithm to predict the disease. The Deep Learning algorithm scans analyses and compares the patient's report with its dataset and predicts the nature and severity of the disease. The test results from the patient’s report are extracted by using PDF processing. More the medical reports analyzed, more will be the intelligence gained by the algorithm. This will be of great assistance to the doctors as they can interpret their diagnosis with the results predicted by the algorithm.  

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