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

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.

Disease Prediction and Doctor Recommendation System using Machine Learning Approaches

In our everyday life we go over numerous individuals who are experiencing some sort of Diseases. Prediction of disease is an integral part of treatment. In this project the disease is accurately predicted by looking at the symptoms of the patient where the patient can input his/her symptom and the system will predict the disease patient is suffering from. Classification Algorithms like the Naïve Bayes (NB), Random Forest, Logistic Regression and KNN have been broadly utilized to anticipate the Disease, where different accuracies were obtained. In corresponding to a particular Disease, for example, Heart Disease, Diabetes and so on is additionally anticipated by demonstrating “True” or “False” i.e. if an individual is having or not having that Disease. Prediction of such a system can have a very large potential in the medical treatment of the future. Once the Disease is predicted by the system, It then recommends which type of doctor to consult. In this paper, an audit of some new works identified with utilization of Machine Learning in expectation of disease is predicted. An interactive interface is built as front-end to facilitate interaction with the symptoms. The whole model is implemented using Django and is connected to the Django Server.

Perception of Cancer Patients and Their Attendants during Pandemic of COVID-19: An Experience from Tertiary Care Center, Pakistan

Introduction: Cancer patients have concerns about treatment during COVID-19 pandemic virus as well as its impact on their health. This survey was conducted to ascertain perception of cancer patients and their attendants during this pandemic. Methods & Results: This cross-sectional study was conducted at Oncology OPD of SIUT, from May 2020 to July 2020 on cancer patients along with their attendants. Among 306 patients, 68.9% received chemotherapy. In response of each question, 1st one belonged to patients and 2nd was related to attendants. Only positive answers are reported here. For increasing gap of chemotherapy during the pandemic COVID-19, 58.3% vs 38.4% agreed with doctor recommendation. For start of single agent chemotherapy instead of combination regimen, 41% vs 19% agreed. For hospitalization 41.5%, vs 47.7% depicted inclination towards admission whereas for mental health questions, 63.7% vs 51.3% were neither afraid nor had psychological issues 79.7% vs 25.8% respectively. About COVID-19 testing, 66% vs 22.5% wanted to be tested. If results turned out positive, 82.2% vs 24.7% would go in isolation. Conclusion: This study provides evidence of perception of cancer patients with their attendants from resource restrained country. Our study confirms that for disease like cancer, fear of the illness is always more paramount than any infection.

Doctor recommendation on healthcare consultation platforms: an integrated framework of knowledge graph and deep learning

PurposeRecommending suitable doctors to patients on healthcare consultation platforms is important to both the patients and the platforms. Although doctor recommendation methods have been proposed, they failed to explain recommendations and address the data sparsity problem, i.e. most patients on the platforms are new and provide little information except disease descriptions. This research aims to develop an interpretable doctor recommendation method based on knowledge graph and interpretable deep learning techniques to fill the research gaps.Design/methodology/approachThis research proposes an advanced doctor recommendation method that leverages a health knowledge graph to overcome the data sparsity problem and uses deep learning techniques to generate accurate and interpretable recommendations. The proposed method extracts interactive features from the knowledge graph to indicate implicit interactions between patients and doctors and identifies individual features that signal the doctors' service quality. Then, the authors feed the features into a deep neural network with layer-wise relevance propagation to generate readily usable and interpretable recommendation results.FindingsThe proposed method produces more accurate recommendations than diverse baseline methods and can provide interpretations for the recommendations.Originality/valueThis study proposes a novel doctor recommendation method. Experimental results demonstrate the effectiveness and robustness of the method in generating accurate and interpretable recommendations. The research provides a practical solution and some managerial implications to online platforms that confront information overload and transparency issues.

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

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.