Scientific Research

Automated question classification is a fundamental component of automated question-answering systems, which plays a critical role in promoting medical and healthcare services. Developing an automated question classification system depends heavily on natural language processing and data mining techniques. Question classification methods based on classical machine learning techniques face limitations in capturing the hidden relationships of features, as well as, handling complex languages and very large-scale datasets. Therefore, this paper proposes a deep learning approach for question classification, since deep learning methods have the powerful capability to extract implicit, hidden relationships and automatically generate dense representations of features. The proposed question classification model depends on unidirectional and bidirectional long short-term memory networks (LSTM and BiLSTM), which essentially developed to handle the Arabic language in the field of healthcare. The features are represented and created using a domain-specific word embedding model (Word2Vec) that is constructed by training around 1.5 million medical consultations from Altibbi company. Altibbi is a telemedicine company that is used as a case study and a source for curating and collecting the data. The proposed deep learning approach is a multi-class classification algorithm that automatically labels and maps the questions into 15 categories of medical specialities. The proposed deep learning model is evaluated using several evaluation metrics, including accuracy, precision, recall, and F1-score. Markedly, the proposed model achieved a superb classification capacity in terms of classification accuracy rate, which gained 87.2%.

The massive increase in health-related digital data has revolutionized the power of machine learning algorithms to produce more salient information. Digital health data consists of various information, including diagnoses, treatments, and medications. Diagnosis is a fundamental service provided by healthcare agents for improving patient health. However, diagnosis errors result in treating the patient incorrectly or at an improper time causing harm to them. Computer-aided diagnosis systems are intelligent methods that help clinicians in making correct decisions by mitigating the potential of clinical cognitive errors. This paper proposes an intelligent diagnosis decision support system as part of a telemedicine 1 platform for serving the Middle East and North Africa (MENA) region. The proposed system utilizes a huge health-related dataset curated by the Altibbi company, which includes numerous unstructured patient questions written in different dialects of the Arabic language, and structured symptoms identified by specialized doctors. The system encompasses a fusion of machine learning models trained based on two modalities: the symptoms and the medical questions of the patients. Various feature representation techniques (i.e., statistical and word embeddings) and machine learning classifiers, including Logistic Regression (LR), Random Forest (RF), Stochastic Gradient Descent Classifier (SGDClassifier), and variants of the Multilayer Perceptron (MLP) classifier have been used for experiments. The output of the combination of the two modalities has shown promising predictive ability in terms of the classification accuracy, which is 84.9%. The obtained results indicate the potential of the model in predicting the diagnosis of possible patient conditions based on the given symptoms and patients’ questions, which consequently can aid doctors in making the right decisions.

Download File

Pregnancy and childbirth are some of the most important events in a woman's health, starting from conception and to the actual birth event. During pregnancy, women are concerned with so many things, including her fetus development, how she will perceive different pregnancy-related changes, and prepare herself for labor and delivery.Females seek health advice starting from planning the pregnancy, the conception, delivery, and the puerperium which is the 6 weeks duration after delivery. Interaction with the health care system like the telehealth system creates opportunities to address all the concerns a woman might face during her pregnancy by laying a strong base for the ongoing health of the woman and her fetus. Telehealth services have a major impact on women’s healthcare during these phases, by educating and providing the necessary advice to walk through the changes that a woman might experience once planning the pregnancy or the pregnancy itself. Most women in the MENA region find some difficulties in presenting themselves to a general practitioner or obstetrician especially in the early phases of pregnancy. Last year, Altibbi telehealth service contributed to the reduction of doctor’s visits in 17,540 maternity cases, which made up 5.6% of the total consultations held by Altibbi telehealth service.In this paper, we will show Altibbi users’ interest in implementing telehealth services in the maternity-related topics. We will breakdown the consultations based on the country, the age median, and the average age of the users, the preferred way to consult a doctor, and any educational or complications concerns.

Download File

Nowadays, artificial intelligence plays an integral role in medical and healthcare informatics. Developing an automatic question classification and answering system is essential for coping with constant advancements in science and technology. However, efficient online medical services are required to promote offline medical services. This article proposes a system that automatically classifies medical questions of patients into medical specialties and supports the Arabic language in the MENA region. Text classification is not trivial, especially when dealing with a highly morphologically complex language, the dialectical form of which is the dominant form on the Internet. This work utilizes 15,000 medical questions asked by the clients of Altibbi telemedicine company. The questions are classified into 15 medical specialties. As the number of medical questions received daily by the company has increased, a need has arisen for an automatic classification system that can save the medical personnel much time and effort. Therefore, this article presents an efficient medical speciality classification system based on swarm intelligence (SI) and an ensemble of support vector machines (SVMs). Particle swarm optimization (PSO) is an SI-based and stochastic metaheuristic algorithm that is adopted to search for the optimal number of features and tune the hyperparameters of the SVM classifiers, which are deployed as oneversus- rest for multi-class classification. In addition, PSO is integrated with various binarization techniques to boost its performance. The experimental results show that the proposed approach accomplished remarkable performance as it achieved an accuracy of 85% and a features reduction rate of 95.9%.

This is a whitepaper published by altibbi.com comparing the numbers of mental health consultations during COVID-19 pandemic with the year prior.

Download File