Low Testosterone on Social Media: Application of Natural Language Processing to Understand Patients’ Perceptions of Hypogonadism and Its Treatment

Osadchiy, Vadim; Jiang, Tommy; Mills, Jesse N.; Eleswarapu, Sriram

doi:10.2196/21383

Cited by 13 publications

(8 citation statements)

References 25 publications

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“…Inside each post, proximity of a 10-word window between each temporal marker and each detected symptom was set. 15 , 16 This threshold was selected empirically, after a comprehensive reading of all the posts. The production of the co-occurrence matrix allowed the detection of symptoms close to a temporal marker evocative of precocity ( Supplementary Table 1 ).…”

Section: Methodsmentioning

confidence: 99%

Identification of early symptoms of endometriosis through the analysis of online social networks: A social media study

et al. 2023

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Objective Endometriosis is a complex full-body inflammation disease with an average time to diagnosis of 7–10 years. Social networks give opportunity to patient to openly discuss about their condition, share experiences, and seek advice. Thus, data from social media may provide insightful data about patient's experience. This study aimed at applying a text-mining approach to online social networks in order to identify early signs associated with endometriosis. Methods An automated exploration technique of online forums was performed to extract posts. After a cleaning step of the built corpus, we retrieved all symptoms evoked by women, and connected them to the MedDRA dictionary. Then, temporal markers allowed targeting only the earliest symptoms. The latter were those evoked near a marker of precocity. A co-occurrence approach was further applied to better account for the context of evocations. Results Results were visualised using the graph-oriented database Neo4j. We collected 7148 discussions threads and 78,905 posts from 10 French forums. We extracted 41 groups of contextualised symptoms, including 20 groups of early symptoms associated with endometriosis. Among these groups of early symptoms, 13 were found to portray already known signs of endometriosis. The remaining 7 clusters of early symptoms were limb oedema, muscle pain, neuralgia, haematuria, vaginal itching, altered general condition (i.e. dizziness, fatigue, nausea) and hot flush. Conclusion We pointed out some additional symptoms of endometriosis qualified as early symptoms, which can serve as a screening tool for prevention and/or treatment purpose. The present findings offer an opportunity for further exploration of early biological processes triggering this disease.

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Section: Methodsmentioning

confidence: 99%

Identification of early symptoms of endometriosis through the analysis of online social networks: A social media study

et al. 2023

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“…Using the gradient formulas of Eq. (14,15), the network can be updated with gradient descent algorithm.The motion planning method of mobile robot based on DDPG is shown in FIGURE 12.…”

Section:     mentioning

confidence: 99%

“…Deep learning, as an important part of machine learning, has made remarkable achievements in image processing [11] , face recognition [12] , video detection [13] and natural language processing [14] field. Through multi-layer network structure and nonlinear transformation, deep learning combines low-level features to form an abstract and easily distinguishable high-level representation, so as to discover distributed feature representation of data and enable mobile robots to have strong environmental awareness.…”

Section: Introductionmentioning

confidence: 99%

Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review

Sun

Zhang

et al. 2021

IEEE Access

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Mobile robots contributed significantly to the intelligent development of human society, and the motion-planning policy is critical for mobile robots. This paper reviews the methods based on motionplanning policy, especially the ones involving Deep Reinforcement Learning (DRL) in the unstructured environment. The conventional methods of DRL are categorized to value-based, policy-based and actorcritic-based algorithms, and the corresponding theories and applications are surveyed. Furthermore, the recently-emerged methods of DRL are also surveyed, especially the ones involving the imitation learning, meta-learning and multi-robot systems. According to the surveys, the potential research directions of motion-planning algorithms serving for mobile robots are enlightened.

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“…In essence, it is a simple mapping of the biological nervous system. With the in-depth study of the ANN, researchers have made breakthroughs in such fields as pattern recognition [ 2 ], natural language processing [ 3 ], intelligent control [ 4 , 5 ], expert system [ 6 ] and predictive analysis [ 7 , 8 ], and the network structure and function is developing towards complexity and intelligence. The development of the ANN has been distributed over three stages [ 9 ]: the first-generation neural network is a very simple model, which is composed of threshold neurons, such as multi-layer perceptron [ 10 ] and Hopfield neural network [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics

et al. 2021

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With the continuous deepening of Artificial Neural Network (ANN) research, ANN model structure and function are improving towards diversification and intelligence. However, the model is more evaluated from the pros and cons of the problem-solving results and the lack of evaluation from the biomimetic aspect of imitating neural networks is not inclusive enough. Hence, a new ANN models evaluation strategy is proposed from the perspective of bionics in response to this problem in the paper. Firstly, four classical neural network models are illustrated: Back Propagation (BP) network, Deep Belief Network (DBN), LeNet5 network, and olfactory bionic model (KIII model), and the neuron transmission mode and equation, network structure, and weight updating principle of the models are analyzed qualitatively. The analysis results show that the KIII model comes closer to the actual biological nervous system compared with other models, and the LeNet5 network simulates the nervous system in depth. Secondly, evaluation indexes of ANN are constructed from the perspective of bionics in this paper: small-world, synchronous, and chaotic characteristics. Finally, the network model is quantitatively analyzed by evaluation indexes from the perspective of bionics. The experimental results show that the DBN network, LeNet5 network, and BP network have synchronous characteristics. And the DBN network and LeNet5 network have certain chaotic characteristics, but there is still a certain distance between the three classical neural networks and actual biological neural networks. The KIII model has certain small-world characteristics in structure, and its network also exhibits synchronization characteristics and chaotic characteristics. Compared with the DBN network, LeNet5 network, and the BP network, the KIII model is closer to the real biological neural network.

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Low Testosterone on Social Media: Application of Natural Language Processing to Understand Patients’ Perceptions of Hypogonadism and Its Treatment

Cited by 13 publications

References 25 publications

Identification of early symptoms of endometriosis through the analysis of online social networks: A social media study

Identification of early symptoms of endometriosis through the analysis of online social networks: A social media study

Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review

A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics

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