Machine learning to support social media empowered patients in cancer care and cancer treatment decisions

Silva, Daswin De; Ranasinghe, Weranja; Bandaragoda, Tharindu; Adikari, Achini; Mills, Nishan; Iddamalgoda, Lahiru; Alahakoon, Damminda; Lawrentschuk, Nathan; Persad, Raj; Osipov, Evgeny; Gray, Richard; Bolton, Damien

doi:10.1371/journal.pone.0205855

Cited by 79 publications

(58 citation statements)

References 76 publications

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“…Medical imaging techniques such as computed tomography, magnetic resonance imaging, positron emission tomography, ultrasound and X-ray images, have facilitated the creation of large digital databases that can be processed with AI tools [2]. Thus, AI is now playing an important role in medical imaging interpretation to support tasks such as early detection, accurate diagnosis and treatment for diseases [3][4][5]. In addition, AI has powerful algorithms that can be used to enhance medical tasks and skills, thus overcoming fatigue, distraction, out of date in new diagnosis techniques or age-related impairment of the visual sense in physicians [6].…”

Section: Introductionmentioning

confidence: 99%

Computer-aided diagnosis of external and middle ear conditions: A machine learning approach

et al. 2020

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In medicine, a misdiagnosis or the absence of specialists can affect the patient's health, leading to unnecessary tests and increasing the costs of healthcare. In particular, the lack of specialists in otolaryngology in third world countries forces patients to seek medical attention from general practitioners, whom might not have enough training and experience for making correct diagnosis in this field. To tackle this problem, we propose and test a computer-aided system based on machine learning models and image processing techniques for otoscopic examination, as a support for a more accurate diagnosis of ear conditions at primary care before specialist referral; in particular, for myringosclerosis, earwax plug, and chronic otitis media. To characterize the tympanic membrane and ear canal for each condition, we implemented three different feature extraction methods: color coherence vector, discrete cosine transform, and filter bank. We also considered three machine learning algorithms: support vector machine (SVM), k-nearest neighbor (k-NN) and decision trees to develop the ear condition predictor model. To conduct the research, our database included 160 images as testing set and 720 images as training and validation sets of 180 patients. We repeatedly trained the learning models using the training dataset and evaluated them using the validation dataset to thus obtain the best feature extraction method and learning model that produce the highest validation accuracy. The results showed that the SVM and k-NN presented the best performance followed by decision trees model. Finally, we performed a classification stage-i.e., diagnosis-using testing data, where the SVM model achieved an average classification accuracy of 93.9%, average sensitivity of 87.8%, average specificity of 95.9%, and average positive predictive value of 87.7%. The results show that this system might be used for general practitioners as a reference to make better decisions in the ear pathologies diagnosis.

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Section: Introductionmentioning

confidence: 99%

Computer-aided diagnosis of external and middle ear conditions: A machine learning approach

et al. 2020

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“…This enabled creating an emotion and side-effect profile for each patient participating in OCSG, thus transforming their free-flowing, unstructured posts into 'real-life' patient-reported outcomes. PRIME has been successfully validated and demonstrated across several research endeavors; the extraction and investigation of patient emotions and clinical factors [23,24], the comparative analysis of patient-reported outcomes for different treatment types [25], and the study of online social influences on patient behaviours, decisions and emotions from diagnosis to recovery [19]. Outcomes of PRIME were compared with outcomes of three clinical trials [26][27][28] to assess the reliability and the validity of using PRIME [23] (Detailed information regarding PRIME is provided in S1 Fig).…”

Section: Methodsmentioning

confidence: 99%

“…OCSG are organized as discussions initiated by a question, a comment or an experience that receives responses from other patients. An active, globally available, high volume OCSG is defined as having at least 100 new conversations per week [19]. The following table shows the OCSG selected for this study (Table 1).…”

Section: Participantsmentioning

confidence: 99%

Can online support groups address psychological morbidity of cancer patients? An artificial intelligence based investigation of prostate cancer trajectories

et al. 2020

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Background Online Cancer Support Groups (OCSG) are becoming an increasingly vital source of information, experiences and empowerment for patients with cancer. Despite significant contributions to physical, psychological and emotional wellbeing of patients, OCSG are yet to be formally recognised and used in multidisciplinary cancer support programs. This study highlights the opportunity of using Artificial Intelligence (AI) in OCSG to address psychological morbidity, with supporting empirical evidence from prostate cancer (PCa) patients. Methods A validated framework of AI techniques and Natural Language Processing (NLP) methods, was used to investigate PCa patient activities based on conversations in ten international OCSG (18,496 patients-277,805 conversations). The specific focus was on activities that indicate psychological morbidity; the reasons for joining OCSG, deep emotions and the variation from joining through to milestones in the cancer trajectory. Comparative analyses were conducted using t-tests, One-way ANOVA and Tukey-Kramer post-hoc analysis. Findings PCa patients joined OCSG at four key phases of psychological distress; diagnosis, treatment, side-effects, and recurrence, the majority group was 'treatment' (61.72%). The four groups varied in expression of the intense emotional burden of cancer. The 'side-effects' group expressed increased negative emotions during the first month compared to other groups (p<0.01). A comparison of pre-treatment vs post-treatment emotions showed that

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“…Hence, unsupervised learning is becoming one of the most important and challenging topics in Machine Learning (ML) and AI. The Self-Organizing Map (SOM) proposed by Kohonen [39] is one of the most popular Artificial Neural Networks (ANNs) in the unsupervised learning category [64], inspired from the cortical synaptic plasticity and used in a large range of applications [65] going from high-dimensional data analysis to more recent developments such as identification of social media trends [66], incremental change detection [67] and energy consumption minimization on sensor networks [68]. We introduced in Reference [52] the problem of post-labeled unsupervised learning: no label is available during SOM training then very few labels are available for assigning each neuron the class it represents.…”

Section: Unimodal Post-labeled Unsupervised Learning With Self-organimentioning

confidence: 99%

Brain-Inspired Self-Organization with Cellular Neuromorphic Computing for Multimodal Unsupervised Learning

2020

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Cortical plasticity is one of the main features that enable our ability to learn and adapt in our environment. Indeed, the cerebral cortex self-organizes itself through structural and synaptic plasticity mechanisms that are very likely at the basis of an extremely interesting characteristic of the human brain development: the multimodal association. In spite of the diversity of the sensory modalities, like sight, sound and touch, the brain arrives at the same concepts (convergence). Moreover, biological observations show that one modality can activate the internal representation of another modality when both are correlated (divergence). In this work, we propose the Reentrant Self-Organizing Map (ReSOM), a brain-inspired neural system based on the reentry theory using Self-Organizing Maps and Hebbian-like learning. We propose and compare different computational methods for unsupervised learning and inference, then quantify the gain of the ReSOM in a multimodal classification task. The divergence mechanism is used to label one modality based on the other, while the convergence mechanism is used to improve the overall accuracy of the system. We perform our experiments on a constructed written/spoken digits database and a Dynamic Vision Sensor (DVS)/EletroMyoGraphy (EMG) hand gestures database. The proposed model is implemented on a cellular neuromorphic architecture that enables distributed computing with local connectivity. We show the gain of the so-called hardware plasticity induced by the ReSOM, where the system’s topology is not fixed by the user but learned along the system’s experience through self-organization.

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Machine learning to support social media empowered patients in cancer care and cancer treatment decisions

Cited by 79 publications

References 76 publications

Computer-aided diagnosis of external and middle ear conditions: A machine learning approach

Computer-aided diagnosis of external and middle ear conditions: A machine learning approach

Can online support groups address psychological morbidity of cancer patients? An artificial intelligence based investigation of prostate cancer trajectories

Brain-Inspired Self-Organization with Cellular Neuromorphic Computing for Multimodal Unsupervised Learning

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