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10.2196/27460 http://scihub22266oqcxt.onion/10.2196/27460 33882012!8104000!33882012     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Med+Internet+Res 2021 ; 23 (5): e27460 Nephropedia Template TP {{tp|p=33882012|t=2021. Medical Specialty Recommendations by an Artificial Intelligence Chatbot on a Smartphone: Development and Deployment |pdf=|usr=}}{{33882012}} gab.com Text Medical Specialty Recommendations by an Artificial Intelligence Chatbot on a Smartphone: Development and Deployment JO: J Med Internet Res http://www.kidney.de/pget.php?&tw=1&pmid=33882012 #FOAvip BACKGROUND: The COVID-19 pandemic has limited daily activities and even contact between patients and primary care providers. This makes it more difficult to provide adequate primary care services, which include connecting patients to an appropriate medical specialist. A smartphone-compatible artificial intelligence (AI) chatbot that classifies patients' symptoms and recommends the appropriate medical specialty could provide a valuable solution. OBJECTIVE: In order to establish a contactless method of recommending the appropriate medical specialty, this study aimed to construct a deep learning-based natural language processing (NLP) pipeline and to develop an AI chatbot that can be used on a smartphone. METHODS: We collected 118,008 sentences containing information on symptoms with labels (medical specialty), conducted data cleansing, and finally constructed a pipeline of 51,134 sentences for this study. Several deep learning models, including 4 different long short-term memory (LSTM) models with or without attention and with or without a pretrained FastText embedding layer, as well as bidirectional encoder representations from transformers for NLP, were trained and validated using a randomly selected test data set. The performance of the models was evaluated on the basis of the precision, recall, F(1)-score, and area under the receiver operating characteristic curve (AUC). An AI chatbot was also designed to make it easy for patients to use this specialty recommendation system. We used an open-source framework called "Alpha" to develop our AI chatbot. This takes the form of a web-based app with a frontend chat interface capable of conversing in text and a backend cloud-based server application to handle data collection, process the data with a deep learning model, and offer the medical specialty recommendation in a responsive web that is compatible with both desktops and smartphones. RESULTS: The bidirectional encoder representations from transformers model yielded the best performance, with an AUC of 0.964 and F(1)-score of 0.768, followed by LSTM model with embedding vectors, with an AUC of 0.965 and F(1)-score of 0.739. Considering the limitations of computing resources and the wide availability of smartphones, the LSTM model with embedding vectors trained on our data set was adopted for our AI chatbot service. We also deployed an Alpha version of the AI chatbot to be executed on both desktops and smartphones. CONCLUSIONS: With the increasing need for telemedicine during the current COVID-19 pandemic, an AI chatbot with a deep learning-based NLP model that can recommend a medical specialty to patients through their smartphones would be exceedingly useful. This chatbot allows patients to identify the proper medical specialist in a rapid and contactless manner, based on their symptoms, thus potentially supporting both patients and primary care providers. Twit Text FOAVip Medical Specialty Recommendations by an Artificial Intelligence Chatbot on a Smartphone: Development and Deployment ????J Med Internet Res http://www.kidney.de/pget.php?&tw=1&pmid=33882012 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33882012 #FOAvip English Wikipedia <ref>{{Cite pmid|33882012}}</ref> Medical Specialty Recommendations by an Artificial Intelligence Chatbot on a Smartphone: Development and Deployment #MMPMID33882012 Lee H; Kang J; Yeo J J Med Internet Res 2021[May]; 23 (5): e27460 PMID33882012show ga BACKGROUND: The COVID-19 pandemic has limited daily activities and even contact between patients and primary care providers. This makes it more difficult to provide adequate primary care services, which include connecting patients to an appropriate medical specialist. A smartphone-compatible artificial intelligence (AI) chatbot that classifies patients' symptoms and recommends the appropriate medical specialty could provide a valuable solution. OBJECTIVE: In order to establish a contactless method of recommending the appropriate medical specialty, this study aimed to construct a deep learning-based natural language processing (NLP) pipeline and to develop an AI chatbot that can be used on a smartphone. METHODS: We collected 118,008 sentences containing information on symptoms with labels (medical specialty), conducted data cleansing, and finally constructed a pipeline of 51,134 sentences for this study. Several deep learning models, including 4 different long short-term memory (LSTM) models with or without attention and with or without a pretrained FastText embedding layer, as well as bidirectional encoder representations from transformers for NLP, were trained and validated using a randomly selected test data set. The performance of the models was evaluated on the basis of the precision, recall, F(1)-score, and area under the receiver operating characteristic curve (AUC). An AI chatbot was also designed to make it easy for patients to use this specialty recommendation system. We used an open-source framework called "Alpha" to develop our AI chatbot. This takes the form of a web-based app with a frontend chat interface capable of conversing in text and a backend cloud-based server application to handle data collection, process the data with a deep learning model, and offer the medical specialty recommendation in a responsive web that is compatible with both desktops and smartphones. RESULTS: The bidirectional encoder representations from transformers model yielded the best performance, with an AUC of 0.964 and F(1)-score of 0.768, followed by LSTM model with embedding vectors, with an AUC of 0.965 and F(1)-score of 0.739. Considering the limitations of computing resources and the wide availability of smartphones, the LSTM model with embedding vectors trained on our data set was adopted for our AI chatbot service. We also deployed an Alpha version of the AI chatbot to be executed on both desktops and smartphones. CONCLUSIONS: With the increasing need for telemedicine during the current COVID-19 pandemic, an AI chatbot with a deep learning-based NLP model that can recommend a medical specialty to patients through their smartphones would be exceedingly useful. This chatbot allows patients to identify the proper medical specialist in a rapid and contactless manner, based on their symptoms, thus potentially supporting both patients and primary care providers. |*Deep Learning[MESH] |*Referral and Consultation[MESH] |*Smartphone[MESH] |COVID-19/*epidemiology[MESH] |Humans[MESH] |Pandemics[MESH] |Primary Health Care/*methods[MESH] |SARS-CoV-2/isolation & purification[MESH] |Specialization[MESH]Medical Specialty Recommendations by an Artificial Intelligence Chatbo(epmc).pdf DeepDyve Pubget Overpricing