Objectives:From the view of everyday practices and the socio-technical coordination lens,this study aimed to analyz the gap between creators’intention and the users’implementation(mainly nursing staff and social wor...Objectives:From the view of everyday practices and the socio-technical coordination lens,this study aimed to analyz the gap between creators’intention and the users’implementation(mainly nursing staff and social workers)of an alert system in assisted living communities.Methods:Qualitative methods were employed by way of five user interviews and focus groups with six system developers.Modeling instruments were applied for data collection to analyze the different clinical workflows versus the expectations of the system development team.Results:Results indicate that the clinical workflow changed over time,which led to a mismatch of nurse care coordination,social practices,and technology use.The results show different mental models of the socio-technical practice.Applying the coordination theory,the following recommendations could be developed to overcome the mismatch.First,it is recommended that nursing staff set goals together.Second,a communication rhythmwith the nursing staff and developer teams should be established,with guided questions to facilitate the conversation,to shed light on the different workflows and the difference in social practices when using sensor technologies or alert systems.Third,a checklist for new employees should be created so they know how and on which devices to use the alert system.Fourth,the user experience with the alert system should be improved(e.g.,an improved user interface).Conclusions:This work indicates recommendations to close the mental model gap to overcome the mismatch between optimal use of the alert system and how the nursing staff is actually using it.展开更多
Understanding complex biological pathways,including gene–gene interactions and gene regulatory networks,is critical for exploring disease mechanisms and drug development.Manual literature curation of biological pathw...Understanding complex biological pathways,including gene–gene interactions and gene regulatory networks,is critical for exploring disease mechanisms and drug development.Manual literature curation of biological pathways cannot keep up with the exponential growth of new discoveries in the literature.Large-scale language models(LLMs)trained on extensive text corpora contain rich biological information,and they can be mined as a biological knowledge graph.This study assesses 21 LLMs,including both application programming interface(API)-based models and open-source models in their capacities of retrieving biological knowledge.The evaluation focuses on predicting gene regulatory relations(activation,inhibition,and phosphorylation)and the Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway components.Results indicated a significant disparity in model performance.API-based models GPT-4 and Claude-Pro showed superior performance,with an F1 score of 0.4448 and 0.4386 for the gene regulatory relation prediction,and a Jaccard similarity index of 0.2778 and 0.2657 for the KEGG pathway prediction,respectively.Open-source models lagged behind their API-based counterparts,whereas Falcon-180b and llama2-7b had the highest F1 scores of 0.2787 and 0.1923 in gene regulatory relations,respectively.The KEGG pathway recognition had a Jaccard similarity index of 0.2237 for Falcon-180b and 0.2207 for llama2-7b.Our study suggests that LLMs are informative in gene network analysis and pathway mapping,but their effectiveness varies,necessitating careful model selection.This work also provides a case study and insight into using LLMs das knowledge graphs.Our code is publicly available at the website of GitHub(Muh-aza).展开更多
The biomedical literature is a vast and invaluable resource for biomedical research.Integrating knowledge from the literature with biomedical data can help biological studies and the clinical decision-making process.E...The biomedical literature is a vast and invaluable resource for biomedical research.Integrating knowledge from the literature with biomedical data can help biological studies and the clinical decision-making process.Efforts have been made to gather information from the biomedical literature and create biomedical knowledge bases,such as KEGG and Reactome.However,manual curation remains the primary method to retrieve accurate biomedical entities and relationships.Manual curation becomes increasingly challenging and costly as the volume of biomedical publications quickly grows.Fortunately,recent advancements in Artificial Intelligence(AI)technologies offer the potential to automate the process of curating,updating,and integrating knowledge from the literature.Herein,we highlight the AI capabilities to aid in mining knowledge and building the knowledge base from the biomedical literature.展开更多
基金This work was supported by the National Library of Medicine grant #1R01LM01222.
文摘Objectives:From the view of everyday practices and the socio-technical coordination lens,this study aimed to analyz the gap between creators’intention and the users’implementation(mainly nursing staff and social workers)of an alert system in assisted living communities.Methods:Qualitative methods were employed by way of five user interviews and focus groups with six system developers.Modeling instruments were applied for data collection to analyze the different clinical workflows versus the expectations of the system development team.Results:Results indicate that the clinical workflow changed over time,which led to a mismatch of nurse care coordination,social practices,and technology use.The results show different mental models of the socio-technical practice.Applying the coordination theory,the following recommendations could be developed to overcome the mismatch.First,it is recommended that nursing staff set goals together.Second,a communication rhythmwith the nursing staff and developer teams should be established,with guided questions to facilitate the conversation,to shed light on the different workflows and the difference in social practices when using sensor technologies or alert systems.Third,a checklist for new employees should be created so they know how and on which devices to use the alert system.Fourth,the user experience with the alert system should be improved(e.g.,an improved user interface).Conclusions:This work indicates recommendations to close the mental model gap to overcome the mismatch between optimal use of the alert system and how the nursing staff is actually using it.
基金National Institute of General Medical Sciences,Grant/Award Number:R35-GM126985National Institute of Diabetes and Digestive and Kidney Diseases,Grant/Award Number:P30DK092950U.S.National Library of Medicine,Grant/Award Number:LM013392。
文摘Understanding complex biological pathways,including gene–gene interactions and gene regulatory networks,is critical for exploring disease mechanisms and drug development.Manual literature curation of biological pathways cannot keep up with the exponential growth of new discoveries in the literature.Large-scale language models(LLMs)trained on extensive text corpora contain rich biological information,and they can be mined as a biological knowledge graph.This study assesses 21 LLMs,including both application programming interface(API)-based models and open-source models in their capacities of retrieving biological knowledge.The evaluation focuses on predicting gene regulatory relations(activation,inhibition,and phosphorylation)and the Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway components.Results indicated a significant disparity in model performance.API-based models GPT-4 and Claude-Pro showed superior performance,with an F1 score of 0.4448 and 0.4386 for the gene regulatory relation prediction,and a Jaccard similarity index of 0.2778 and 0.2657 for the KEGG pathway prediction,respectively.Open-source models lagged behind their API-based counterparts,whereas Falcon-180b and llama2-7b had the highest F1 scores of 0.2787 and 0.1923 in gene regulatory relations,respectively.The KEGG pathway recognition had a Jaccard similarity index of 0.2237 for Falcon-180b and 0.2207 for llama2-7b.Our study suggests that LLMs are informative in gene network analysis and pathway mapping,but their effectiveness varies,necessitating careful model selection.This work also provides a case study and insight into using LLMs das knowledge graphs.Our code is publicly available at the website of GitHub(Muh-aza).
基金the National Library of Medicine of the National Institute of Health(NIH)award number 5R01LM013392。
文摘The biomedical literature is a vast and invaluable resource for biomedical research.Integrating knowledge from the literature with biomedical data can help biological studies and the clinical decision-making process.Efforts have been made to gather information from the biomedical literature and create biomedical knowledge bases,such as KEGG and Reactome.However,manual curation remains the primary method to retrieve accurate biomedical entities and relationships.Manual curation becomes increasingly challenging and costly as the volume of biomedical publications quickly grows.Fortunately,recent advancements in Artificial Intelligence(AI)technologies offer the potential to automate the process of curating,updating,and integrating knowledge from the literature.Herein,we highlight the AI capabilities to aid in mining knowledge and building the knowledge base from the biomedical literature.
