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In order to access these data, approval is first required through the NT Department of Health/Menzies School of Health Research HREC (email: ", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "The study uses two human health-related data sets, both of which require Human Research Ethics Committee (HREC) approval for access and analysis, and both of which are third-party and require direct application to data custodians. 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Once granted HREC approval, data custodians are as follows: first, mortality data for the Northern Territory from 1980-2019 available through Australian Births Deaths and Marriages (email: ", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "), referencing this study (reference 2021-4056). Once granted HREC approval, data custodians are as follows: first, mortality data for the Northern Territory from 1980-2019 available through Australian Births Deaths and Marriages (email: "}}]}, "properties": [], "parentId": "510", "childIds": [], "backendDOMNodeId": 1174}, {"nodeId": "718", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "agd.registrargeneral@nt.gov.au", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "agd.registrargeneral@nt.gov.au"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "510", "childIds": ["1010"], "backendDOMNodeId": 1175, "browsergym_id": "2dd11ea9-800e-41f6"}, {"nodeId": "719", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "). The second set, all aeromedical retrievals in the NT from 2018-2019, is available through a combination of Careflight NT clinical retrieval database (email: ", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "). The second set, all aeromedical retrievals in the NT from 2018-2019, is available through a combination of Careflight NT clinical retrieval database (email: "}}]}, "properties": [], "parentId": "510", "childIds": [], "backendDOMNodeId": 1177}, {"nodeId": "720", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "helppoint@careflight.org", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "helppoint@careflight.org"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "510", "childIds": ["1011"], "backendDOMNodeId": 1178, "browsergym_id": "f3937282-a234-4a0c"}, {"nodeId": "721", "ignored": false, "role": {"type": 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"backendDOMNodeId": 1189, "browsergym_id": "55bfb68a-8ecf-42d9"}, {"nodeId": "727", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "The authors have declared that no competing interests exist.", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "The authors have declared that no competing interests exist."}}]}, "properties": [], "parentId": "512", "childIds": [], "backendDOMNodeId": 1191}, {"nodeId": "728", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "Introduction", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "Introduction"}}]}, "properties": [], "parentId": "514", "childIds": [], "backendDOMNodeId": 1196}, {"nodeId": "729", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "Electronic health records represent a wealth of information for health service researchers, but the utility is limited by the challenges of extracting meaningful information from vast datasets of unstructured text [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "Electronic health records represent a wealth of information for health service researchers, but the utility is limited by the challenges of extracting meaningful information from vast datasets of unstructured text ["}}]}, "properties": [], "parentId": "516", "childIds": [], "backendDOMNodeId": 1199}, {"nodeId": "730", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "1", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "1"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "516", "childIds": ["1015"], "backendDOMNodeId": 1200, "browsergym_id": "17652e94-1d6e-4812"}, {"nodeId": "731", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "]. Machine learning (ML) tools can be used to classify free text from clinical notes into categories, with useful clinical and research applications.", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "]. Machine learning (ML) tools can be used to classify free text from clinical notes into categories, with useful clinical and research applications."}}]}, "properties": [], "parentId": "516", "childIds": [], "backendDOMNodeId": 1202}, {"nodeId": "732", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "ML has been used to classify cases into a medical speciality type based on clinical notes from inpatient and outpatient healthcare encounters [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "ML has been used to classify cases into a medical speciality type based on clinical notes from inpatient and outpatient healthcare encounters ["}}]}, "properties": [], "parentId": "518", "childIds": [], "backendDOMNodeId": 1206}, {"nodeId": "733", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "2", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "2"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "518", "childIds": ["1016"], "backendDOMNodeId": 1207, "browsergym_id": "9461315d-2a2f-484c"}, {"nodeId": "734", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "]. Similarly, ML has been used to categorise healthcare encounters as either related or not related to falls, to inform clinical and health service interventions [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "]. Similarly, ML has been used to categorise healthcare encounters as either related or not related to falls, to inform clinical and health service interventions ["}}]}, "properties": [], "parentId": "518", "childIds": [], "backendDOMNodeId": 1209}, {"nodeId": "735", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "3", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "3"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "518", "childIds": ["1017"], "backendDOMNodeId": 1210, "browsergym_id": "87ff8d2a-367f-4b43"}, {"nodeId": "736", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "]. In these examples, ML tools have utilised natural language processing techniques that require significant computer programming and data science expertise, which may be beyond the scope of clinician researchers. Furthermore, the ML tool must first learn from a labelled training data set, which may be difficult, time-consuming, or expensive to obtain [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "]. In these examples, ML tools have utilised natural language processing techniques that require significant computer programming and data science expertise, which may be beyond the scope of clinician researchers. Furthermore, the ML tool must first learn from a labelled training data set, which may be difficult, time-consuming, or expensive to obtain ["}}]}, "properties": [], "parentId": "518", "childIds": [], "backendDOMNodeId": 1212}, {"nodeId": "737", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "4", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "4"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "518", "childIds": ["1018"], "backendDOMNodeId": 1213, "browsergym_id": "9b321676-6667-4c72"}, {"nodeId": "738", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "\u2013", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "\u2013"}}]}, "properties": [], "parentId": "518", "childIds": [], "backendDOMNodeId": 1215}, {"nodeId": "739", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "6", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "6"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "518", "childIds": ["1019"], "backendDOMNodeId": 1216, "browsergym_id": "2b7c55e7-f56d-49a4"}, {"nodeId": "740", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "]. There have been recent software developments that make ML readily available to a broader range of low-resource research challenges [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "]. There have been recent software developments that make ML readily available to a broader range of low-resource research challenges ["}}]}, "properties": [], "parentId": "518", "childIds": [], "backendDOMNodeId": 1218}, {"nodeId": "741", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "7", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "7"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "518", "childIds": ["1020"], "backendDOMNodeId": 1219, "browsergym_id": "1b193d29-3eec-4fe3"}, {"nodeId": "742", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "], although such readily available tools have yet to appear as mainstream research tools.", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "], although such readily available tools have yet to appear as mainstream research tools."}}]}, "properties": [], "parentId": "518", "childIds": [], "backendDOMNodeId": 1221}, {"nodeId": "743", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "The Human-in-the-Loop (HITL) approach incorporates human skills and expertise into ML processes, including creating labelled datasets [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "The Human-in-the-Loop (HITL) approach incorporates human skills and expertise into ML processes, including creating labelled datasets ["}}]}, "properties": [], "parentId": "520", "childIds": [], "backendDOMNodeId": 1225}, {"nodeId": "744", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "8", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "8"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "520", "childIds": ["1021"], "backendDOMNodeId": 1226, "browsergym_id": "185c8057-59f7-4273"}, {"nodeId": "745", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": ", ", "sources": [{"type": "contents", "value": {"type": "computedString", "value": ", "}}]}, "properties": [], "parentId": "520", "childIds": [], "backendDOMNodeId": 1228}, {"nodeId": "746", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "9", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "9"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "520", "childIds": ["1022"], "backendDOMNodeId": 1229, "browsergym_id": "d7dce388-de32-4aa2"}, {"nodeId": "747", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "]. Compared to an automated approach, this interactive approach may achieve greater accuracy with fewer training labels because of the human expert capacity to identify patterns from relatively few samples [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "]. Compared to an automated approach, this interactive approach may achieve greater accuracy with fewer training labels because of the human expert capacity to identify patterns from relatively few samples ["}}]}, "properties": [], "parentId": "520", "childIds": [], "backendDOMNodeId": 1231}, {"nodeId": "748", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "10", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "10"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "520", "childIds": ["1023"], "backendDOMNodeId": 1232, "browsergym_id": "671eb524-bb6e-4f69"}, {"nodeId": "749", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "].", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "]."}}]}, "properties": [], "parentId": "520", "childIds": [], "backendDOMNodeId": 1234}, {"nodeId": "750", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "Investigators requiring bespoke classification tasks of large-scale datasets must either create their own training dataset or use previously labelled datasets. For a research project investigating the impact of heat waves on specific health outcomes, there were no published examples of use of ML tools to classify prehospital aeromedical triage notes into medical specialty type. Furthermore, previous attempts to classify death certificate records into cause-of-death categories have achieved limited accuracy due to high number of categories and automated approaches without clinician input.", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "Investigators requiring bespoke classification tasks of large-scale datasets must either create their own training dataset or use previously labelled datasets. For a research project investigating the impact of heat waves on specific health outcomes, there were no published examples of use of ML tools to classify prehospital aeromedical triage notes into medical specialty type. Furthermore, previous attempts to classify death certificate records into cause-of-death categories have achieved limited accuracy due to high number of categories and automated approaches without clinician input."}}]}, "properties": [], "parentId": "522", "childIds": [], "backendDOMNodeId": 1238}, {"nodeId": "751", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "The objective of this study was to assess the accuracy and speed of the HITL ML methodological approach for clinician researchers to classify clinical notes into customised categories for future health services research purposes.", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "The objective of this study was to assess the accuracy and speed of the HITL ML methodological approach for clinician researchers to classify clinical notes into customised categories for future 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The HITL labelling process took 124 minutes and was 94.7% accurate in classifying death certificate data into 3 categories related to stroke diagnosis. For aeromedical retrieval triage data, the HITL labelling process took 144 minutes and was 92.4% accurate in classifying cases into 5 categories related to medical specialty type.", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "In this study, historically collected free-text data from healthcare records was classified into customised categories for use in health services research projects using a human-in-the-loop interactive ML methodology. The HITL labelling process took 124 minutes and was 94.7% accurate in classifying death certificate data into 3 categories related to stroke diagnosis. For aeromedical retrieval triage data, the HITL labelling process took 144 minutes and was 92.4% accurate in classifying cases into 5 categories related to medical specialty type."}}]}, "properties": [], "parentId": "540", "childIds": [], "backendDOMNodeId": 1724}, {"nodeId": "842", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "As our study demonstrates, the challenge of coding complex natural language databases can have simple solutions using well-developed frameworks for automating ML algorithms (we used ML.NET). This allowed us to use our own understanding of the datasets that we were applying our research question to, and then iteratively train the model until we were satisfied it met our accuracy requirements. Prior to the availability of off-the-shelf software, the implementation of ML to manipulate data in such a way required high-level software engineers. With the advent of AutoML and in combination with HITL, our study proves the democratising of ML that will make such techniques available to laboratories that may not have had sufficient funding to employ ML specialists.", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "As our study demonstrates, the challenge of coding complex natural language databases can have simple solutions using well-developed frameworks for automating ML algorithms (we used ML.NET). This allowed us to use our own understanding of the datasets that we were applying our research question to, and then iteratively train the model until we were satisfied it met our accuracy requirements. Prior to the availability of off-the-shelf software, the implementation of ML to manipulate data in such a way required high-level software engineers. With the advent of AutoML and in combination with HITL, our study proves the democratising of ML that will make such techniques available to laboratories that may not have had sufficient funding to employ ML specialists."}}]}, "properties": [], "parentId": "542", "childIds": [], "backendDOMNodeId": 1728}, {"nodeId": "843", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "Previous studies that classify death certificate records into International Classification of Disease (ICD-10) diagnostic codes have not yielded the accuracy of this study [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "Previous studies that classify death certificate records into International Classification of Disease (ICD-10) diagnostic codes have not yielded the accuracy of this study ["}}]}, "properties": [], "parentId": "544", "childIds": [], "backendDOMNodeId": 1732}, {"nodeId": "844", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "11", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "11"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "544", "childIds": ["1113"], "backendDOMNodeId": 1733, "browsergym_id": "059c652a-5a5d-40bd"}, {"nodeId": "845", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "\u2013", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "\u2013"}}]}, "properties": [], "parentId": "544", "childIds": [], "backendDOMNodeId": 1735}, {"nodeId": "846", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "13", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "13"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "544", "childIds": ["1114"], "backendDOMNodeId": 1736, "browsergym_id": "f23a8d87-db37-4a79"}, {"nodeId": "847", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "], and thus were not suitable for the task of identifying stroke-related cases from a jurisdictional death registry database. Challenges to accuracy in this field include the high number of categories, unbalanced class frequencies, non-conventional language, and abbreviations, and cross-over between categories. The interactive HITL approach allows the clinician to define and customise a small number of categories that are relevant to clinical, health services or research needs. Furthermore, allowing the content-expert to select cases in the training set for labelling may achieve better accuracy with smaller samples, due to the efficiency of human pattern-recognition compared to automated processes.", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "], and thus were not suitable for the task of identifying stroke-related cases from a jurisdictional death registry database. Challenges to accuracy in this field include the high number of categories, unbalanced class frequencies, non-conventional language, and abbreviations, and cross-over between categories. The interactive HITL approach allows the clinician to define and customise a small number of categories that are relevant to clinical, health services or research needs. Furthermore, allowing the content-expert to select cases in the training set for labelling may achieve better accuracy with smaller samples, due to the efficiency of human pattern-recognition compared to automated processes."}}]}, "properties": [], "parentId": "544", "childIds": [], "backendDOMNodeId": 1738}, {"nodeId": "848", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "ML techniques have previously been applied to prehospital triage data, but has utilised numerical or categorical variables, such as heart rate or temperature rather than free text in clinical notes [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "ML techniques have previously been applied to prehospital triage data, but has utilised numerical or categorical variables, such as heart rate or temperature rather than free text in clinical notes ["}}]}, "properties": [], "parentId": "546", "childIds": [], "backendDOMNodeId": 1742}, {"nodeId": "849", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "14", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "14"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "546", "childIds": ["1115"], "backendDOMNodeId": 1743, "browsergym_id": "aec93e37-7c88-438f"}, {"nodeId": "850", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "]. A supervised ML tool that classified clinical notes into medical specialty types achieved a high degree of accuracy. In this study, 2 expert clinicians labelled 431 clinical notes by medical specialty category. The authors do not report on the time taken for the clinical experts to complete the labelling, nor the time for computer programmers and data scientists to compare and select the best-performing classifier technique.", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "]. A supervised ML tool that classified clinical notes into medical specialty types achieved a high degree of accuracy. In this study, 2 expert clinicians labelled 431 clinical notes by medical specialty category. The authors do not report on the time taken for the clinical experts to complete the labelling, nor the time for computer programmers and data scientists to compare and select the best-performing classifier technique."}}]}, "properties": [], "parentId": "546", "childIds": [], "backendDOMNodeId": 1745}, {"nodeId": "851", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "Furthermore, it is unknown how this MLA would perform when applied to novel datasets. Given the variations in language and abbreviations in different health care settings, machine learning tools will perform best when applied to a dataset that is derived from the same source as the training dataset [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "Furthermore, it is unknown how this MLA would perform when applied to novel datasets. Given the variations in language and abbreviations in different health care settings, machine learning tools will perform best when applied to a dataset that is derived from the same source as the training dataset ["}}]}, "properties": [], "parentId": "548", "childIds": [], "backendDOMNodeId": 1749}, {"nodeId": "852", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "15", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "15"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "548", "childIds": ["1116"], "backendDOMNodeId": 1750, "browsergym_id": "0ecefa7e-82b2-434e"}, {"nodeId": "853", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "]. For this reason, researchers may prefer to create a custom ML tool for each new project, rather than utilising a ML algorithm that was trained on a dissimilar dataset.", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "]. For this reason, researchers may prefer to create a custom ML tool for each new project, rather than utilising a ML algorithm that was trained on a dissimilar dataset."}}]}, "properties": [], "parentId": "548", "childIds": [], "backendDOMNodeId": 1752}, {"nodeId": "854", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "Conclusion", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "Conclusion"}}]}, "properties": [], "parentId": "550", "childIds": [], "backendDOMNodeId": 1758}, {"nodeId": "855", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "An interactive approach to machine learning that uses readily available off-the-shelf ML software and medical experts as the \u201chuman-in-the-loop\u201d for training data 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The aim of this study was to (a) develop interactive active machine-learning model training methodology using readily available software that was (b) easily adaptable to a wide range of natural language databases and allowed customised researcher-defined categories, and then (c) evaluate the accuracy and speed of this model for classifying free text from two unique and unrelated clinical notes into coded data. A user interface for medical experts to train and evaluate the algorithm was created. Data requiring coding in the form of two independent databases of free-text clinical notes, each of unique natural language structure. Medical experts defined categories relevant to research projects and performed \u2018label-train-evaluate\u2019 loops on the training data set. A separate dataset was used for validation, with the medical experts blinded to the label given by the algorithm. The first dataset was 32,034 death certificate records from Northern Territory Births Deaths and Marriages, which were coded into 3 categories: haemorrhagic stroke, ischaemic stroke or no stroke. The second dataset was 12,039 recorded episodes of aeromedical retrieval from two prehospital and retrieval services in Northern Territory, Australia, which were coded into 5 categories: medical, surgical, trauma, obstetric or psychiatric. For the first dataset, macro-accuracy of the algorithm was 94.7%. For the second dataset, macro-accuracy was 92.4%. The time taken to develop and train the algorithm was 124 minutes for the death certificate coding, and 144 minutes for the aeromedical retrieval coding. This machine-learning training method was able to classify free-text clinical notes quickly and accurately from two different health datasets into categories of relevance to clinicians undertaking health service research.", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "Classifying free-text from historical databases into research-compatible formats is a barrier for clinicians undertaking audit and research projects. The aim of this study was to (a) develop interactive active machine-learning model training methodology using readily available software that was (b) easily adaptable to a wide range of natural language databases and allowed customised researcher-defined categories, and then (c) evaluate the accuracy and speed of this model for classifying free text from two unique and unrelated clinical notes into coded data. A user interface for medical experts to train and evaluate the algorithm was created. Data requiring coding in the form of two independent databases of free-text clinical notes, each of unique natural language structure. Medical experts defined categories relevant to research projects and performed \u2018label-train-evaluate\u2019 loops on the training data set. A separate dataset was used for validation, with the medical experts blinded to the label given by the algorithm. The first dataset was 32,034 death certificate records from Northern Territory Births Deaths and Marriages, which were coded into 3 categories: haemorrhagic stroke, ischaemic stroke or no stroke. The second dataset was 12,039 recorded episodes of aeromedical retrieval from two prehospital and retrieval services in Northern Territory, Australia, which were coded into 5 categories: medical, surgical, trauma, obstetric or psychiatric. For the first dataset, macro-accuracy of the algorithm was 94.7%. For the second dataset, macro-accuracy was 92.4%. The time taken to develop and train the algorithm was 124 minutes for the death certificate coding, and 144 minutes for the aeromedical retrieval coding. 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