Probability density and information entropy of machine learning derived intracranial pressure predictions
Even with the powerful statistical parameters derived from the Extreme Gradient Boost (XGB) algorithm, it would be advantageous to define the predicted accuracy to the level of a specific case, particularly when the model output is used to guide clinical decision-making. The probability density func...
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| description | Even with the powerful statistical parameters derived from the Extreme Gradient Boost (XGB) algorithm, it would be advantageous to define the predicted accuracy to the level of a specific case, particularly when the model output is used to guide clinical decision-making. The probability density function (PDF) of the derived intracranial pressure predictions enables the computation of a definite integral around a point estimate, representing the event's probability within a range of values. Seven hold-out test cases used for the external validation of an XGB model underwent retinal vascular pulse and intracranial pressure measurement using modified photoplethysmography and lumbar puncture, respectively. The definite integral ±1 cm water from the median (DIICP) demonstrated a negative and highly significant correlation (-0.5213±0.17, p< 0.004) with the absolute difference between the measured and predicted median intracranial pressure (DiffICPmd). The concordance between the arterial and venous probability density functions was estimated using the two-sample Kolmogorov-Smirnov statistic, extending the distribution agreement across all data points. This parameter showed a statistically significant and positive correlation (0.4942±0.18, p< 0.001) with DiffICPmd. Two cautionary subset cases (Case 8 and Case 9), where disagreement was observed between measured and predicted intracranial pressure, were compared to the seven hold-out test cases. Arterial predictions from both cautionary subset cases converged on a uniform distribution in contrast to all other cases where distributions converged on either log-normal or closely related skewed distributions (gamma, logistic, beta). The mean±standard error of the arterial DIICP from cases 8 and 9 (3.83±0.56%) was lower compared to that of the hold-out test cases (14.14±1.07%) the between group difference was statistically significant (p |
| doi_str_mv | 10.1371/journal.pone.0306028 |
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The probability density function (PDF) of the derived intracranial pressure predictions enables the computation of a definite integral around a point estimate, representing the event's probability within a range of values. Seven hold-out test cases used for the external validation of an XGB model underwent retinal vascular pulse and intracranial pressure measurement using modified photoplethysmography and lumbar puncture, respectively. The definite integral ±1 cm water from the median (DIICP) demonstrated a negative and highly significant correlation (-0.5213±0.17, p< 0.004) with the absolute difference between the measured and predicted median intracranial pressure (DiffICPmd). The concordance between the arterial and venous probability density functions was estimated using the two-sample Kolmogorov-Smirnov statistic, extending the distribution agreement across all data points. This parameter showed a statistically significant and positive correlation (0.4942±0.18, p< 0.001) with DiffICPmd. Two cautionary subset cases (Case 8 and Case 9), where disagreement was observed between measured and predicted intracranial pressure, were compared to the seven hold-out test cases. Arterial predictions from both cautionary subset cases converged on a uniform distribution in contrast to all other cases where distributions converged on either log-normal or closely related skewed distributions (gamma, logistic, beta). The mean±standard error of the arterial DIICP from cases 8 and 9 (3.83±0.56%) was lower compared to that of the hold-out test cases (14.14±1.07%) the between group difference was statistically significant (p<0.03). Although the sample size in this analysis was limited, these results support a dual and complementary analysis approach from independently derived retinal arterial and venous non-invasive intracranial pressure predictions. Results suggest that plotting the PDF and calculating the lower order moments, arterial DIICP, and the two sample Kolmogorov-Smirnov statistic may provide individualized predictive accuracy parameters.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0306028</identifier><identifier>PMID: 38950055</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accuracy ; Adult ; Algorithms ; Artificial intelligence ; Bias ; Biology and Life Sciences ; Blood pressure ; Clinical decision making ; Data points ; Decision making ; Diagnostic tests ; Distribution (Probability theory) ; Entropy ; Entropy (Information theory) ; Female ; Gender ; Humans ; Hypertension ; Intracranial pressure ; Intracranial Pressure - physiology ; Machine Learning ; Male ; Median (statistics) ; Medicine and Health Sciences ; Middle Aged ; Parameters ; Physical Sciences ; Predictions ; Pressure distribution ; Pressure measurement ; Probability ; Probability density function ; Probability density functions ; Probability learning ; Random variables ; Research and Analysis Methods ; Retina ; Skewed distributions ; Specific gravity ; Standard error ; Statistical analysis ; Traumatic brain injury</subject><ispartof>PloS one, 2024-07, Vol.19 (7), p.e0306028</ispartof><rights>Copyright: © 2024 Abdul-Rahman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2024 Public Library of Science</rights><rights>2024 Abdul-Rahman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2024 Abdul-Rahman et al 2024 Abdul-Rahman et al</rights><rights>2024 Abdul-Rahman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c572t-77049caf59bf7ea4b1e9323f139e9bf37c537d3f93b570887180342f1690a13c3</cites><orcidid>0000-0002-0812-8944 ; 0000-0003-0723-6334</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11216561/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11216561/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38950055$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Harris, Alon</contributor><creatorcontrib>Abdul-Rahman, Anmar</creatorcontrib><creatorcontrib>Morgan, William</creatorcontrib><creatorcontrib>Vukmirovic, Aleksandar</creatorcontrib><creatorcontrib>Yu, Dao-Yi</creatorcontrib><title>Probability density and information entropy of machine learning derived intracranial pressure predictions</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Even with the powerful statistical parameters derived from the Extreme Gradient Boost (XGB) algorithm, it would be advantageous to define the predicted accuracy to the level of a specific case, particularly when the model output is used to guide clinical decision-making. 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Results suggest that plotting the PDF and calculating the lower order moments, arterial DIICP, and the two sample Kolmogorov-Smirnov statistic may provide individualized predictive accuracy parameters.</description><subject>Accuracy</subject><subject>Adult</subject><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Bias</subject><subject>Biology and Life Sciences</subject><subject>Blood pressure</subject><subject>Clinical decision making</subject><subject>Data points</subject><subject>Decision making</subject><subject>Diagnostic tests</subject><subject>Distribution (Probability theory)</subject><subject>Entropy</subject><subject>Entropy (Information theory)</subject><subject>Female</subject><subject>Gender</subject><subject>Humans</subject><subject>Hypertension</subject><subject>Intracranial pressure</subject><subject>Intracranial Pressure - physiology</subject><subject>Machine Learning</subject><subject>Male</subject><subject>Median 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Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Abdul-Rahman, Anmar</au><au>Morgan, William</au><au>Vukmirovic, Aleksandar</au><au>Yu, Dao-Yi</au><au>Harris, Alon</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Probability density and information entropy of machine learning derived intracranial pressure predictions</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2024-07-01</date><risdate>2024</risdate><volume>19</volume><issue>7</issue><spage>e0306028</spage><pages>e0306028-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Even with the powerful statistical parameters derived from the Extreme Gradient Boost (XGB) algorithm, it would be advantageous to define the predicted accuracy to the level of a specific case, particularly when the model output is used to guide clinical decision-making. The probability density function (PDF) of the derived intracranial pressure predictions enables the computation of a definite integral around a point estimate, representing the event's probability within a range of values. Seven hold-out test cases used for the external validation of an XGB model underwent retinal vascular pulse and intracranial pressure measurement using modified photoplethysmography and lumbar puncture, respectively. The definite integral ±1 cm water from the median (DIICP) demonstrated a negative and highly significant correlation (-0.5213±0.17, p< 0.004) with the absolute difference between the measured and predicted median intracranial pressure (DiffICPmd). The concordance between the arterial and venous probability density functions was estimated using the two-sample Kolmogorov-Smirnov statistic, extending the distribution agreement across all data points. This parameter showed a statistically significant and positive correlation (0.4942±0.18, p< 0.001) with DiffICPmd. Two cautionary subset cases (Case 8 and Case 9), where disagreement was observed between measured and predicted intracranial pressure, were compared to the seven hold-out test cases. Arterial predictions from both cautionary subset cases converged on a uniform distribution in contrast to all other cases where distributions converged on either log-normal or closely related skewed distributions (gamma, logistic, beta). The mean±standard error of the arterial DIICP from cases 8 and 9 (3.83±0.56%) was lower compared to that of the hold-out test cases (14.14±1.07%) the between group difference was statistically significant (p<0.03). Although the sample size in this analysis was limited, these results support a dual and complementary analysis approach from independently derived retinal arterial and venous non-invasive intracranial pressure predictions. Results suggest that plotting the PDF and calculating the lower order moments, arterial DIICP, and the two sample Kolmogorov-Smirnov statistic may provide individualized predictive accuracy parameters.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>38950055</pmid><doi>10.1371/journal.pone.0306028</doi><tpages>e0306028</tpages><orcidid>https://orcid.org/0000-0002-0812-8944</orcidid><orcidid>https://orcid.org/0000-0003-0723-6334</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Adult Algorithms Artificial intelligence Bias Biology and Life Sciences Blood pressure Clinical decision making Data points Decision making Diagnostic tests Distribution (Probability theory) Entropy Entropy (Information theory) Female Gender Humans Hypertension Intracranial pressure Intracranial Pressure - physiology Machine Learning Male Median (statistics) Medicine and Health Sciences Middle Aged Parameters Physical Sciences Predictions Pressure distribution Pressure measurement Probability Probability density function Probability density functions Probability learning Random variables Research and Analysis Methods Retina Skewed distributions Specific gravity Standard error Statistical analysis Traumatic brain injury |
| title | Probability density and information entropy of machine learning derived intracranial pressure predictions |
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