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• W3201748949 abstract "Abstract Background Biological aging is revealed by physical measures, e . g ., DNA probes or brain scans. Instead, individual differences in mental function are explained by psychological constructs, e.g., intelligence or neuroticism. These constructs are typically assessed by tailored neuropsychological tests that build on expert judgement and require careful interpretation. Could machine learning on large samples from the general population be used to build proxy measures of these constructs that do not require human intervention? Results Here, we built proxy measures by applying machine learning on multimodal MR images and rich sociodemographic information from the largest biomedical cohort to date: the UK Biobank. Objective model comparisons revealed that all proxies captured the target constructs and were as useful, and sometimes more useful than the original measures for characterizing real-world health behavior (sleep, exercise, tobacco, alcohol consumption). We observed this complementarity of proxy measures and original measures when modeling from brain signals or sociodemographic data, capturing multiple health-related constructs. Conclusions Population modeling with machine learning can derive measures of mental health from brain signals and questionnaire data, which may complement or even substitute for psychometric assessments in clinical populations. Key Points We applied machine learning on more than 10.000 individuals from the general population to define empirical approximations of health-related psychological measures that do not require human judgment. We found that machine-learning enriched the given psychological measures via approximation from brain and sociodemographic data: Resulting proxy measures related as well or better to real-world health behavior than the original measures. Model comparisons showed that sociodemographic information contributed most to characterizing psychological traits beyond aging." @default.
• W3201748949 created "2021-10-11" @default.
• W3201748949 creator A5003535842 @default.
• W3201748949 creator A5026762833 @default.
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• W3201748949 creator A5040139150 @default.
• W3201748949 creator A5051587217 @default.
• W3201748949 creator A5074733625 @default.
• W3201748949 date "2020-08-25" @default.
• W3201748949 modified "2023-10-13" @default.
• W3201748949 title "Population modeling with machine learning can enhance measures of mental health" @default.
• W3201748949 cites W1871068556 @default.
• W3201748949 cites W1944439379 @default.
• W3201748949 cites W1964218847 @default.
• W3201748949 cites W1965092590 @default.
• W3201748949 cites W1977465442 @default.
• W3201748949 cites W1980390607 @default.
• W3201748949 cites W1981077350 @default.
• W3201748949 cites W1981457167 @default.
• W3201748949 cites W1982330837 @default.
• W3201748949 cites W1994518954 @default.
• W3201748949 cites W1996048943 @default.
• W3201748949 cites W2006500696 @default.
• W3201748949 cites W2007729962 @default.
• W3201748949 cites W2009494091 @default.
• W3201748949 cites W2016610517 @default.
• W3201748949 cites W2024624851 @default.
• W3201748949 cites W2026251305 @default.
• W3201748949 cites W2035963313 @default.
• W3201748949 cites W2043822013 @default.
• W3201748949 cites W2051037982 @default.
• W3201748949 cites W2057720927 @default.
• W3201748949 cites W2063404606 @default.
• W3201748949 cites W2097167091 @default.
• W3201748949 cites W2099446873 @default.
• W3201748949 cites W2101135654 @default.
• W3201748949 cites W2121585268 @default.
• W3201748949 cites W2127613226 @default.
• W3201748949 cites W2132218683 @default.
• W3201748949 cites W2137229374 @default.
• W3201748949 cites W2140964565 @default.
• W3201748949 cites W2142059961 @default.
• W3201748949 cites W2143285014 @default.
• W3201748949 cites W2151591509 @default.
• W3201748949 cites W2154868463 @default.
• W3201748949 cites W2164481374 @default.
• W3201748949 cites W2167579130 @default.
• W3201748949 cites W2168463527 @default.
• W3201748949 cites W2170596158 @default.
• W3201748949 cites W2208777739 @default.
• W3201748949 cites W2265465794 @default.
• W3201748949 cites W2283684210 @default.
• W3201748949 cites W2421101021 @default.
• W3201748949 cites W2509365953 @default.
• W3201748949 cites W2522628945 @default.
• W3201748949 cites W2530132460 @default.
• W3201748949 cites W2544158810 @default.
• W3201748949 cites W2552208519 @default.
• W3201748949 cites W2565517058 @default.
• W3201748949 cites W2579617530 @default.
• W3201748949 cites W2589870402 @default.
• W3201748949 cites W2590328111 @default.
• W3201748949 cites W2602552939 @default.
• W3201748949 cites W2607804943 @default.
• W3201748949 cites W2613914741 @default.
• W3201748949 cites W2723723801 @default.
• W3201748949 cites W2735059498 @default.
• W3201748949 cites W2788146204 @default.
• W3201748949 cites W2791781046 @default.
• W3201748949 cites W2792919287 @default.
• W3201748949 cites W2814372830 @default.
• W3201748949 cites W2886161411 @default.
• W3201748949 cites W2890996272 @default.
• W3201748949 cites W2911964244 @default.
• W3201748949 cites W2915727102 @default.
• W3201748949 cites W2920590181 @default.
• W3201748949 cites W2951042025 @default.
• W3201748949 cites W2951081425 @default.
• W3201748949 cites W2952824318 @default.
• W3201748949 cites W2963176674 @default.
• W3201748949 cites W2963389298 @default.
• W3201748949 cites W2977685912 @default.
• W3201748949 cites W2982384224 @default.
• W3201748949 cites W2990091959 @default.
• W3201748949 cites W2996053974 @default.
• W3201748949 cites W3006025189 @default.
• W3201748949 cites W3006385477 @default.
• W3201748949 cites W3009640614 @default.
• W3201748949 cites W3010773416 @default.
• W3201748949 cites W3021883334 @default.
• W3201748949 cites W3022130366 @default.
• W3201748949 cites W3027718029 @default.
• W3201748949 cites W3048423955 @default.
• W3201748949 cites W3092538788 @default.
• W3201748949 cites W3111553083 @default.
• W3201748949 cites W3123280175 @default.
• W3201748949 cites W3124158341 @default.
• W3201748949 cites W3153689252 @default.
• W3201748949 cites W3201748949 @default.
• W3201748949 cites W3215186461 @default.

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