A Machine Learning Approach for the Automated Interpretation of Plasma Amino Acid Profiles.
BACKGROUND: Plasma amino acid (PAA) profiles are used in routine clinical practice for the diagnosis and monitoring of inherited disorders of amino acid metabolism, organic acidemias, and urea cycle defects. Interpretation of PAA profiles is complex and requires substantial training and expertise to...
| Publicado en: | Clinical Chemistry Vol. 66; no. 9; pp. 1210 - 1219 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Oxford University Press / USA
Sep2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=147088097&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 147088097 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00099147 10CS jtl: Clinical Chemistry issn: 00099147 maglogo: N pubinfo: dt: Sep2020 vid: 66 iid: 9 pid: 622 pub: Oxford University Press / USA artinfo: ui: 147088097 10.1093/clinchem/hvaa134 147088097 ppf: 1210 ppct: 9 formats: fmt: @attributes: type: P tig: atl: A Machine Learning Approach for the Automated Interpretation of Plasma Amino Acid Profiles. aug: au: Wilkes, Edmund H. Emmett, Erin Beltran, Luisa Woodward, Gary M. Carling, Rachel S. affil: Department of Clinical Biochemistry, North West London Pathology, Imperial College Healthcare NHS Trust, Charing Cross Hospital, Hammersmith, London, UK sug: ab: BACKGROUND: Plasma amino acid (PAA) profiles are used in routine clinical practice for the diagnosis and monitoring of inherited disorders of amino acid metabolism, organic acidemias, and urea cycle defects. Interpretation of PAA profiles is complex and requires substantial training and expertise to perform. Given previous demonstrations of the ability of machine learning (ML) algorithms to interpret complex clinical biochemistry data, we sought to determine if ML-derived classifiers could interpret PAA profiles with high predictive performance. METHODS: We collected PAA profiling data routinely performed within a clinical biochemistry laboratory (2084 profiles) and developed decision support classi- fiers with several ML algorithms. We tested the generalization performance of each classifier using a nested cross-validation (CV) procedure and examined the effect of various subsampling, feature selection, and ensemble learning strategies. RESULTS: The classifiers demonstrated excellent predictive performance, with the 3 ML algorithms tested producing comparable results. The best-performing ensemble binary classifier achieved a mean precision-recall (PR) AUC of 0.957 (95% CI 0.952, 0.962) and the best-performing ensemble multiclass classifier achieved a mean F4 score of 0.788 (0.773, 0.803). CONCLUSIONS: This work builds upon previous demonstrations of the utility of ML-derived decision support tools in clinical biochemistry laboratories. Our findings suggest that, pending additional validation studies, such tools could potentially be used in routine clinical practice to streamline and aid the interpretation of PAA profiles. This would be particularly useful in laboratories with limited resources and large workloads. We provide the necessary code for other laboratories to develop their own decision support tools. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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