Machine learning applications in precision medicine: Overcoming challenges and unlocking potential

TrAC Trends in Analytical Chemistry, Год журнала: 2024, Номер 179, С. 117872 - 117872

Опубликована: Июль 15, 2024

Precision medicine, utilizing genomic and phenotypic data, aims to tailor treatments for individual patients. However, successful implementation into clinical practice is challenging. Machine learning (ML) algorithms have demonstrated incredible capabilities in handling probabilities, managing diverse datasets, are increasingly applied precision medicine research. The key ML applications include classification diagnosis, patient stratification, prognosis, treatment monitoring. offers solutions automated structural elucidation, silico library construction, efficient processing of mass spectrometry raw data. Integration with genome-scale metabolic models (GEMs) provides mechanistic insights genotype-phenotype relationships. In this manuscript, we examine the impact various facets from diagnostics phenotyping personalized strategies. Finally, propose a methodological framework implementing practice, emphasizing step-by-step approach, starting identification needs research questions, followed by development, validation, implementation.

Язык: Английский

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Toward real‐world deployment of machine learning for health care: External validation, continual monitoring, and randomized clinical trials

Health care science, Год журнала: 2024, Номер 3(5), С. 360 - 364

Опубликована: Окт. 1, 2024

In this commentary, we elucidate three indispensable evaluation steps toward the real-world deployment of machine learning within healthcare sector and demonstrate referable examples for diagnostic, therapeutic, prognostic tasks. We encourage researchers to move beyond retrospective within-sample validation, step into practical implementation at bedside rather than leaving developed models in dust archived literature. Machine (ML) has been increasingly used tackling various tasks owing its capability learn reason without explicit programming [1]. Most ML have had their accuracy proven through internal validation using data. However, external data, continual monitoring prospective randomized controlled trials (RCTs) data are important translation clinical practice [2]. Furthermore, ethics fairness across subpopulations should be considered throughout these evaluations. Different from which evaluates performance a subset original datasets, assesses contexts that may vary subtly or considerably one they were [3]. External serves rectify inflated estimates capabilities overfitting guarantees generalizability transportability diverse populations [4]. For can leverage abundant resources publicly accessible databases such as PhysioNet [5]. Three scenarios recommended after identifying suitable database with sufficient sample size guarantee testing robustness [6]. The first involves directly deploying trained on simulate brand-new scenario previous second entails large training set new fine-tune models, simulating ample collected context [7]. third represents an intermediate situation wherein gradually fed where deployed setting, incrementally collected, updated iteratively newly [8]. existing studies focused direct [9]. Holsbeke et al. [10] previously published diagnostic detecting adnexal mass malignancy multiple medical centers different countries population characteristics. therapeutic pertinent reference is study investigating survival benefits adjuvant therapy breast cancer evaluated originally United Kingdom, settings States [11]. realm tasks, Clift [12] offered comprehensive approach externally validate predicting 10-year risk cancer-related mortality, detailing methods calculation, identification, outcome definition, evaluation. addition assessing model performance, similarity between datasets quantified enable elucidation degradation further identify potential avenues enhancement [13]. Following large-scale subsequent specific setting [14]. Specifically, receive make predictions accordingly, predefined time frame Compared step, distribution drift, control quality, trigger system alarms when deviates normal behavior target [15]. Because operation mainly conducted by professionals, developers focus user-friendly practice. aspect offline hospital allocated computation would limited low latency responding other functions inside system. development secure privacy-aware maintenance method quickly addressing technical collapses while minimizing access patients' private last interface Android app [16] web-based software [17] facilitates use health care professionals comprehends suggestions. It emphasized application designed operate independently from, not interfere with, decision-making processes. This precaution necessary avoid any adverse impact quality. Exemplary seen work Wissel [18]. Those authors prospective, real-time assessment ML-based classifiers epilepsy surgery candidacy Cincinnati Children's Hospital Medical Center. To mitigate risks associated classifiers, patients who deemed appropriate surgical candidates algorithm subjected manual review two expert epileptologists, final decisions confirmed via chart review. A critical insight was effective necessitates synergistic collaboration clinicians, provide essential expertize, information technology contribute research operational knowledge [19, 20]. Assuming tool demonstrates accurate pursue approval RCTs administrative committees. tools classic four-phase RCTs. ensure safety real-life scenarios, absolutely interventions likely avoided. recommend designing compare diagnosis clinicians (intervention group) (control [21-23]. instance, He [24] implemented ML-guided workflows reduced required sonographers cardiologists diagnoses left ventricular ejection fraction. seek ethical institutional board comply standards regulations. Then, proceed Phase I trial assess (whether introduction distracts impairs diagnoses) used. II, few hundred recruited whether statistically significant improvements result clinicians' diagnoses. III, several even thousand effectiveness tool, demonstrating superiority over solutions. If receives agency then investigate wider range IV. Upon efficacy rigorously RCTs, national regulatory agencies US Food Drug Administration (FDA) commercialization [25]. paradigmatic illustration found Titano [26]. three-dimensional convolutional neural networks diagnose acute neurological events head computed tomography images. efficiency subsequently validated randomized, double-blind, trial. suggest referring Nimri [27]. multicenter multinational physicians specialized academic diabetes optimizing insulin pump doses. Mayo Clinic 1-year occurrence asthma exacerbation [28]. detailed guideline conducting could benefit FDA's Policy Device Software Functions Mobile Applications [29], includes provisions applications apply algorithms [30]. Alongside population-level evaluations, there burgeoning awareness about implications revealed diagnose, treat, bill inconsistently [31]. Therefore, it imperative equity patient outcomes, resource allocation [31-33]. Thompson [34] proposed framework biases recalibration modules. module adjusted decision cutoff threshold affected bias, recalibrated outputs, enhancing congruence observed events. Chen [31] systematically summarized path fair medicine, subpopulation collection federated learning, principles, operationalization ecosystems, independent regularization governance disparities. Apart assessments, endorsement thoroughly integrated processes [31, 35]. light these, buried Han Yuan: Conceptualization (lead); curation formal analysis investigation methodology writing—original draft writing—review editing (lead). like acknowledge Prof. Nan Liu Duke-NUS School his invaluable support. author declares no conflict interest. exempt committee because did involve human participants, animal subjects, sensitive collection. Not applicable. Data sharing applicable article generated analyzed during current study.

Язык: Английский

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Code-Free Machine Learning Approach for EVO-ICL Vault Prediction: A Retrospective Two-Center Study

Translational Vision Science & Technology, Год журнала: 2024, Номер 13(4), С. 4 - 4

Опубликована: Апрель 2, 2024

Purpose: Establishing a development environment for machine learning is difficult medical researchers because to code major barrier. This study aimed improve the accuracy of postoperative vault value prediction model implantable collamer lens (ICL) sizing using without coding experience. Methods: We used Orange data mining, recently developed open-source, code-free tool. included eye-pair from 294 patients B&VIIT Eye Center and 26 Kim's Hospital. The was OCULUS Pentacam internally evaluated through 10-fold cross-validation. External validation performed Results: successfully trained collected coding. random forest showed mean absolute errors 124.8 µm 152.4 internal cross-validation external validation, respectively. For high (>750 µm), areas under curve 0.725 0.760 datasets, better than classic statistical regression models Google no-code platform. Conclusions: Applying tool our ICL implantation datasets more accurate models. Translational Relevance: Because significant bias in measurements surgery between clinics, customized nomogram will implantation.

Язык: Английский

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Prediction of diagnosis and diastolic filling pressure by AI-enhanced cardiac MRI: a modelling study of hospital data

The Lancet Digital Health, Год журнала: 2024, Номер 6(6), С. e407 - e417

Опубликована: Май 22, 2024

Язык: Английский

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Data flow in clinical laboratories: could metadata and peridata bridge the gap to new AI-based applications?

Clinical Chemistry and Laboratory Medicine (CCLM), Год журнала: 2024, Номер unknown

Опубликована: Окт. 5, 2024

In the last decades, clinical laboratories have significantly advanced their technological capabilities, through use of interconnected systems and software. Laboratory Information Systems (LIS), introduced in 1970s, transformed into sophisticated information technology (IT) components that integrate with various digital tools, enhancing data retrieval exchange. However, current capabilities LIS are not sufficient to rapidly save extensive data, generated during total testing process (TTP), beyond just test results. This opinion paper discusses qualitative types TTP proposing how divide laboratory-generated two categories, namely metadata peridata. Being both peridata derived from process, it is proposed first useful describe characteristics while second for interpretation Together standardizing preanalytical coding, subdivision or might enhance ML studies, also by facilitating adherence laboratory-derived Findability, Accessibility, Interoperability, Reusability (FAIR) principles. Finally, integrating can improve usability, support utility, advance AI model development healthcare, emphasizing need standardized management practices.

Язык: Английский

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