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

Health care science, Journal Year: 2024, Volume and Issue: 3(5), P. 360 - 364

Published: Oct. 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.

Language: Английский

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Photoreceptor metabolic window unveils eye–body interactions

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: Jan. 15, 2025

Language: Английский

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Wind Power Forecasting with Machine Learning Algorithms in Low-Cost Devices

Electronics, Journal Year: 2024, Volume and Issue: 13(8), P. 1541 - 1541

Published: April 18, 2024

The urgent imperative to mitigate carbon dioxide (CO2) emissions from power generation poses a pressing challenge for contemporary society. In response, there is critical need intensify efforts improve the efficiency of clean energy sources and expand their use, including wind energy. Within this field, it necessary address variability inherent resource with application prediction methodologies that allow production be managed. At same time, extend its should made accessible everyone, on small scale, boosting devices are affordable individuals, such as Raspberry other low-cost hardware platforms. This study designed evaluate effectiveness various machine learning (ML) algorithms, special emphasis deep models, in accurately forecasting output turbines. Specifically, research deals convolutional neural networks (CNN), fully connected (FC), gated recurrent unit cells (GRU), transformer-based models. However, main objective work analyze feasibility deploying these architectures computing platforms, comparing performance both conventional systems lower-cost alternatives, Pi 3, order make them more management generation. Through training rigorous benchmarking process, considering accuracy, real-time performance, consumption, identifies optimal technique model series data related production, evaluates implementation studied Importantly, our findings demonstrate effective can achieved highlighting potential widespread adoption personal generation, thus representing fundamental step towards democratization technologies.

Language: Английский

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A multi-center study on the adaptability of a shared foundation model for electronic health records

npj Digital Medicine, Journal Year: 2024, Volume and Issue: 7(1)

Published: June 27, 2024

Foundation models are transforming artificial intelligence (AI) in healthcare by providing modular components adaptable for various downstream tasks, making AI development more scalable and cost-effective. structured electronic health records (EHR), trained on coded medical from millions of patients, demonstrated benefits including increased performance with fewer training labels, improved robustness to distribution shifts. However, questions remain the feasibility sharing these across hospitals their local tasks. This multi-center study examined adaptability a publicly accessible EHR foundation model (FM

Language: Английский

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DOME: Directional medical embedding vectors from electronic health records

Journal of Biomedical Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 104768 - 104768

Published: Jan. 1, 2025

Language: Английский

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Multi-Modal Fusion of Routine Care Electronic Health Records (EHR): A Scoping Review

Information, Journal Year: 2025, Volume and Issue: 16(1), P. 54 - 54

Published: Jan. 15, 2025

Background: Electronic health records (EHR) are now widely available in healthcare institutions to document the medical history of patients as they interact with services. In particular, routine care EHR data collected for a large number patients.These span multiple heterogeneous elements (i.e., demographics, diagnosis, medications, clinical notes, vital signs, and laboratory results) which contain semantic, concept, temporal information. Recent advances generative learning techniques were able leverage fusion enhance decision support. Objective: A scoping review proposed including architectures, input elements, application areas is needed synthesize variances identify research gaps that can promote re-use these new outcomes. Design: comprehensive literature search was conducted using Google Scholar high impact architectures over multi-modal during period 2018 2023. The guidelines from PRISMA (Preferred Reporting Items Systematic Reviews Meta-Analyses) extension followed. findings derived selected studies thematic comparative analysis. Results: revealed lack standard definition transformed into modalities. These definitions ignore one or more key characteristics source, encoding scheme, concept level. Moreover, order adapt emergent techniques, classification should distinguish take consideration concurrently happen all three layers encoding, representation, decision). aspects constitute first step towards streamlined approach design data. addition, current pretrained models inconsistent their handling semantic information thereby hindering different applications settings. Conclusions: Current mostly follow design-by-example methodology. Guidelines efficient broad range applications. addition promoting re-use, need outline best practices combining modalities while leveraging transfer co-learning well encoding.

Language: Английский

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A deep attention-based encoder for the prediction of type 2 diabetes longitudinal outcomes from routinely collected health care data

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126876 - 126876

Published: Feb. 1, 2025

Language: Английский

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BSAformer: bidirectional sequence splitting aggregation attention mechanism for long term series forecasting

Complex & Intelligent Systems, Journal Year: 2025, Volume and Issue: 11(4)

Published: Feb. 28, 2025

Language: Английский

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Integrin αvβ3-Targeted Therapeutic Strategies in Pancreatic Cancers

IntechOpen eBooks, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 25, 2025

Pancreatic cancer is a significant health concern, primarily due to challenges in early diagnosis and limited treatment options. The increasing incidence of pancreatic cancers the lack effective chemotherapy underscore need for detection efficient therapy. cell surface integrin αvβ3 overexpresses most newly growing endothelial cells crucial growth metastasis. Novel nanotechnologies have been developed target its functions detective therapeutic purposes. This chapter details importance target, αvβ3, cancer’s development, proliferation, Theranostics, new strategy combined with diagnostics therapeutics, can help monitoring response. These cutting-edge technologies enable simultaneous through imaging targeted delivery therapeutics cells. Nanocarriers, such as liposomes PLGA, be used theranostics provide comprehensive approach potentially revolutionizing cancer. potential nano-drugs, either standalone treatments or theranostics, will explored. Combined currently available anticancer drugs, target-specific nano-delivery system personalized approach, where drug’s dosage duration adjusted based on patient’s elucidation targeting anti-vascular effects medicine introduce strategic therapy cancers.

Language: Английский

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Leveraging patients’ longitudinal data to improve the Hospital One-year Mortality Risk

Health Information Science and Systems, Journal Year: 2025, Volume and Issue: 13(1)

Published: March 4, 2025

Language: Английский

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