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The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI) models is likely to usher in newfound capabilities in medicine. We propose a new paradigm for medical AI, which we refer to as generalist medical AI (GMAI). GMAI models will be capable of carrying out a diverse set of tasks using very little or no task-specific labelled data. Built through self-supervision on large, diverse datasets, GMAI will flexibly interpret different combinations of medical modalities, including data from imaging, electronic health records, laboratory results, genomics, graphs or medical text. Models will in turn produce expressive outputs such as free-text explanations, spoken recommendations or image annotations that demonstrate advanced medical reasoning abilities. Here we identify a set of high-impact potential applications for GMAI and lay out specific technical capabilities and training datasets necessary to enable them. We expect that GMAI-enabled applications will challenge current strategies for regulating and validating AI devices for medicine and will shift practices associated with the collection of large medical datasets.
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Inteligência Artificial , Medicina , Diagnóstico por Imagem , Registros Eletrônicos de Saúde , Genômica , Conjuntos de Dados como Assunto , Aprendizado de Máquina não Supervisionado , HumanosRESUMO
MOTIVATION: Sepsis is a leading cause of death and disability in children globally, accounting for â¼3 million childhood deaths per year. In pediatric sepsis patients, the multiple organ dysfunction syndrome (MODS) is considered a significant risk factor for adverse clinical outcomes characterized by high mortality and morbidity in the pediatric intensive care unit. The recent rapidly growing availability of electronic health records (EHRs) has allowed researchers to vastly develop data-driven approaches like machine learning in healthcare and achieved great successes. However, effective machine learning models which could make the accurate early prediction of the recovery in pediatric sepsis patients from MODS to a mild state and thus assist the clinicians in the decision-making process is still lacking. RESULTS: This study develops a machine learning-based approach to predict the recovery from MODS to zero or single organ dysfunction by 1 week in advance in the Swiss Pediatric Sepsis Study cohort of children with blood-culture confirmed bacteremia. Our model achieves internal validation performance on the SPSS cohort with an area under the receiver operating characteristic (AUROC) of 79.1% and area under the precision-recall curve (AUPRC) of 73.6%, and it was also externally validated on another pediatric sepsis patients cohort collected in the USA, yielding an AUROC of 76.4% and AUPRC of 72.4%. These results indicate that our model has the potential to be included into the EHRs system and contribute to patient assessment and triage in pediatric sepsis patient care. AVAILABILITY AND IMPLEMENTATION: Code available at https://github.com/BorgwardtLab/MODS-recovery. The data underlying this article is not publicly available for the privacy of individuals that participated in the study. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Insuficiência de Múltiplos Órgãos , Sepse , Criança , Estudos de Coortes , Humanos , Unidades de Terapia Intensiva Pediátrica , Insuficiência de Múltiplos Órgãos/diagnóstico , Insuficiência de Múltiplos Órgãos/etiologia , Curva ROC , Sepse/complicações , Sepse/diagnósticoRESUMO
MOTIVATION: Temporal biomarker discovery in longitudinal data is based on detecting reoccurring trajectories, the so-called shapelets. The search for shapelets requires considering all subsequences in the data. While the accompanying issue of multiple testing has been mitigated in previous work, the redundancy and overlap of the detected shapelets results in an a priori unbounded number of highly similar and structurally meaningless shapelets. As a consequence, current temporal biomarker discovery methods are impractical and underpowered. RESULTS: We find that the pre- or post-processing of shapelets does not sufficiently increase the power and practical utility. Consequently, we present a novel method for temporal biomarker discovery: Statistically Significant Submodular Subset Shapelet Mining (S5M) that retrieves short subsequences that are (i) occurring in the data, (ii) are statistically significantly associated with the phenotype and (iii) are of manageable quantity while maximizing structural diversity. Structural diversity is achieved by pruning non-representative shapelets via submodular optimization. This increases the statistical power and utility of S5M compared to state-of-the-art approaches on simulated and real-world datasets. For patients admitted to the intensive care unit (ICU) showing signs of severe organ failure, we find temporal patterns in the sequential organ failure assessment score that are associated with in-ICU mortality. AVAILABILITY AND IMPLEMENTATION: S5M is an option in the python package of S3M: github.com/BorgwardtLab/S3M.
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Pesquisa Biomédica , Biomarcadores , Humanos , Fenótipo , Projetos de PesquisaRESUMO
Motivation: Most modern intensive care units record the physiological and vital signs of patients. These data can be used to extract signatures, commonly known as biomarkers, that help physicians understand the biological complexity of many syndromes. However, most biological biomarkers suffer from either poor predictive performance or weak explanatory power. Recent developments in time series classification focus on discovering shapelets, i.e. subsequences that are most predictive in terms of class membership. Shapelets have the advantage of combining a high predictive performance with an interpretable component-their shape. Currently, most shapelet discovery methods do not rely on statistical tests to verify the significance of individual shapelets. Therefore, identifying associations between the shapelets of physiological biomarkers and patients that exhibit certain phenotypes of interest enables the discovery and subsequent ranking of physiological signatures that are interpretable, statistically validated and accurate predictors of clinical endpoints. Results: We present a novel and scalable method for scanning time series and identifying discriminative patterns that are statistically significant. The significance of a shapelet is evaluated while considering the problem of multiple hypothesis testing and mitigating it by efficiently pruning untestable shapelet candidates with Tarone's method. We demonstrate the utility of our method by discovering patterns in three of a patient's vital signs: heart rate, respiratory rate and systolic blood pressure that are indicators of the severity of a future sepsis event, i.e. an inflammatory response to an infective agent that can lead to organ failure and death, if not treated in time. Availability and implementation: We make our method and the scripts that are required to reproduce the experiments publicly available at https://github.com/BorgwardtLab/S3M. Supplementary information: Supplementary data are available at Bioinformatics online.
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Biomarcadores , Mineração de Dados/métodos , Estudos de Associação Genética/métodos , Software , HumanosRESUMO
The digitisation of health care is offering the promise of transforming the management of paediatric sepsis, which is a major source of morbidity and mortality in children worldwide. Digital technology is already making an impact in paediatric sepsis, but is almost exclusively benefiting patients in high-resource health-care settings. However, digital tools can be highly scalable and cost-effective, and-with the right planning-have the potential to reduce global health disparities. Novel digital solutions, from wearable devices and mobile apps, to electronic health record-embedded decision support tools, have an unprecedented opportunity to transform paediatric sepsis research and care. In this Series paper, we describe the current state of digital solutions in paediatric sepsis around the world, the advances in digital technology that are enabling the development of novel applications, and the potential effect of advances in artificial intelligence in paediatric sepsis research and clinical care.
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BACKGROUND: Large language models (LLMs) have recently shown impressive zero-shot capabilities, whereby they can use auxiliary data, without the availability of task-specific training examples, to complete a variety of natural language tasks, such as summarization, dialogue generation, and question answering. However, despite many promising applications of LLMs in clinical medicine, adoption of these models has been limited by their tendency to generate incorrect and sometimes even harmful statements. METHODS: We tasked a panel of eight board-certified clinicians and two health care practitioners with evaluating Almanac, an LLM framework augmented with retrieval capabilities from curated medical resources for medical guideline and treatment recommendations. The panel compared responses from Almanac and standard LLMs (ChatGPT-4, Bing, and Bard) versus a novel data set of 314 clinical questions spanning nine medical specialties. RESULTS: Almanac showed a significant improvement in performance compared with the standard LLMs across axes of factuality, completeness, user preference, and adversarial safety. CONCLUSIONS: Our results show the potential for LLMs with access to domain-specific corpora to be effective in clinical decision-making. The findings also underscore the importance of carefully testing LLMs before deployment to mitigate their shortcomings. (Funded by the National Institutes of Health, National Heart, Lung, and Blood Institute.).
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Importance: Quantifying injury severity is integral to trauma care benchmarking, decision-making, and research, yet the most prevalent metric to quantify injury severity-Injury Severity Score (ISS)- is impractical to use in real time. Objective: To develop and validate a practical model that uses a limited number of injury patterns to quantify injury severity in real time through 3 intuitive outcomes. Design, Setting, and Participants: In this cohort study for prediction model development and validation, training, development, and internal validation cohorts comprised 223â¯545, 74â¯514, and 74â¯514 admission encounters, respectively, of adults (age ≥18 years) with a primary diagnosis of traumatic injury hospitalized more than 2 days (2017-2018 National Inpatient Sample). The external validation cohort comprised 3855 adults admitted to a level I trauma center who met criteria for the 2 highest of the institution's 3 trauma activation levels. Main Outcomes and Measures: Three outcomes were hospital length of stay, probability of discharge disposition to a facility, and probability of inpatient mortality. The prediction performance metric for length of stay was mean absolute error. Prediction performance metrics for discharge disposition and inpatient mortality were average precision, precision, recall, specificity, F1 score, and area under the receiver operating characteristic curve (AUROC). Calibration was evaluated using calibration plots. Shapley addictive explanations analysis and bee swarm plots facilitated model explainability analysis. Results: The Length of Stay, Disposition, Mortality (LDM) Injury Index (the model) comprised a multitask deep learning model trained, developed, and internally validated on a data set of 372â¯573 traumatic injury encounters (mean [SD] age = 68.7 [19.3] years, 56.6% female). The model used 176 potential injuries to output 3 interpretable outcomes: the predicted hospital length of stay, probability of discharge to a facility, and probability of inpatient mortality. For the external validation set, the ISS predicted length of stay with mean absolute error was 4.16 (95% CI, 4.13-4.20) days. Compared with the ISS, the model had comparable external validation set discrimination performance (facility discharge AUROC: 0.67 [95% CI, 0.67-0.68] vs 0.65 [95% CI, 0.65-0.66]; recall: 0.59 [95% CI, 0.58-0.61] vs 0.59 [95% CI, 0.58-0.60]; specificity: 0.66 [95% CI, 0.66-0.66] vs 0.62 [95%CI, 0.60-0.63]; mortality AUROC: 0.83 [95% CI, 0.81-0.84] vs 0.82 [95% CI, 0.82-0.82]; recall: 0.74 [95% CI, 0.72-0.77] vs 0.75 [95% CI, 0.75-0.76]; specificity: 0.81 [95% CI, 0.81-0.81] vs 0.76 [95% CI, 0.75-0.77]). The model had excellent calibration for predicting facility discharge disposition, but overestimated inpatient mortality. Explainability analysis found the inputs influencing model predictions matched intuition. Conclusions and Relevance: In this cohort study using a limited number of injury patterns, the model quantified injury severity using 3 intuitive outcomes. Further study is required to evaluate the model at scale.
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Comportamento Aditivo , Hospitalização , Adulto , Humanos , Animais , Abelhas , Feminino , Adolescente , Idoso , Masculino , Estudos de Coortes , Área Sob a Curva , BenchmarkingRESUMO
Background: When sepsis is detected, organ damage may have progressed to irreversible stages, leading to poor prognosis. The use of machine learning for predicting sepsis early has shown promise, however international validations are missing. Methods: This was a retrospective, observational, multi-centre cohort study. We developed and externally validated a deep learning system for the prediction of sepsis in the intensive care unit (ICU). Our analysis represents the first international, multi-centre in-ICU cohort study for sepsis prediction using deep learning to our knowledge. Our dataset contains 136,478 unique ICU admissions, representing a refined and harmonised subset of four large ICU databases comprising data collected from ICUs in the US, the Netherlands, and Switzerland between 2001 and 2016. Using the international consensus definition Sepsis-3, we derived hourly-resolved sepsis annotations, amounting to 25,694 (18.8%) patient stays with sepsis. We compared our approach to clinical baselines as well as machine learning baselines and performed an extensive internal and external statistical validation within and across databases, reporting area under the receiver-operating-characteristic curve (AUC). Findings: Averaged over sites, our model was able to predict sepsis with an AUC of 0.846 (95% confidence interval [CI], 0.841-0.852) on a held-out validation cohort internal to each site, and an AUC of 0.761 (95% CI, 0.746-0.770) when validating externally across sites. Given access to a small fine-tuning set (10% per site), the transfer to target sites was improved to an AUC of 0.807 (95% CI, 0.801-0.813). Our model raised 1.4 false alerts per true alert and detected 80% of the septic patients 3.7 h (95% CI, 3.0-4.3) prior to the onset of sepsis, opening a vital window for intervention. Interpretation: By monitoring clinical and laboratory measurements in a retrospective simulation of a real-time prediction scenario, a deep learning system for the detection of sepsis generalised to previously unseen ICU cohorts, internationally. Funding: This study was funded by the Personalized Health and Related Technologies (PHRT) strategic focus area of the ETH domain.
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Large-language models have recently demonstrated impressive zero-shot capabilities in a variety of natural language tasks such as summarization, dialogue generation, and question-answering. Despite many promising applications in clinical medicine, adoption of these models in real-world settings has been largely limited by their tendency to generate incorrect and sometimes even toxic statements. In this study, we develop Almanac, a large language model framework augmented with retrieval capabilities for medical guideline and treatment recommendations. Performance on a novel dataset of clinical scenarios (n= 130) evaluated by a panel of 5 board-certified and resident physicians demonstrates significant increases in factuality (mean of 18% at p-value < 0.05) across all specialties, with improvements in completeness and safety. Our results demonstrate the potential for large language models to be effective tools in the clinical decision-making process, while also emphasizing the importance of careful testing and deployment to mitigate their shortcomings.
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The last decade saw an enormous boost in the field of computational topology: methods and concepts from algebraic and differential topology, formerly confined to the realm of pure mathematics, have demonstrated their utility in numerous areas such as computational biology personalised medicine, and time-dependent data analysis, to name a few. The newly-emerging domain comprising topology-based techniques is often referred to as topological data analysis (TDA). Next to their applications in the aforementioned areas, TDA methods have also proven to be effective in supporting, enhancing, and augmenting both classical machine learning and deep learning models. In this paper, we review the state of the art of a nascent field we refer to as "topological machine learning," i.e., the successful symbiosis of topology-based methods and machine learning algorithms, such as deep neural networks. We identify common threads, current applications, and future challenges.
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Background: Sepsis is among the leading causes of death in intensive care units (ICUs) worldwide and its recognition, particularly in the early stages of the disease, remains a medical challenge. The advent of an affluence of available digital health data has created a setting in which machine learning can be used for digital biomarker discovery, with the ultimate goal to advance the early recognition of sepsis. Objective: To systematically review and evaluate studies employing machine learning for the prediction of sepsis in the ICU. Data Sources: Using Embase, Google Scholar, PubMed/Medline, Scopus, and Web of Science, we systematically searched the existing literature for machine learning-driven sepsis onset prediction for patients in the ICU. Study Eligibility Criteria: All peer-reviewed articles using machine learning for the prediction of sepsis onset in adult ICU patients were included. Studies focusing on patient populations outside the ICU were excluded. Study Appraisal and Synthesis Methods: A systematic review was performed according to the PRISMA guidelines. Moreover, a quality assessment of all eligible studies was performed. Results: Out of 974 identified articles, 22 and 21 met the criteria to be included in the systematic review and quality assessment, respectively. A multitude of machine learning algorithms were applied to refine the early prediction of sepsis. The quality of the studies ranged from "poor" (satisfying ≤ 40% of the quality criteria) to "very good" (satisfying ≥ 90% of the quality criteria). The majority of the studies (n = 19, 86.4%) employed an offline training scenario combined with a horizon evaluation, while two studies implemented an online scenario (n = 2, 9.1%). The massive inter-study heterogeneity in terms of model development, sepsis definition, prediction time windows, and outcomes precluded a meta-analysis. Last, only two studies provided publicly accessible source code and data sources fostering reproducibility. Limitations: Articles were only eligible for inclusion when employing machine learning algorithms for the prediction of sepsis onset in the ICU. This restriction led to the exclusion of studies focusing on the prediction of septic shock, sepsis-related mortality, and patient populations outside the ICU. Conclusions and Key Findings: A growing number of studies employs machine learning to optimize the early prediction of sepsis through digital biomarker discovery. This review, however, highlights several shortcomings of the current approaches, including low comparability and reproducibility. Finally, we gather recommendations how these challenges can be addressed before deploying these models in prospective analyses. Systematic Review Registration Number: CRD42020200133.
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With the biomedical field generating large quantities of time series data, there has been a growing interest in developing and refining machine learning methods that allow its mining and exploitation. Classification is one of the most important and challenging machine learning tasks related to time series. Many biomedical phenomena, such as the brain's activity or blood pressure, change over time. The objective of this chapter is to provide a gentle introduction to time series classification. In the first part we describe the characteristics of time series data and challenges in its analysis. The second part provides an overview of common machine learning methods used for time series classification. A real-world use case, the early recognition of sepsis, demonstrates the applicability of the methods discussed.
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Pesquisa Biomédica/métodos , Aprendizado Profundo , Aprendizado de Máquina , Mineração de Dados/métodos , HumanosRESUMO
Intensive-care clinicians are presented with large quantities of measurements from multiple monitoring systems. The limited ability of humans to process complex information hinders early recognition of patient deterioration, and high numbers of monitoring alarms lead to alarm fatigue. We used machine learning to develop an early-warning system that integrates measurements from multiple organ systems using a high-resolution database with 240 patient-years of data. It predicts 90% of circulatory-failure events in the test set, with 82% identified more than 2 h in advance, resulting in an area under the receiver operating characteristic curve of 0.94 and an area under the precision-recall curve of 0.63. On average, the system raises 0.05 alarms per patient and hour. The model was externally validated in an independent patient cohort. Our model provides early identification of patients at risk for circulatory failure with a much lower false-alarm rate than conventional threshold-based systems.
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Unidades de Terapia Intensiva , Aprendizado de Máquina , Choque/diagnóstico , Estudos de Coortes , Bases de Dados como Assunto , Humanos , Modelos Teóricos , Prognóstico , Curva ROC , Reprodutibilidade dos Testes , Fatores de Risco , Fatores de TempoRESUMO
We report a case of distal migration of a stent from the brachiocephalic artery to the distal right common carotid artery 7 months after implantation in a 5-year-old child with Williams syndrome. There were no neurological sequelae and the migrated stent remained widely patent 5 years following implantation.
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Arteriopatias Oclusivas/terapia , Tronco Braquiocefálico/diagnóstico por imagem , Artéria Carótida Primitiva/diagnóstico por imagem , Falha de Prótese , Stents/efeitos adversos , Síndrome de Williams/complicações , Aortografia , Estenose das Carótidas/terapia , Pré-Escolar , Humanos , MasculinoRESUMO
BACKGROUND: Morbid obesity is a worldwide epidemic and is increasingly treated by bariatric surgery. Fatty liver is a common finding; almost half of all patients with non-alcoholic steatohepatitis develop steatohepatitis. Bariatric surgery improves steatohepatitis documented by liver biopsy and single voxel magnetic resonance imaging (MRI) techniques. OBJECTIVE: To investigate changes before and after bariatric surgery using whole organ MRI quantification of liver, visceral, and subcutaneous fat. SETTING: University of Basel Hospital and St. Clara Research Ltd, Basel, Switzerland. METHODS: Sixteen morbidly obese patients were evaluated by abdominal MRI-scanning before and 3, 6, 12, and 24 months after bariatric surgery to measure percentage liver fat (%-LF), total liver volume (TLV) and visceral and subcutaneous adipose tissues (VAT and SAT). Fasting plasma samples were taken for measurement of glucose, insulin, blood lipids, and liver biomarkers. In a control group of 12 healthy lean volunteers, the liver biomarker was also measured. RESULTS: The reproducibility of fat quantification by use of MRI was excellent. LF decreased significantly faster than VAT and SAT (%-LF vs. VAT p < 0.001 and %-LF vs. SAT p < 0.001). At certain time points, %-LF, VAT, and SAT were associated with changes in blood lipids and insulin. CONCLUSIONS: MRI quantification offers excellently reproducible results in measurement of liver fat and visceral and subcutaneous adipose tissues. Liver fat decreased significantly faster than visceral or subcutaneous adipose tissue. Decrease in %-LF and VAT is associated with decrease in total cholesterol, LDL, and plasma insulin.
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Cirurgia Bariátrica , Gordura Intra-Abdominal/diagnóstico por imagem , Fígado/diagnóstico por imagem , Imageamento por Ressonância Magnética/métodos , Gordura Subcutânea/diagnóstico por imagem , Estudos de Coortes , Fígado Gorduroso/diagnóstico por imagem , Humanos , Obesidade Mórbida/cirurgia , Período PerioperatórioAssuntos
Endocardite Bacteriana/terapia , Febre/terapia , Imunoglobulinas Intravenosas/administração & dosagem , Fatores Imunológicos/administração & dosagem , Infecções Estreptocócicas/complicações , Estreptococos Viridans/isolamento & purificação , Criança , Endocardite Bacteriana/etiologia , Endocardite Bacteriana/microbiologia , Comunicação Interventricular/complicações , Humanos , MasculinoAssuntos
Estenose Coronária/diagnóstico por imagem , Estenose Coronária/etiologia , Vasos Coronários/diagnóstico por imagem , Síndrome de Williams/complicações , Pré-Escolar , Deleção Cromossômica , Cromossomos Humanos Par 7/genética , Elastina/genética , Humanos , Masculino , Ultrassonografia , Síndrome de Williams/genéticaRESUMO
BACKGROUND: Percutaneous transcatheter closure of patent foramen ovale (PFO) is used as an alternative to surgery or long-term anticoagulation for the treatment of patients with paradoxical embolism and PFO. METHODS AND RESULTS: We report the immediate and long-term clinical and echocardiographic outcome of 110 consecutive patients (58 males, mean age 47+/-14 years) who underwent transcatheter closure of PFO because of paradoxical embolism between 1995 and 2001. Procedural success, defined as successful deployment of the device and effective occlusion (no, or trivial, shunt after device placement), was achieved in all (100%) patients. There was no in-hospital mortality, 1 device migration requiring surgical intervention (0.9%), and 1 episode of cardiac tamponade (0.9%) requiring pericardiocentesis. A progressive increment in full occlusion was observed (44%, 51%, 66%, and 71% at 1 day, 6 months, and 1 and 2 years, respectively, after device placement). At a mean follow-up of 2.3 years, 2 patients experienced recurrent neurological events (1 fatal stroke and 1 transient ischemic attack), representing an annual risk of recurrence of 0.9%. In addition, 4 (3.6%) of the patients required reintervention for device malalignment or significant shunt. Kaplan-Meier analysis showed a freedom from recurrent embolic events and reintervention of 96% and 90% at 1 and 5 years, respectively. CONCLUSIONS: Transcatheter closure of PFO is a safe and effective therapy for patients with paradoxical embolism and PFO. It is associated with a high success rate, low incidence of hospital complications, and low frequency of recurrent systemic embolic events.