Future of machine learning in paediatrics

2021 ◽  
pp. archdischild-2020-321023
Author(s):  
Sarah LN Clarke ◽  
Kevon Parmesar ◽  
Moin A Saleem ◽  
Athimalaipet V Ramanan
Keyword(s):  
Machine Learning ◽  
Clinical Decision Making ◽  
Healthcare Delivery ◽  
Clinical Decision ◽  
Spam Filtering ◽  
Paediatric Care ◽  
Patient Complexity ◽  
Potential Impact

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn without being explicitly programmed, through a combination of statistics and computer science. It encompasses a variety of techniques used to analyse and interpret extremely large amounts of data, which can then be applied to create predictive models. Such applications of this technology are now ubiquitous in our day-to-day lives: predictive text, spam filtering, and recommendation systems in social media, streaming video and e-commerce to name a few examples. It is only more recently that ML has started to be implemented against the vast amount of data generated in healthcare. The emerging role of AI in refining healthcare delivery was recently highlighted in the ‘National Health Service Long Term Plan 2019’. In paediatrics, workforce challenges, rising healthcare attendance and increased patient complexity and comorbidity mean that demands on paediatric services are also growing. As healthcare moves into this digital age, this review considers the potential impact ML can have across all aspects of paediatric care from improving workforce efficiency and aiding clinical decision-making to precision medicine and drug development.

scholarly journals Current Role of Surgery in Pancreatic Cancer With Synchronous Liver Metastasis

2020 ◽  
Vol 27 (1) ◽  
pp. 107327482097659
Author(s):  
Wentao Zhou ◽  
Dansong Wang ◽  
Wenhui Lou
Keyword(s):  
Pancreatic Cancer ◽  
Liver Metastasis ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Synchronous Liver Metastasis ◽  
Long Term Outcomes ◽  
Short And Long Term ◽  
Current Guidelines

Pancreatic cancer with synchronous liver metastasis has an extremely poor prognosis, and surgery is not recommended for such patients by the current guidelines. However, an increasing body of studies have shown that concurrent resection of pancreatic cancer and liver metastasis is not only technically feasible but also beneficial to the survival in the selected patients. In this review, we aim to summarize the short- and long-term outcomes following synchronous liver metastasectomy for pancreatic cancer patients, and discuss the potential criteria in selecting appropriate surgical candidates, which might be helpful in clinical decision-making.

Machine learning in clinical decision making

Med ◽  
2021 ◽  
Author(s):  
Lorenz Adlung ◽  
Yotam Cohen ◽  
Uria Mor ◽  
Eran Elinav
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision

scholarly journals Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer

2021 ◽  
Author(s):  
E. Amiri Souri ◽  
A. Chenoweth ◽  
A. Cheung ◽  
S. N. Karagiannis ◽  
S. Tsoka
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Clinical Decision Making ◽  
Histological Grade ◽  
Tumour Size ◽  
Clinical Decision ◽  
Gene Signature ◽  
Risk Classification ◽  
Breast Cancers ◽  
Grade 3

Abstract Background Prognostic stratification of breast cancers remains a challenge to improve clinical decision making. We employ machine learning on breast cancer transcriptomics from multiple studies to link the expression of specific genes to histological grade and classify tumours into a more or less aggressive prognostic type. Materials and methods Microarray data of 5031 untreated breast tumours spanning 33 published datasets and corresponding clinical data were integrated. A machine learning model based on gradient boosted trees was trained on histological grade-1 and grade-3 samples. The resulting predictive model (Cancer Grade Model, CGM) was applied on samples of grade-2 and unknown-grade (3029) for prognostic risk classification. Results A 70-gene signature for assessing clinical risk was identified and was shown to be 90% accurate when tested on known histological-grade samples. The predictive framework was validated through survival analysis and showed robust prognostic performance. CGM was cross-referenced with existing genomic tests and demonstrated the competitive predictive power of tumour risk. Conclusions CGM is able to classify tumours into better-defined prognostic categories without employing information on tumour size, stage, or subgroups. The model offers means to improve prognosis and support the clinical decision and precision treatments, thereby potentially contributing to preventing underdiagnosis of high-risk tumours and minimising over-treatment of low-risk disease.

scholarly journals Clinician checklist for assessing suitability of machine learning applications in healthcare

2021 ◽  
Vol 28 (1) ◽  
pp. e100251
Author(s):  
Ian Scott ◽  
Stacey Carter ◽  
Enrico Coiera
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Routine Care ◽  
Machine Learning Algorithms ◽  
Clinical Settings ◽  
Machine Learning Applications ◽  
Key Issues

Machine learning algorithms are being used to screen and diagnose disease, prognosticate and predict therapeutic responses. Hundreds of new algorithms are being developed, but whether they improve clinical decision making and patient outcomes remains uncertain. If clinicians are to use algorithms, they need to be reassured that key issues relating to their validity, utility, feasibility, safety and ethical use have been addressed. We propose a checklist of 10 questions that clinicians can ask of those advocating for the use of a particular algorithm, but which do not expect clinicians, as non-experts, to demonstrate mastery over what can be highly complex statistical and computational concepts. The questions are: (1) What is the purpose and context of the algorithm? (2) How good were the data used to train the algorithm? (3) Were there sufficient data to train the algorithm? (4) How well does the algorithm perform? (5) Is the algorithm transferable to new clinical settings? (6) Are the outputs of the algorithm clinically intelligible? (7) How will this algorithm fit into and complement current workflows? (8) Has use of the algorithm been shown to improve patient care and outcomes? (9) Could the algorithm cause patient harm? and (10) Does use of the algorithm raise ethical, legal or social concerns? We provide examples where an algorithm may raise concerns and apply the checklist to a recent review of diagnostic imaging applications. This checklist aims to assist clinicians in assessing algorithm readiness for routine care and identify situations where further refinement and evaluation is required prior to large-scale use.

scholarly journals In-depth blood proteome profiling analysis revealed distinct functional characteristics of plasma proteins between severe and non-severe COVID-19 patients

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Joonho Park ◽  
Hyeyoon Kim ◽  
So Yeon Kim ◽  
Yeonjae Kim ◽  
Jee-Soo Lee ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Clinical Decision ◽  
Good Prognosis ◽  
Severe Illness ◽  
Proteome Profiling ◽  
T Cell Suppression ◽  
Cell Suppression ◽  
Profiling Analysis ◽  
Necrosis Factor

AbstractThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected over forty million patients worldwide. Although most coronavirus disease 2019 (COVID-19) patients have a good prognosis, some develop severe illness. Markers that define disease severity or predict clinical outcome need to be urgently developed as the mortality rate in critical cases is approximately 61.5%. In the present study, we performed in-depth proteome profiling of undepleted plasma from eight COVID-19 patients. Quantitative proteomic analysis using the BoxCar method revealed that 91 out of 1222 quantified proteins were differentially expressed depending on the severity of COVID-19. Importantly, we found 76 proteins, previously not reported, which could be novel prognostic biomarker candidates. Our plasma proteome signatures captured the host response to SARS-CoV-2 infection, thereby highlighting the role of neutrophil activation, complement activation, platelet function, and T cell suppression as well as proinflammatory factors upstream and downstream of interleukin-6, interleukin-1B, and tumor necrosis factor. Consequently, this study supports the development of blood biomarkers and potential therapeutic targets to aid clinical decision-making and subsequently improve prognosis of COVID-19.

scholarly journals Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Imogen Schofield ◽  
David C. Brodbelt ◽  
Noel Kennedy ◽  
Stijn J. M. Niessen ◽  
David B. Church ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cushing’S Syndrome ◽  
Clinical Decision Making ◽  
Predictive Performance ◽  
Clinical Decision ◽  
Cushing's Syndrome ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Clinical Records

AbstractCushing’s syndrome is an endocrine disease in dogs that negatively impacts upon the quality-of-life of affected animals. Cushing’s syndrome can be a challenging diagnosis to confirm, therefore new methods to aid diagnosis are warranted. Four machine-learning algorithms were applied to predict a future diagnosis of Cushing's syndrome, using structured clinical data from the VetCompass programme in the UK. Dogs suspected of having Cushing's syndrome were included in the analysis and classified based on their final reported diagnosis within their clinical records. Demographic and clinical features available at the point of first suspicion by the attending veterinarian were included within the models. The machine-learning methods were able to classify the recorded Cushing’s syndrome diagnoses, with good predictive performance. The LASSO penalised regression model indicated the best overall performance when applied to the test set with an AUROC = 0.85 (95% CI 0.80–0.89), sensitivity = 0.71, specificity = 0.82, PPV = 0.75 and NPV = 0.78. The findings of our study indicate that machine-learning methods could predict the future diagnosis of a practicing veterinarian. New approaches using these methods could support clinical decision-making and contribute to improved diagnosis of Cushing’s syndrome in dogs.

scholarly journals A Rational Evaluation of the Syncope Patient: Optimizing the Emergency Department Visit

Medicina ◽  
2021 ◽  
Vol 57 (6) ◽  
pp. 514
Author(s):  
Tarek Hatoum ◽  
Robert S. Sheldon
Keyword(s):  
Emergency Department ◽  
Diagnostic Yield ◽  
Clinical Decision ◽  
Emergency Department Visits ◽  
Decision Tools ◽  
Short And Long Term ◽  
Electrocardiogram Ecg ◽  
Structured Approach

Syncope accounts for up to 2% of emergency department visits and results in the hospitalization of 12–86% of patients. There is often a low diagnostic yield, with up to 50% of hospitalized patients being discharged with no clear diagnosis. We will outline a structured approach to the syncope patient in the emergency department, highlighting the evidence supporting the role of clinical judgement and the initial electrocardiogram (ECG) in making the preliminary diagnosis and in safely identifying the patients at low risk of short- and long-term adverse events or admitting the patient if likely to benefit from urgent intervention. Clinical decision tools and additional testing may aid in further stratifying patients and may guide disposition. While hospital admission does not seem to offer additional mortality benefit, the efficient utilization of outpatient testing may provide similar diagnostic yield, preventing unnecessary hospitalizations.

scholarly journals Clinical decision making and research in hepatocellular carcinoma: Pivotal role of imaging techniques

Hepatology ◽  
2011 ◽  
Vol 54 (6) ◽  
pp. 2238-2244 ◽  
Author(s):  
Jordi Bruix ◽  
Maria Reig ◽  
Jordi Rimola ◽  
Alejandro Forner ◽  
Marta Burrel ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Imaging Techniques ◽  
Clinical Decision ◽  
Pivotal Role

Practice Guidelines for the Diagnosis of COVID-19-Associated Pulmonary Aspergillosis in an Intensive Care Setting

2021 ◽  
pp. 088506662110471
Author(s):  
Zia Hashim ◽  
Zafar Neyaz ◽  
Rungmei S.K. Marak ◽  
Alok Nath ◽  
Soniya Nityanand ◽  
...  
Keyword(s):  
Intensive Care ◽  
Practice Guidelines ◽  
Clinical Management ◽  
Care Setting ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Third Wave ◽  
Intensive Care Setting ◽  
Pulmonary Aspergillosis

Coronavirus disease-2019 (COVID-19)-associated pulmonary aspergillosis (CAPA) is a new disease characterized by secondary Aspergillus mold infection in patients with COVID-19. It primarily affects patients with COVID-19 in critical state with acute respiratory distress syndrome, requiring intensive care and mechanical ventilation. CAPA has a higher mortality rate than COVID-19, posing a serious threat to affected individuals. COVID-19 is a potential risk factor for CAPA and has already claimed a massive death toll worldwide since its outbreak in December 2019. Its second wave is currently progressing towards a peak, while the third wave of this devastating pandemic is expected to follow. Therefore, an early and accurate diagnosis of CAPA is of utmost importance for effective clinical management of this highly fatal disease. However, there are no uniform criteria for diagnosing CAPA in an intensive care setting. Therefore, based on a review of existing information and our own experience, we have proposed new criteria in the form of practice guidelines for diagnosing CAPA, focusing on the points relevant for intensivists and pulmonary and critical care physicians. The main highlights of these guidelines include the role of CAPA-appropriate test specimens, clinical risk factors, computed tomography of the thorax, and non-culture-based indirect and direct mycological evidence for diagnosing CAPA in the intensive care unit. These guidelines classify the diagnosis of CAPA into suspected, possible, and probable categories to facilitate clinical decision-making. We hope that these practice guidelines will adequately address the diagnostic challenges of CAPA, providing an easy-to-use and practical algorithm to clinicians for rapid diagnosis and clinical management of the disease.

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