The JAK-inhibitor ruxolitinib impairs dendritic cell function in vitro and in vivo

This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately, 22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among the methods considered, support vector machines (SVM), logistic regression (LR), clustering were the most commonly used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future.

https://www.mdpi.com/2075-4426/10/2/21/pdf

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

Talaromyces (Penicillium) marneffei is an important pathogenic thermally dimorphic fungus causing systemic mycosis in Southeast Asia. The clinical significance of T. marneffei became evident when the human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome epidemic arrived in Southeast Asia in 1988. Subsequently, a decline in the incidence of T. marneffei infection among HIV-infected patients was seen in regions with access to highly active antiretroviral therapy and other control measures for HIV. Since the 1990s, an increasing number of T. marneffei infections have been reported among non-HIV-infected patients with impaired cell-mediated immunity. Their comorbidities included primary adult-onset immunodeficiency due to anti-interferon-gamma autoantibodies and secondary immunosuppressive conditions including other autoimmune diseases, solid organ and hematopoietic stem cell transplantations, T-lymphocyte-depleting immunsuppressive drugs and novel anti-cancer targeted therapies such as anti-CD20 monoclonal antibodies and kinase inhibitors. Moreover, improved immunological diagnostics identified more primary immunodeficiency syndromes associated with T. marneffei infection in children. The higher case-fatality rate of T. marneffei infection in non-HIV-infected than HIV-infected patients might be related to delayed diagnosis due to the lack of clinical suspicion. Correction of the underlying immune defects and early use of antifungals are important treatment strategies. Clinicians should be familiar with the changing epidemiology and clinical management of T. marneffei infection among non-HIV-infected patients.

Talaromyces (Penicillium) marneffei infection in non-HIV-infected patients.

Immunocompromised patients are at high risk of invasive fungal infections (IFI), in particular those with haematological malignancies undergoing remission-induction chemotherapy for acute myeloid leukaemia (AML) or myelodysplastic syndrome (MDS) and recipients of allogeneic haematopoietic stem cell transplants (HSCT). Despite the development of new treatment options in the past decades, IFI remains a concern due to substantial morbidity and mortality in these patient populations. In addition, the increasing use of new immune modulating drugs in cancer therapy has opened an entirely new spectrum of at risk periods. Since the last edition of antifungal prophylaxis recommendations of the German Society for Haematology and Medical Oncology in 2014, seven clinical trials regarding antifungal prophylaxis in patients with haematological malignancies have been published, comprising 1227 patients. This update assesses the impact of this additional evidence and effective revisions. Our key recommendations are the following: prophylaxis should be performed with posaconazole delayed release tablets during remission induction chemotherapy for AML and MDS (AI). Posaconazole iv can be used when the oral route is contraindicated or not feasible. Intravenous liposomal amphotericin B did not significantly decrease IFI rates in acute lymphoblastic leukaemia (ALL) patients during induction chemotherapy, and there is poor evidence to recommend it for prophylaxis in these patients (CI). Despite substantial risk of IFI, we cannot provide a stronger recommendation for these patients. There is poor evidence regarding voriconazole prophylaxis in patients with neutropenia (CII). Therapeutic drug monitoring TDM should be performed within 2 to 5 days of initiating voriconazole prophylaxis and should be repeated in case of suspicious adverse events or of dose changes of interacting drugs (BIItu). General TDM during posaconazole prophylaxis is not recommended (CIItu), but may be helpful in cases of clinical failure such as breakthrough IFI for verification of compliance or absorption.

/pdf/primary-prophylaxis-of-invasive-fungal-infections-in-2w0w558s2a.pdf

Primary prophylaxis of invasive fungal infections in patients with haematological malignancies: 2017 update of the recommendations of the Infectious Diseases Working Party (AGIHO) of the German Society for Haematology and Medical Oncology (DGHO).

We developed an explainable artificial intelligence (AI) early warning score (xAI-EWS) system for early detection of acute critical illness. While maintaining a high predictive performance, our system explains to the clinician on which relevant electronic health records (EHRs) data the prediction is grounded. Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Early clinical prediction is typically based on manually calculated screening metrics that simply weigh these parameters, such as Early Warning Scores (EWS). The predictive performance of EWSs yields a tradeoff between sensitivity and specificity that can lead to negative outcomes for the patient. Previous work on EHR-trained AI systems offers promising results with high levels of predictive performance in relation to the early, real-time prediction of acute critical illness. However, without insight into the complex decisions by such system, clinical translation is hindered. In this letter, we present our xAI-EWS system, which potentiates clinical translation by accompanying a prediction with information on the EHR data explaining it.

Explainable artificial intelligence model to predict acute critical illness from electronic health records

An Opportunistic Infection Associated With Ruxolitinib, a Novel Janus Kinase 1,2 Inhibitor

COPD patients are burdened with a daily risk of acute exacerbation and loss of control, which could be mitigated by effective, on-demand decision support tools. In this study, we present a machine learning-based strategy for early detection of exacerbations and subsequent triage. Our application uses physician opinion in a statistically and clinically comprehensive set of patient cases to train a supervised prediction algorithm. The accuracy of the model is assessed against a panel of physicians each triaging identical cases in a representative patient validation set. Our results show that algorithm accuracy and safety indicators surpass all individual pulmonologists in both identifying exacerbations and predicting the consensus triage in a 101 case validation set. The algorithm is also the top performer in sensitivity, specificity, and ppv when predicting a patient's need for emergency care.

/pdf/a-machine-learning-approach-to-triaging-patients-with-2at4peygjp.pdf

A machine learning approach to triaging patients with chronic obstructive pulmonary disease.

Inadequate at-home management and self-awareness of heart failure (HF) exacerbations are known to be leading causes of the greater than 1 million estimated HF-related hospitalizations in the USA alone. Most current at-home HF management protocols include paper guidelines or exploratory health applications that lack rigor and validation at the level of the individual patient. We report on a novel triage methodology that uses machine learning predictions for real-time detection and assessment of exacerbations. Medical specialist opinions on statistically and clinically comprehensive, simulated patient cases were used to train and validate prediction algorithms. Model performance was assessed by comparison to physician panel consensus in a representative, out-of-sample validation set of 100 vignettes. Algorithm prediction accuracy and safety indicators surpassed all individual specialists in identifying consensus opinion on existence/severity of exacerbations and appropriate treatment response. The algorithms also scored the highest sensitivity, specificity, and PPV when assessing the need for emergency care. Here we develop a machine-learning approach for providing real-time decision support to adults diagnosed with congestive heart failure. The algorithm achieves higher exacerbation and triage classification performance than any individual physician when compared to physician consensus opinion.

/pdf/a-machine-learning-methodology-for-identification-and-triage-111tht8s9b.pdf

A Machine Learning Methodology for Identification and Triage of Heart Failure Exacerbations

Cryptococcal Pneumonia In A Patient Treated With Ruxolitinib, A Novel Janus Kinase Inhibitor

A Patient Feedback Driven, Stacked Machine-Learning Approach to At-Home COPD Triage

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