Recommendations for the assessment of non‐extremity venous thromboembolism outcomes: communication from the SSC of the ISTH

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110812/1/jth12809.pd

Management of intracranial surgery for refractory epilepsy in severe factor VII deficiency: choosing the optimal dosing regimen

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106864/1/hae12397.pd

Improving dermatology classifiers across populations using images generated by large diffusion models

Dermatological classification algorithms developed without sufficiently diverse training data may generalize poorly across populations. While intentional data collection and annotation offer the best means for improving representation, new computational approaches for generating training data may also aid in mitigating the effects of sampling bias. In this paper, we show that DALL$\cdot$E 2, a large-scale text-to-image diffusion model, can produce photorealistic images of skin disease across skin types. Using the Fitzpatrick 17k dataset as a benchmark, we demonstrate that augmenting training data with DALL$\cdot$E 2-generated synthetic images improves classification of skin disease overall and especially for underrepresented groups.Comment: NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Researc

Influence Of Nanopolymers With Different End‐Functionalities On Platelets And Coagulation. An Ex‐Vivo Study

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106058/1/jth03252.pd

Recommendations for future research in relation to pediatric pulmonary embolism: communication from the SSC of the ISTH

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142464/1/jth13902_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142464/2/jth13902.pd

CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients. We design a labeler to automatically detect the presence of 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation. We investigate different approaches to using the uncertainty labels for training convolutional neural networks that output the probability of these observations given the available frontal and lateral radiographs. On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies. We then evaluate our best model on a test set composed of 500 chest radiographic studies annotated by a consensus of 5 board-certified radiologists, and compare the performance of our model to that of 3 additional radiologists in the detection of 5 selected pathologies. On Cardiomegaly, Edema, and Pleural Effusion, the model ROC and PR curves lie above all 3 radiologist operating points. We release the dataset to the public as a standard benchmark to evaluate performance of chest radiograph interpretation models. The dataset is freely available at https://stanfordmlgroup.github.io/competitions/chexpert .Comment: Published in AAAI 201

Clinical practice: The bleeding child. Part II: Disorders of secondary hemostasis and fibrinolysis

Bleeding complications in children may be caused by disorders of secondary hemostasis or fibrinolysis. Characteristic features in medical history and physical examination, especially of hemophilia, are palpable deep hematomas, bleeding in joints and muscles, and recurrent bleedings. A detailed medical and family history combined with a thorough physical examination is essential to distinguish abnormal from normal bleeding and to decide whether it is necessary to perform diagnostic laboratory evaluation. Initial laboratory tests include prothrombin time and activated partial thromboplastin time. Knowledge of the classical coagulation cascade with its intrinsic, extrinsic, and common pathways, is useful to identify potential defects in the coagulation in order to decide which additional coagulation tests should be performed

Germline mutations in ETV6 are associated with thrombocytopenia, red cell macrocytosis and predisposition to lymphoblastic leukemia

Some familial platelet disorders are associated with predisposition to leukemia, myelodysplastic syndrome (MDS) or dyserythropoietic anemia. We identified a family with autosomal dominant thrombocytopenia, high erythrocyte mean corpuscular volume (MCV) and two occurrences of B cell-precursor acute lymphoblastic leukemia (ALL). Whole-exome sequencing identified a heterozygous single-nucleotide change in ETV6 (ets variant 6), c.641C>T, encoding a p.Pro214Leu substitution in the central domain, segregating with thrombocytopenia and elevated MCV. A screen of 23 families with similar phenotypes identified 2 with ETV6 mutations. One family also had a mutation encoding p.Pro214Leu and one individual with ALL. The other family had a c.1252A>G transition producing a p.Arg418Gly substitution in the DNA-binding domain, with alternative splicing and exon skipping. Functional characterization of these mutations showed aberrant cellular localization of mutant and endogenous ETV6, decreased transcriptional repression and altered megakaryocyte maturation. Our findings underscore a key role for ETV6 in platelet formation and leukemia predisposition

Text Mining Emergent Human Behaviors for Interactive Systems

The additional fee must be paid to ACM. This text field is large enough to hold the appropriate release statement assuming it is single spaced. Every submission will be assigned their own unique DOI string to be included here. Abstract People engage with thousands of situations, activities, and objects on a daily basis. Hand-coding this knowledge into interactive systems is prohibitively labor-intensive, but fiction captures a vast number of human lives in moment to moment detail. In this paper, we bootstrap a knowledge graph of human activities by text mining a large dataset of modern fiction on the web. Our knowledge graph, Augur, describes human actions over time as conditioned by nearby locations, people, and objects. Applications can use this graph to react to human behavior in a data-driven way. We demonstrate an Augur-enhanced video game world in which non-player characters follow realistic patterns of behavior, interact with their environment and each other, and respond to the user's behavior