Contextual Embeddings from Clinical Notes Improves Prediction of Sepsis

AbstractSepsis, a life-threatening organ dysfunction, is a clinical syndrome triggered by acute infection and affects over 1 million Americans every year. Untreated sepsis can progress to septic shock and organ failure, making sepsis one of the leading causes of morbidity and mortality in hospitals. Early detection of sepsis and timely antibiotics administration is known to save lives. In this work, we design a sepsis prediction algorithm based on data from electronic health records (EHR) using a deep learning approach. While most existing EHR-based sepsis prediction models utilize structured data including vitals, labs, and clinical information, we show that incorporation of features based on clinical texts, using a pre-trained neural language representation model, allows for incorporation of unstructured data without an explicit need for ontology-based named-entity recognition and classification. The proposed model is trained on a large critical care database of over 40,000 patients, including 2805 septic patients, and is compared against competing baseline models. In comparison to a baseline model based on structured data alone, incorporation of clinical texts improved AUC from 0.81 to 0.84. Our findings indicate that incorporation of clinical text features via a pre-trained language representation model can improve early prediction of sepsis and reduce false alarms.</jats:p

Cytotoxicity evaluation and magnetic characteristics of mechano-thermally synthesized CuNi nanoparticles for hyperthermia

CuNi alloys are very well known, both in academia and industry, based on their wide range of applications. In the present investigation, the previously synthesized Cu_0.5Ni_0.5 nanoparticles (NPs) by mechano-thermal method were studied more extensively. Phase composition and morphology of the samples were studied by employing x-ray diffraction (XRD), field-emission scanning electron microscopy (FESEM), and transmission electron microscopy (TEM) techniques. The Curie temperature (<em><i>T</i></em>_c) was determined by differential scanning calorimetry (DSC). In vitro cytotoxicity was studied through methyl-thiazolyl-tetrazolium (MTT) assay. XRD and FESEM results indicated the formation of single-phase Cu_0.5Ni_0.5. TEM micrographs showed that the mean particle size of powders is 20 nm. DSC results revealed that <em><i>T</i></em>_c of mechano-thermally synthesized Cu_0.5Ni_0.5 is 44 °C. The MTT assay results confirmed the viability and proliferation of human bone marrow stem cells in contact with Cu_0.5Ni_0.5 NPs. In summary, the fabricated particles were demonstrated to have potential in low concentrations for cancer treatment applications

Leveraging clinical data across healthcare institutions for continual learning of predictive risk models

AbstractThe inherent flexibility of machine learning-based clinical predictive models to learn from episodes of patient care at a new institution (site-specific training) comes at the cost of performance degradation when applied to external patient cohorts. To exploit the full potential of cross-institutional clinical big data, machine learning systems must gain the ability to transfer their knowledge across institutional boundaries and learn from new episodes of patient care without forgetting previously learned patterns. In this work, we developed a privacy-preserving learning algorithm named WUPERR (Weight Uncertainty Propagation and Episodic Representation Replay) and validated the algorithm in the context of early prediction of sepsis using data from over 104,000 patients across four distinct healthcare systems. We tested the hypothesis, that the proposed continual learning algorithm can maintain higher predictive performance than competing methods on previous cohorts once it has been trained on a new patient cohort. In the sepsis prediction task, after incremental training of a deep learning model across four hospital systems (namely hospitals H-A, H-B, H-C, and H-D), WUPERR maintained the highest positive predictive value across the first three hospitals compared to a baseline transfer learning approach (H-A: 39.27% vs. 31.27%, H-B: 25.34% vs. 22.34%, H-C: 30.33% vs. 28.33%). The proposed approach has the potential to construct more generalizable models that can learn from cross-institutional clinical big data in a privacy-preserving manner.</jats:p