Ensembling classical machine learning and deep learning approaches for morbidity identification from clinical notes

The past decade has seen an explosion of the amount of digital information generated within the healthcare domain. Digital data exist in the form of images, video, speech, transcripts, electronic health records, clinical records, and free-text. Analysis and interpretation of healthcare data is a daunting task, and it demands a great deal of time, resources, and human effort. In this paper, we focus on the problem of co-morbidity recognition from patient’s clinical records. To this aim, we employ both classical machine learning and deep learning approaches.We use word embeddings and bag-of-words representations, coupled with feature selection techniques. The goal of our work is to develop a classification system to identify whether a certain health condition occurs for a patient by studying his/her past clinical records. In more detail, we have used pre-trained word2vec, domain-trained, GloVe, fastText, and universal sentence encoder embeddings to tackle the classification of sixteen morbidity conditions within clinical records. We have compared the outcomes of classical machine learning and deep learning approaches with the employed feature representation methods and feature selection methods. We present a comprehensive discussion of the performances and behaviour of the employed classical machine learning and deep learning approaches. Finally, we have also used ensemble learning techniques over a large number of combinations of classifiers to improve the single model performance. For our experiments, we used the n2c2 natural language processing research dataset, released by Harvard Medical School. The dataset is in the form of clinical notes that contain patient discharge summaries. Given the unbalancedness of the data and their small size, the experimental results indicate the advantage of the ensemble learning technique with respect to single classifier models. In particular, the ensemble learning technique has slightly improved the performances of single classification models but has greatly reduced the variance of predictions stabilizing the accuracies (i.e., the lower standard deviation in comparison with single classifiers). In real-life scenarios, our work can be employed to identify with high accuracy morbidity conditions of patients by feeding our tool with their current clinical notes. Moreover, other domains where classification is a common problem might benefit from our approach as well

SmartFABER: Recognizing fine-grained abnormal behaviors for early detection of mild cognitive impairment

Objective: In an ageing world population more citizens are at risk of cognitive impairment, with negative consequences on their ability of independent living, quality of life and sustainability of healthcare systems. Cognitive neuroscience researchers have identified behavioral anomalies that are significant indicators of cognitive decline. A general goal is the design of innovative methods and tools for continuously monitoring the functional abilities of the seniors at risk and reporting the behavioral anomalies to the clinicians. SmartFABER is a pervasive system targeting this objective. Methods: A non-intrusive sensor network continuously acquires data about the interaction of the senior with the home environment during daily activities. A novel hybrid statistical and knowledge-based technique is used to analyses this data and detect the behavioral anomalies, whose history is presented through a dashboard to the clinicians. Differently from related works, SmartFABER can detect abnormal behaviors at a fine-grained level. Results: We have fully implemented the system and evaluated it using real datasets, partly generated by performing activities in a smart home laboratory, and partly acquired during several months of monitoring of the instrumented home of a senior diagnosed with MCI. Experimental results, including comparisons with other activity recognition techniques, show the effectiveness of SmartFABER in terms of recognition rates

Phosphatidylinositol 3-Kinase/AKT pathway regulates the endoplasmic reticulum to Golgi traffic of ceramide in glioma cells : a link between lipid signaling pathways involved in the control of cell survival

Different lines of evidence indicate that both aberrant activation of the phosphatidylinositol 3-OH kinase (PI3K)/Akt survival pathway and down-regulation of the death mediator ceramide play a critical role in the aggressive behavior, apoptosis resistance, and adverse clinical outcome of glioblastoma multiforme. Furthermore, the inhibition of the PI3K/Akt pathway and the up-regulation of ceramide have been found functional to the activity of many cytotoxic treatments against glioma cell lines and glioblastomas as well. A reciprocal control between PI3K/Akt and ceramide signaling in glioma cell survival/death is suggested by data demonstrating a protective role of PI3K/Akt on ceramide-induced cell death in glial cells. In this study we investigated the role of the PI3K/Akt pathway in the regulation of the ceramide metabolism in C6 glioma cells, a cell line in which the PI3K/Akt pathway is constitutively activated. Metabolic experiments performed with different radioactive metabolic precursors of sphingolipids and microscopy studies with fluorescent ceramides demonstrated that the chemical inhibition of PI3K and the transfection with a dominant negative Akt strongly inhibited ceramide utilization for the biosynthesis of complex sphingolipids by controlling the endoplasmic reticulum (ER) to Golgi vesicular transport of ceramide. These findings constitute the first evidence for a PI3K/Akt-dependent regulation of vesicle-mediated movements of ceramide in the ER-Golgi district. Moreover, the findings also suggest the activation of the PI3K/Akt pathway as crucial to coordinate the biosynthesis of membrane complex sphingolipids with cell proliferation and growth and/or to maintain low ceramide levels, especially as concerns those treatments that promote ceramide biosynthesis in the ER

TF-IDF vs word embeddings for morbidity identification in clinical notes: An initial study

Today, we are seeing an ever-increasing number of clinical notes that contain clinical results, images, and textual descriptions of patient's health state. All these data can be analyzed and employed to cater novel services that can help people and domain experts with their common healthcare tasks. However, many technologies such as Deep Learning and tools like Word Embeddings have started to be investigated only recently, and many challenges remain open when it comes to healthcare domain applications. To address these challenges, we propose the use of Deep Learning and Word Embeddings for identifying sixteen morbidity types within textual descriptions of clinical records. For this purpose, we have used a Deep Learning model based on Bidirectional Long-Short Term Memory (LSTM) layers which can exploit state-of-the-art vector representations of data such as Word Embeddings. We have employed pre-trained Word Embeddings namely GloVe and Word2Vec, and our own Word Embeddings trained on the target domain. Furthermore, we have compared the performances of the deep learning approaches against the traditional tf-idf using Support Vector Machine and Multilayer perceptron (our baselines). From the obtained results it seems that the latter outperform the combination of Deep Learning approaches using any word embeddings. Our preliminary results indicate that there are specific features that make the dataset biased in favour of traditional machine learning approaches

Sphingosine-1-Phosphate in the Tumor Microenvironment: A Signaling Hub Regulating Cancer Hallmarks

As a key hub of malignant properties, the cancer microenvironment plays a crucial role intimately connected to tumor properties. Accumulating evidence supports that the lysophospholipid sphingosine-1-phosphate acts as a key signal in the cancer extracellular milieu. In this review, we have a particular focus on glioblastoma, representative of a highly aggressive and deleterious neoplasm in humans. First, we highlight recent advances and emerging concepts for how tumor cells and different recruited normal cells contribute to the sphingosine-1-phosphate enrichment in the cancer microenvironment. Then, we describe and discuss how sphingosine-1-phosphate signaling contributes to favor cancer hallmarks including enhancement of proliferation, stemness, invasion, death resistance, angiogenesis, immune evasion and, possibly, aberrant metabolism. We also discuss the potential of how sphingosine-1-phosphate control mechanisms are coordinated across distinct cancer microenvironments. Further progress in understanding the role of S1P signaling in cancer will depend crucially on increasing knowledge of its participation in the tumor microenvironment

Aluminum alloy production for the reinforcement of the CMS conductor

The Compact Muon Solenoid (CMS) is one of the general-purpose detectors to be provided for the Large Hadron Collider (LHC) project at CERN. The design field of the CMS superconducting magnet is 4 T, the magnetic length is 12.5 m and the free bore is 6 m. To reinforce the high-purity (99.998%) Al-stabilized conductor of the magnet against the magnetic loadings experienced during operation at 4.2 K, two continuous sections of Al-alloy (AA) reinforcement are Electron Beam (EB) welded to it. The reinforcements have a section of 24*18 mm and are produced in continuous 2.55 km lengths. The alloy EN AW-6082 has been selected for the reinforcement due to its excellent extrudability, high strength in the precipitation hardened states, high toughness and strength at cryogenic temperature and good EB weldability. Each of the continuous lengths of the reinforcement is extruded billet on billet and press quenched on-line from the extrusion temperature in an industrial extrusion plant. In order to insure the ready EB weldability of the reinforcement onto the pure aluminum of the insert, tight dimensional tolerances and proper surface finish of the reinforcement are required in the as-extruded state. As well, in order to facilitate the winding operation of the conductor, the uniformity of the mechanical properties of the extruded reinforcement, especially at the billet on billet joints, is critical. To achieve these requirements in an industrial environment, substantial effort was made to refine existing production techniques and to monitor critical extrusion parameters during production. This paper summarizes the main results obtained during the establishment of the extrusion line and of the production phase of the reinforcement. (10 refs)

Continuous EB welding of the reinforcement of the CMS conductor

The Compact Muon Solenoid (CMS) is one of the general-purpose detectors to be provided for the LHC project at CERN. The design field of the CMS superconducting magnet is 4 T, the magnetic length is 12.5 m and the free bore is 6 m. In order to withstand the electro-mechanical forces during the operation of the CMS magnet, the superconducting cable embedded in a 99.998% pure aluminum matrix is reinforced with two sections of aluminum alloy EN AW-6082 assembled by continuous Electron Beam Welding (EBW). A dedicated production line has been designed by Techmeta, a leading company in the field of EBW. The production line has a total length of 70 m. Non-stop welding of each of the 20 lengths of 2.5 km, required to build the coil, will last 22 hours. EBW is the most critical process involved in the production line. The main advantage of the EBW process is to minimize the Heat Affected Zone; this is particularly important for avoiding damage to the superconducting cable located only 4.7 mm from the welded joints. Two welding guns of 20 kW each operate in parallel in a vacuum chamber fitted with dynamic airlocks. After welding, the conductor is continuously machined on the four faces and on each corner to obtain the required dimensions and surface finish. Special emphasis has been put on quality monitoring. All significant production parameters are recorded during operation and relevant samples are taken from each produced length for destructive testing purposes. In addition, a continuous phased array ultrasonic checking device is located immediately after the welding unit for the continuous welding quality control, along with a dimension laser measurement unit following the machining. (8 refs)

Fenton Oxidation of Gaseous Isoprene on Aqueous Surfaces

We report that gaseous isoprene ISO(g) is oxidized into soluble species on the surface of aqueous acidic FeCl_2 solutions simultaneously exposed to H_2O_2(g). In our experiments, ISO(g) and/or H_2O_2(g) streams intersect aqueous pH 2 FeCl_2 microjets for 10 μs. The products formed in these reactive encounters are identified in situ via online electrospray ionization mass spectrometry. We found that the (ISO)nH^+ oligomers generated from ISO(g) on the surface of pH < 4 water are oxidized into myriad products whose combined yields exceed 5%. MS^2 analysis reveals that the positive ions derived from the protonation of neutral products split H_2O and O neutrals, whereas the less abundant negative carboxylate ion products undergo CO, H_2O, and CO_2 losses. Significantly, all products are fully quenched by ·OH scavenger tert-butyl alcohol. These results are consistent with an oxidation process initiated by the addition of ·OH from (Fe^(2+)(aq) + H_2O_2(g)) to (ISO)nH^+, followed by fast reactions involving dissolved H_2O_2, HO_2·, and O_2 that lead to polyols; carbonyls; and, to a lesser extent, carboxylic acids. Our experiments demonstrate that gas-phase olefins are oxidized upon colliding on the surface of Fe-containing acidic aqueous media under typical tropospheric conditions

The CMS conductor

The Compact Muon Solenoid (CMS) is one of the experiments, which are being designed in the framework of the Large Hadron Collider (LHC) project at CERN, the design field of the CMS magnet is 4 T, the magnetic length is 13 m and the aperture is 6 m. This high magnetic field is achieved by means of a 4 layer, 5 modules superconducting coil. The coil is wound from an Al-stabilized Rutherford type conductor. The nominal current of the magnet is 20 kA at 4.5 K. In the CMS coil the structural function is ensured, unlike in other existing Al-stabilized thin solenoids, both by the Al-alloy reinforced conductor and the external former. In this paper the retained manufacturing process of the 50-km long reinforced conductor is described. In general the Rutherford type cable is surrounded by high purity aluminium in a continuous co-extrusion process to produce the Insert. Thereafter the reinforcement is joined by Electron Beam Welding to the pure Al of the insert, before being machined to the final dimensions. During the manufacture the bond quality between the Rutherford cable and the high purity aluminium as well as the quality of the EB welding are continuously controlled by a novel ultrasonic phased array system. The dimensions of the insert and the final conductor are measured by laser micrometer. (8 refs)