IRS-HD: an intelligent personalized recommender system for heart disease patients in a tele-health environment

The use of intelligent technologies in clinical decision making support may play a promising role in improving the quality of heart disease patients’ life and helping to reduce cost and workload involved in their daily health care in a tele-health environment. The objective of this demo proposal is to demonstrate an intelligent prediction system we developed, called IRS-HD, that accurately advises patients with heart diseases concerning whether they need to take the body test today or not based on the analysis of their medical data during the past a few days. Easy-to-use user friendly interfaces are developed for users to supply necessary inputs to the system and receive recommendations from the system. IRS-HD yields satisfactory recommendation accuracy, offers a promising way for reducing the risk of incorrect recommendations, as well saves the workload for patients to conduct body tests every day

An intelligent recommender system based on short-term risk prediction for heart disease patients

In this paper, an intelligent recommender system is developed, which uses an innovative time series prediction algorithm to provide recommendations to heart disease patients in the tele-health environment. Based on analytics of each patient’s medical tests in records, the system provides the patient with decision support for necessity of medical tests. The experimental results show that the proposed system yields satisfactory accuracy in recommendations. The system also offers a promising way for saving the workload for patients and healthcare practitioners in conducting daily medical tests. The research will help reduce the workload and cost in healthcare and help the healthcare industry transform from the traditional scenario to more a personalized paradigm in a tele-health environment

Lingering Sound: Event-Related Phase-Amplitude Coupling and Phase-Locking in Fronto-Temporo-Parietal Functional Networks During Memory Retrieval of Music Melodies

Brain oscillations and connectivity have emerged as promising measures of evaluating memory processes, including encoding, maintenance, and retrieval, as well as the related executive function. Although many studies have addressed the neural mechanisms underlying working memory, most of these studies have focused on the visual modality. Neurodynamics and functional connectivity related to auditory working memory are yet to be established. In this study, we explored the dynamic of high density (128-channel) electroencephalography (EEG) in a musical delayed match-to-sample task (DMST), in which 36 participants were recruited and were instructed to recognize and distinguish the target melodies from similar distractors. Event-related spectral perturbations (ERSPs), event-related phase-amplitude couplings (ERPACs), and phase-locking values (PLVs) were used to determine the corresponding brain oscillations and connectivity. First, we observed that low-frequency oscillations in the frontal, temporal, and parietal regions were increased during the processing of both target and distracting melodies. Second, the cross-frequency coupling between low-frequency phases and high-frequency amplitudes was elevated in the frontal and parietal regions when the participants were distinguishing between the target from distractor, suggesting that the phase-amplitude coupling could be an indicator of neural mechanisms underlying memory retrieval. Finally, phase-locking, an index evaluating brain functional connectivity, revealed that there was fronto-temporal phase-locking in the theta band and fronto-parietal phase-locking in the alpha band during the recognition of the two stimuli. These findings suggest the existence of functional connectivity and the phase-amplitude coupling in the neocortex during musical memory retrieval, and provide a highly resolved timeline to evaluate brain dynamics. Furthermore, the inter-regional phase-locking and phase-amplitude coupling among the frontal, temporal and parietal regions occurred at the very beginning of musical memory retrieval, which might reflect the precise timing when cognitive resources were involved in the retrieval of targets and the rejection of similar distractors. To the best of our knowledge, this is the first EEG study employing a naturalistic task to study auditory memory processes and functional connectivity during memory retrieval, results of which can shed light on the use of natural stimuli in studies that are closer to the real-life applications of cognitive evaluations, mental treatments, and brain-computer interface

How to Approach Para-Aortic Lymph Node Metastases During Exploration for Suspected Periampullary Carcinoma:Resection or Bypass?

Background: Intraoperative para-aortic lymph node (PALN) sampling during surgical exploration in patients with suspected pancreatic head cancer remains controversial. Objective: The aim of this study was to assess the value of routine PALN sampling and the consequences of different treatment strategies on overall patient survival. Methods: A retrospective, multicenter cohort study was performed in patients who underwent surgical exploration for suspected pancreatic head cancer. In cohort A, the treatment strategy was to avoid pancreatoduodenectomy and to perform a double bypass procedure when PALN metastases were found during exploration. In cohort B, routinely harvested PALNs were not examined intraoperatively and pancreatoduodenectomy was performed regardless. PALNs were examined with the final resection specimen. Clinicopathological data, survival data and complication data were compared between study groups. Results: Median overall survival for patients with PALN metastases who underwent a double bypass procedure was 7.0 months (95% confidence interval [CI] 5.5–8.5), versus 11 months (95% CI 8.8–13) in the pancreatoduodenectomy group (p = 0.049). Patients with PALN metastases who underwent pancreatoduodenectomy had significantly increased postoperative morbidity compared with patients who underwent a double bypass procedure (p < 0.001). In multivariable analysis, severe comorbidity (ASA grade 2 or higher) was an independent predictor for decreased survival in patients with PALN involvement (hazard ratio 3.607, 95% CI 1.678–7.751; p = 0.001). Conclusion: In patients with PALN metastases, pancreatoduodenectomy was associated with significant survival benefit compared with a double bypass procedure, but with increased risk of complications. It is important to weigh the advantages of resection versus bypass against factors such as comorbidities and clinical performance when positive intraoperative PALNs are found

CrossCheck:toward passive sensing and detection of mental health changes in people with schizophrenia

Early detection of mental health changes in individuals with serious mental illness is critical for effective intervention. CrossCheck is the first step towards the passive monitoring of mental health indicators in patients with schizophrenia and paves the way towards relapse prediction and early intervention. In this paper, we present initial results from an ongoing randomized control trial, where passive smartphone sensor data is collected from 21 outpatients with schizophrenia recently discharged from hospital over a period ranging from 2-8.5 months. Our results indicate that there are statistically significant associations between automatically tracked behavioral features related to sleep, mobility, conversations, smartphone usage and self-reported indicators of mental health in schizophrenia. Using these features we build inference models capable of accurately predicting aggregated scores of mental health indicators in schizophrenia with a mean error of 7.6% of the score range. Finally, we discuss results on the level of personalization that is needed to account for the known variations within people. We show that by leveraging knowledge from a population with schizophrenia, it is possible to train accurate personalized models that require fewer individual-specific data to quickly adapt to new user

PFrauDetector: a parallelized graph mining approach for efficient fraudulent phone call detection

In recent years, fraud is becoming more rampant internationally with the development of modern technology and global communication. Due to the rapid growth in the volume of call logs, the task of fraudulent phone call detection is confronted with Big Data issues in real-world implementations. While our previous work, FrauDetector, has addressed this problem and achieved some promising results, it can be further enhanced as it focuses on the fraud detection accuracy while the efficiency and scalability are not on the top priority. Meanwhile, other known approaches suffer from long training time and/or cannot accurately detect fraudulent phone calls in real time. In this paper, we propose a highly- efficient parallelized graph-miningbased fraudulent phone call detection framework, namely PFrauDetector, which is able to automatically label fraudulent phone numbers with a 'fraud' tag, a crucial prerequisite for distinguishing fraudulent phone call numbers from the normal ones. PFrauDetector generates smaller, more manageable subnetworks from the original graph and performs a parallelized weighted HITS algorithm for significant speed acceleration in the graph learning module. It adopts a novel aggregation approach to generate the trust (or experience) value for each phone number (or user) based on their respective local values. We conduct a comprehensive experimental study based on a real dataset collected through an anti-fraud mobile application, Whoscall. The results demonstrate a significantly improved efficiency of our approach compared to FrauDetector and superior performance against other major classifier-based methods