Healthcare support for underserved communities using a mobile social media platform

This paper investigates the correlations between the energy consumption of Android devices and the presence of threats (e.g. battery-drain attacks). In particular, this paper proposes a model for the energy consumption of single hardware components of a mobile device during normal usage and under attack. The model has been implemented in a kernel module and used to build up an energetic signature of both legal and malicious behaviors of WiFi hardware component in different Android devices. Such activity allows us to build a tentative database of signatures that can be used to detect attacks by means of the actual energy consumption of a mobile device. The proposed power consumption model and kernel module can be applied also to other hardware components, so to obtain very precise energetic signatures.

Towards energy-aware intrusion detection systems on mobile devices

Mobile Network Operators (MNOs) keep a strict control over users accessing the networks by means of the Subscriber Identity Module (SIM). This module grants the user access to the network, by performing the registration and authentication of the user’s device. Without a valid Subscribe IdentityModule (SIM) module and a successful authentication, mobile devices are not granted access and, hence, they are not allowed to inject any traffic in the mobile infrastructure. Nevertheless, in this paper we describe an attack to the security of a mobile network allowing an unauthenticated malicious mobile device to inject traffic in the mobile operator’s infrastructure. We show that even with devices without any SIM module it is possible to inject high levels of signaling traffic in the mobile infrastructure, causing significant service degradation up to a full-fledged Denial of Service (DoS) attack.

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A Denial of Service Attack to GSM Networks via Attach Procedure

Background: Though artificial intelligence (AI) has the potential to augment the patient-physician relationship in primary care, bias in intelligent health care systems has the potential to differentially impact vulnerable patient populations.
Objective: The purpose of this scoping review is to summarize the extent to which AI systems in primary care examine the inherent bias toward or against vulnerable populations and appraise how these systems have mitigated the impact of such biases during their development.
Methods: We will conduct a search update from an existing scoping review to identify studies on AI and primary care in the following databases: Medline-OVID, Embase, CINAHL, Cochrane Library, Web of Science, Scopus, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI, and arXiv. Two screeners will independently review all abstracts, titles, and full-text articles. The team will extract data using a structured data extraction form and synthesize the results in accordance with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines.
Results: This review will provide an assessment of the current state of health care equity within AI for primary care. Specifically, we will identify the degree to which vulnerable patients have been included, assess how bias is interpreted and documented, and understand the extent to which harmful biases are addressed. As of October 2020, the scoping review is in the title- and abstract-screening stage. The results are expected to be submitted for publication in fall 2021.
Conclusions: AI applications in primary care are becoming an increasingly common tool in health care delivery and in preventative care efforts for underserved populations. This scoping review would potentially show the extent to which studies on AI in primary care employ a health equity lens and take steps to mitigate bias.

Health Equity in Artificial Intelligence and Primary Care Research: Protocol for a Scoping Review

A medical device capable of determining its location is provided. The medical device comprises a memory, one or more antennas, one or more processors coupled with the memory and the one or more antennas, a location manager component executable by the one or more processors. The location manager component is configured to receive first location information from a first location information source and second location information from a second location information source, to rank the first location information source and the second location information source according to a hierarchy of location information sources, the hierarchy of location information sources specifying that the first location information source is of higher rank than the second location information source, determine an approximate location of the medical device based on the first location information, and improve the accuracy of the approximate location based on the second location information.

Systems and methods of determining location using a medical device

People in rural areas do not have convenient access to quality healthcare close to their locations. Although there are health care centers in villages, they are not staffed and most lack in basic infrastructural facilities. Villagers are forced to travel long distances to district head quarters or major cities to avail of medical facilities, often at high cost, which they can ill afford. We developed a mobile based decision support framework on the Android open platform and demonstrated the feasibility of building the complete end to end system for large scale deployment to outreach health worker. The decision support system enables the outreach health worker to deliver consistent and quality primary healthcare to rural population. The same platform can host other revenue generating opportunities for the outreach worker to keep them motivated and providing them a means of livelihood.

/pdf/mobile-decision-support-system-for-outreach-health-worker-2dks9fi4zd.pdf

Mobile decision support system for outreach health worker

The present invention provides a system, a method and a computer program product for, identifying an anonymous patient in a health care location, when the anonymous patient requires medical attention. A health parameter data of the patient is acquired by a sensor unit, and transmitted to a gateway module associated with the patient. In an event the acquired health parameter data transgresses preconfigured limits or preconfigured pattern(s); an alert is generated by the gateway module, and transmitted to a context server. The context server is configured to validate the alert; instruct the gateway module to emit a colored light corresponding to the color code and transmit the gateway identifier, the color code and a location of a nearest connector, to a medical agent for the purpose of locating the patient.

Systems, methods and computer-readable media for identifying an anonymous patient

The present invention provides a middleware to automatically bind demographic details of a patient to at least one sensor included in a body worn monitoring device of the patient. In accordance with an embodiment, the middleware comprises a receiver module and an edge intelligence module. The receiver module receives identification code from a sensor associated with the patient, a unique patient identification code from a central server, and signals from each sensor associated with the patient. The edge intelligence module associates the unique patient identification code and the demographic details of the patient with each of the at least one sensor, and updates the central server with sensor identification code of each sensor associated with the unique patient identification code.

Nataraj Subbaraya Kuntagod

Papers

Mobile decision support system for outreach health worker

Systems, methods and computer-readable media for identifying an anonymous patient

Method and system for auto rebinding of patient demographic details to patient monitoring devices