Detecting Hypoglycemia Incidents Reported in Patients\u27 Secure Messages: Using Cost-Sensitive Learning and Oversampling to Reduce Data Imbalance

BACKGROUND: Improper dosing of medications such as insulin can cause hypoglycemic episodes, which may lead to severe morbidity or even death. Although secure messaging was designed for exchanging nonurgent messages, patients sometimes report hypoglycemia events through secure messaging. Detecting these patient-reported adverse events may help alert clinical teams and enable early corrective actions to improve patient safety. OBJECTIVE: We aimed to develop a natural language processing system, called HypoDetect (Hypoglycemia Detector), to automatically identify hypoglycemia incidents reported in patients\u27 secure messages. METHODS: An expert in public health annotated 3000 secure message threads between patients with diabetes and US Department of Veterans Affairs clinical teams as containing patient-reported hypoglycemia incidents or not. A physician independently annotated 100 threads randomly selected from this dataset to determine interannotator agreement. We used this dataset to develop and evaluate HypoDetect. HypoDetect incorporates 3 machine learning algorithms widely used for text classification: linear support vector machines, random forest, and logistic regression. We explored different learning features, including new knowledge-driven features. Because only 114 (3.80%) messages were annotated as positive, we investigated cost-sensitive learning and oversampling methods to mitigate the challenge of imbalanced data. RESULTS: The interannotator agreement was Cohen kappa=.976. Using cross-validation, logistic regression with cost-sensitive learning achieved the best performance (area under the receiver operating characteristic curve=0.954, sensitivity=0.693, specificity 0.974, F1 score=0.590). Cost-sensitive learning and the ensembled synthetic minority oversampling technique improved the sensitivity of the baseline systems substantially (by 0.123 to 0.728 absolute gains). Our results show that a variety of features contributed to the best performance of HypoDetect. CONCLUSIONS: Despite the challenge of data imbalance, HypoDetect achieved promising results for the task of detecting hypoglycemia incidents from secure messages. The system has a great potential to facilitate early detection and treatment of hypoglycemia

Alter ego, state of the art on user profiling: an overview of the most relevant organisational and behavioural aspects regarding User Profiling.

This report gives an overview of the most relevant organisational and\ud behavioural aspects regarding user profiling. It discusses not only the\ud most important aims of user profiling from both an organisation’s as\ud well as a user’s perspective, it will also discuss organisational motives\ud and barriers for user profiling and the most important conditions for\ud the success of user profiling. Finally recommendations are made and\ud suggestions for further research are given

Characterizing the Information Needs of Rural Healthcare Practitioners with Language Agnostic Automated Text Analysis

Objectives – Previous research has characterized urban healthcare providers\u27 information needs, using various qualitative methods. However, little is known about the needs of rural primary care practitioners in Brazil. Communication exchanged during tele-consultations presents a unique data source for the study of these information needs. In this study, I characterize rural healthcare providers\u27 information needs expressed electronically, using automated methods. Methods – I applied automated methods to categorize messages obtained from the telehealth system from two regions in Brazil. A subset of these messages, annotated with top-level categories in the DeCS terminology (the regional equivalent of MeSH), was used to train text categorization models, which were then applied to a larger, unannotated data set. On account of their more granular nature, I focused on answers provided to the queries sent by rural healthcare providers. I studied these answers, as surrogates for the information needs they met. Message representations were generated using methods of distributional semantics, permitting the application of k-Nearest Neighbor classification for category assignment. The resulting category assignments were analyzed to determine differences across regions, and healthcare providers. Results – Analysis of the assigned categories revealed differences in information needs across regions, corresponding to known differences in the distributions of diseases and tele-consultant expertise across these regions. Furthermore, information needs of rural nurses were observed to be different from those documented in qualitative studies of their urban counterparts, and the distribution of expressed information needs categories differed across types of providers (e.g. nurses vs. physicians). Discussion – The automated analysis of large amounts of digitally-captured tele-consultation data suggests that rural healthcare providers\u27 information needs in Brazil are different than those of their urban counterparts in developed countries. The observed disparities in information needs correspond to known differences in the distribution of illness and expertise in these regions, supporting the applicability of my methods in this context. In addition, these methods have the potential to mediate near real-time monitoring of information needs, without imposing a direct burden upon healthcare providers. Potential applications include automated delivery of needed information at the point of care, needs-based deployment of tele-consultation resources and syndromic surveillance. Conclusion – I used automated text categorization methods to assess the information needs expressed at the point of care in rural Brazil. My findings reveal differences in information needs across regions, and across practitioner types, demonstrating the utility of these methods and data as a means to characterize information needs

eVisits in the digital era of Swedish primary care

Objective: To evaluate asynchronous digital visits (eVisits) with regard to digital communication, clinical decisionmaking,and subsequent care utilization in the digital era of primary care in Sweden.Methods: A mixed-methods approach was adopted across the various papers in the thesis, with all studiesevaluating the eVisit platform Flow in various clinical contexts.- Paper I was a comparative study of digital triage decisions when presented with automated patienthistory reports generated by the platform. Inter-rater reliability of triage decisions by majority vote in apanel of five physicians was compared to triage decisions by a machine learning model trained usingdata labelled by an expert primary care physician.- Paper II was a qualitative focus group study of nurse and physician experiences of digitalcommunication at three primary health care centers using the platform. Themes were generated usingqualitative content analysis as described by Graneheim and Lundman.- Papers III and IV were observational studies comparing office visits in the Skåne Region from Capio,a large private health care provider, to eVisit patients from Capio Go, a national eVisit service. Adultpatients with a chief complaint of sore throat, dysuria, or cough/common cold/influenza were recruited.eVisit patients were recruited prospectively digitally prior to their eVisit, while the office visit controlgroup was recruited retrospectively using letters. Paper III primarily compared antibiotic prescriptionrates per sore throat visit, while paper IV primarily compared subsequent physical health careutilization within two weeks for patients in the Skåne Region.Results: Interrater reliability was low (Cohen κ 0.17) between the panel majority vote and the machine learningmodel. Physicians and nurses experienced digitally filtered primary care, adjusting to a novel medium ofcommunication highlighting challenges in interpreting symptoms through text as well as alterations in practiceworkflow using asynchronous communication. Antibiotics prescription rate within three days was not higher aftereVisits compared to office visits (169/798 (21.2%) vs. 124/312 (39.7%) for sore throat, respectively; P<.001). Nosignificant differences in subsequent physical visits within two weeks (excluding the first 48 h of expected “digi-physical”care) were noted following eVisits compared to office visits (179 (18.0%) vs. 102 (17.6%); P = .854).Conclusions: eVisits do not seem to be associated with over-prescription of antibiotics, or over-utilization ofphysical health care when assessing common infectious symptoms. Given staff experiencing uncertainties ininterpretation of symptoms and triage decisions being inconsistent, eVisits may be best used as one of manymodalities to access primary care, with focus placed on facilitating patient-centered professional judgement bystaff, rather than automation of complex decisions

Interpreting streaming biosignals:in search of best approaches to augmenting mobile health monitoring with machine learning for adaptive clinical decision support

We investigate Body Area Networks for ambulant patient monitoring. As well as sensing physiological parameters, BAN applications may provide feedback to patients. Automating formulation of feedback requires realtime analysis and interpretation of streaming biosignals and other context and knowledge sources. We illustrate with two prototype applications: the first is designed to detect epileptic seizures and support appropriate intervention. The second is a decision support application aiding weight management; the goal is to promote health and prevent chronic illnesses associated with overweight/obesity. We begin to explore extending these and other m-health applications with generic AI-based decision support and machine learning. Monitoring success of different behavioural change strategies could provide a basis for machine learning, enabling adaptive clinical decision support by personalising and adapting strategies to individuals and their changing needs. Data mining applied to BAN data aggregated from large numbers of patients opens up possibilities for discovery of new clinical knowledge

Automatic Message Triage: Decision Support from Patient-Provider Messages

Email communication between patients and healthcare providers is gaining popularity. However, healthcare providers are concerned about being inundated with patient messages and their inability to respond to messages in a timely manner. This work provides automated text mining decision support to overcome some of the challenges presented by email communication between patients and healthcare providers

Development and implementation of a remote monitoring and coaching intervention delivered using digital health technology for people with a history of cancer.

There is a need to improve care practices to optimally enhance physical health and health- related quality of life in people with a history of cancer. Intensive treatment of cancer can impact patients both acutely and chronically as long-term or late effects well after treatment completion. As a result, both patients with cancer and cancer survivors need additional support Supportive cancer care, including survivorship and rehabilitation services focuses on developing strategies to support survivors’ well-being and recovery during and after cancer treatment. However, despite the evidence-based benefits of these services, many barriers still exist that may restrict patients with cancer from participation and engagement. One possible solution to these challenges is the use of digital health technologies. The aim of this research was to explore current gaps in knowledge regarding digital health enabled supportive cancer care and design and develop a digital health enabled intervention, specifically tailored to the needs of people with a cancer diagnosis. The experience culminated in the implementation of a 10-week prospective cohort trial, focused on the feasibility and acceptability of a patient-provider tracking and exercise coaching portal. As evidenced by the research studies presented within this thesis, findings suggest that patient-centric supportive care can be provided to cancer patients using a digital health enabled approach. Further, remote monitoring and individual exercise coaching can feasibly be offered to patient populations who may not be able to conveniently access support services, or who choose to access these services remotely. Several recommendations for future research and future directions were provided to further this area of research