title: A review of Automatic end-to-end De-Identification: Is High Accuracy the Only Metric?

authors: Yogarajan, Vithya

subjects: Science & Technology

abstract: De-identification of electronic health records (EHR) is a vital step towards advancing health informatics research and maximising the use of available data. It is a two-step process where step one is the identification of protected health information (PHI), and step two is replacing such PHI with surrogates. Despite the recent advances in automatic de-identification of EHR, significant obstacles remain if the abundant health data available are to be used to the full potential. Accuracy in de-identification could be considered a necessary, but not sufficient condition for the use of EHR without individual patient consent. We present here a comprehensive review of the progress to date, both the impressive successes in achieving high accuracy and the significant risks and challenges that remain. To best of our knowledge, this is the first paper to present a complete picture of end-to-end automatic deidentification. We review 18 recently published automatic de-identification systems -designed to de-identify EHR in the form of free text- to show the advancements made in improving the overall accuracy of the system, and in identifying individual PHI. We argue that despite the improvements in accuracy there remain challenges in surrogate generation and replacements of identified PHIs, and the risks posed to patient protection and privacy.

text: A review of Automatic end-to-end De-Identification: Is High Accuracy the Only Metric? De-identification of electronic health records (EHR) is a vital step towards advancing health informatics research and maximising the use of available data. It is a two-step process where step one is the identification of protected health information (PHI), and step two is replacing such PHI with surrogates. Despite the recent advances in automatic de-identification of EHR, significant obstacles remain if the abundant health data available are to be used to the full potential. Accuracy in de-identification could be considered a necessary, but not sufficient condition for the use of EHR without individual patient consent. We present here a comprehensive review of the progress to date, both the impressive successes in achieving high accuracy and the significant risks and challenges that remain. To best of our knowledge, this is the first paper to present a complete picture of end-to-end automatic deidentification. We review 18 recently published automatic de-identification systems -designed to de-identify EHR in the form of free text- to show the advancements made in improving the overall accuracy of the system, and in identifying individual PHI. We argue that despite the improvements in accuracy there remain challenges in surrogate generation and replacements of identified PHIs, and the risks posed to patient protection and privacy.

year: 2020

title: Adaptive online domain incremental continual learning

authors: Gunasekara, Nuwan Amila

subjects: computer science

abstract: Continual Learning (CL) problems pose significant challenges for Neural Network (NN)s. Online Domain Incremental Continual Learning (ODI-CL) refers to situations where the data distribution may change from one task to another. These changes can severely affect the learned model, focusing too much on previous data and failing to properly learn and represent new concepts. Conversely, if a model constantly forgets previously learned knowledge, it may be deemed too unstable and unsuitable. This work proposes Online Domain Incremental Pool (ODIP), a novel method to cope with catastrophic forgetting. ODIP also employs automatic concept drift detection and does not require task ids during training. ODIP maintains a pool of learners, freezing and storing the best one after training on each task. An additional Task Predictor (TP) is trained to select the most appropriate NN from the frozen pool for prediction. We compare ODIP against regularization methods and observe that it yields competitive predictive performance.

text: Adaptive online domain incremental continual learning Continual Learning (CL) problems pose significant challenges for Neural Network (NN)s. Online Domain Incremental Continual Learning (ODI-CL) refers to situations where the data distribution may change from one task to another. These changes can severely affect the learned model, focusing too much on previous data and failing to properly learn and represent new concepts. Conversely, if a model constantly forgets previously learned knowledge, it may be deemed too unstable and unsuitable. This work proposes Online Domain Incremental Pool (ODIP), a novel method to cope with catastrophic forgetting. ODIP also employs automatic concept drift detection and does not require task ids during training. ODIP maintains a pool of learners, freezing and storing the best one after training on each task. An additional Task Predictor (TP) is trained to select the most appropriate NN from the frozen pool for prediction. We compare ODIP against regularization methods and observe that it yields competitive predictive performance.

year: 2022

title: Developing an integrated ocean observing system for New Zealand

authors: O'Callaghan, J

subjects: Science & Technology

abstract: New Zealand (NZ) is an island nation with stewardship of an ocean twenty times larger than its land area. While the challenges facing NZ's ocean are similar to other maritime countries, no coherent national plan exists that meets the needs of scientists, stakeholders or kaitiakitanga (guardianship) of NZ's ocean in a changing climate. The NZ marine science community used the OceanObs'19 white paper to establish a framework and implementation plan for a collaborative NZ ocean observing system (NZ-OOS). Co-production of ocean knowledge with Māori will be embedded in this national strategy for growing a sustainable, blue economy for NZ. The strengths of an observing system for a relatively small nation come from direct connections between the science impetus through to users and stakeholders of an NZ-OOS. The community will leverage off existing ocean observations to optimize effort and resources in a system that has historically made limited investment in ocean observing. The goal of the community paper will be achieved by bringing together oceanographers, data scientists and marine stakeholders to develop an NZ-OOS that provides best knowledge and tools to the sectors of society that use or are influenced by the ocean.

text: Developing an integrated ocean observing system for New Zealand New Zealand (NZ) is an island nation with stewardship of an ocean twenty times larger than its land area. While the challenges facing NZ's ocean are similar to other maritime countries, no coherent national plan exists that meets the needs of scientists, stakeholders or kaitiakitanga (guardianship) of NZ's ocean in a changing climate. The NZ marine science community used the OceanObs'19 white paper to establish a framework and implementation plan for a collaborative NZ ocean observing system (NZ-OOS). Co-production of ocean knowledge with Māori will be embedded in this national strategy for growing a sustainable, blue economy for NZ. The strengths of an observing system for a relatively small nation come from direct connections between the science impetus through to users and stakeholders of an NZ-OOS. The community will leverage off existing ocean observations to optimize effort and resources in a system that has historically made limited investment in ocean observing. The goal of the community paper will be achieved by bringing together oceanographers, data scientists and marine stakeholders to develop an NZ-OOS that provides best knowledge and tools to the sectors of society that use or are influenced by the ocean.

year: 2019-03-26