Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review.

BACKGROUND: Natural Language Processing (NLP) is widely used to extract clinical insights from Electronic Health Records (EHRs). However, the lack of annotated data, automated tools, and other challenges hinder the full utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL) and NLP techniques are studied and compared to understand the limitations and opportunities in this space comprehensively. METHODOLOGY: After screening 261 articles from 11 databases, we included 127 papers for full-text review covering seven categories of articles: (1) medical note classification, (2) clinical entity recognition, (3) text summarisation, (4) deep learning (DL) and transfer learning architecture, (5) information extraction, (6) Medical language translation and (7) other NLP applications. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULT AND DISCUSSION: EHR was the most commonly used data type among the selected articles, and the datasets were primarily unstructured. Various ML and DL methods were used, with prediction or classification being the most common application of ML or DL. The most common use cases were: the International Classification of Diseases, Ninth Revision (ICD-9) classification, clinical note analysis, and named entity recognition (NER) for clinical descriptions and research on psychiatric disorders. CONCLUSION: We find that the adopted ML models were not adequately assessed. In addition, the data imbalance problem is quite important, yet we must find techniques to address this underlining problem. Future studies should address key limitations in studies, primarily identifying Lupus Nephritis, Suicide Attempts, perinatal self-harmed and ICD-9 classification.

Automated screening for distress: A perspective for the future.

Distress is a complex condition, which affects a significant percentage of cancer patients and may lead to depression, anxiety, sadness, suicide and other forms of psychological morbidity. Compelling evidence supports screening for distress as a means of facilitating early intervention and subsequent improvements in psychological well-being and overall quality of life. Nevertheless, despite the existence of evidence-based and easily administered screening tools, for example, the Distress Thermometer, routine screening for distress is yet to achieve widespread implementation. Efforts are intensifying to utilise innovative, cost-effective methods now available through emerging technologies in the informatics and computational arenas.

Guiding Ebola patients to suitable health facilities: an SMS-based approach.

Access to appropriate health services is a fundamental problem in developing countries, where patients do not have access to information and to the nearest health service facility. We propose building a recommendation system based on simple SMS text messaging to help Ebola patients readily find the closest health service with available and appropriate resources. The system will map people's reported symptoms to likely Ebola case definitions and suitable health service locations. In addition to providing a valuable individual service to people with curable diseases, the proposed system will also predict population-level disease spread risk for infectious diseases using crowd-sourced symptoms from the population. Health workers will be able to better plan and anticipate responses to the current Ebola outbreak in West Africa. Patients will have improved access to appropriate health care. This system could also be applied in other resource poor or rich settings.

Determination of Activities of Daily Living of independent living older people using environmentally placed sensors.

The rapid increase in the ageing population of most developed countries is presenting significant challenges to policymakers of public healthcare. To address this problem, we propose a Smarter Safer Home solution that enables ageing Australians to live independently longer in their own homes. The primary aim of our approach is to enhance the Quality of Life (QoL) of aged citizens and the Family Quality of Life (FQoL) for the adult children supporting their aged parents. To achieve this, we use environmentally placed sensors for non-intrusive monitoring of human behaviour. The various sensors will detect and gather activity and ambience data which will be fused through specific decision support algorithms to extract Activities of Daily Living (ADLs). Subsequently, these estimated ADLs would be correlated with reported and recorded health events to predicate health decline or critical health situations from the changes in ADLs.