Combining Unsupervised, Supervised, and Rule-based Algorithms for Text Mining of Electronic Health Records - A Clinical Decision Support System for Identifying and Classifying Allergies of Concern for Anesthesia During Surgery

Undisclosed allergic reactions of patients are a major risk when undertaking surgeries in hospitals. We present our early experience and preliminary findings for a Clinical Decision Support System (CDSS) being developed in a Norwegian Hospital Trust. The system incorporates unsupervised and supervised machine learning algorithms in combination with rule-based algorithms to identify and classify allergies of concern for anesthesia during surgery. Our approach is novel in that it utilizes unsupervised machine learning to analyze large corpora of narratives to automatically build a clinical language model containing words and phrases of which meanings and relative meanings are also learnt. It further implements a semi-automatic annotation scheme for efficient and interactive machine-learning, which to a large extent eliminates the substantial manual annotation (of clinical narratives) effort necessary for the training of supervised algorithms. Validation of system performance was performed through comparing allergies identified by the CDSS with a manual reference standard

AI for Situational Awareness in Situations With High Uncertainty: An Explorative Case Study

Often, the police experience scenarios with much uncertainty. These scenarios can be characterized by high time pressure, huge amounts of information, and potentially severe consequences. In this paper, we study whether artificial intelligence (AI) can be a fit for the information processing needs of the police helping them achieve situational awareness and make better decisions. Given the potential severity of police situations, AI can potentially reduce the risk of fatal outcomes and wrong decisions. Investigating this issue with police officers and AI experts as our informants, our findings suggest that our informants are positive to AI as a support tool, but more skeptical to whether AI can make an impact in their daily police work due to the complexity of their work. The importance of implementing AI to suitable tasks is emphasized

DRIVERS AND BARRIERS TO STRUCTURING INFORMATION IN ELECTRONIC HEALTH RECORDS

While much research exists on aspects or phenomena related to or depending on structuring of infor-mation in the healthcare context, most of this has been limited to the study of specific Electronic Health Record (EHR) implementation, or to certain capabilities or functionalities of EHRs such as decision support, the narrative, and clinical classifications and terminology. The phenomenon of in-formation structuring in EHRs per se, has received little research attention. This article presents a review on the subject of information structuring in EHRs. While research shows that increasing struc-turing of health information may be favorable to healthcare, there are also caveats. This paper expos-es and discusses both salient drivers and barriers described by the literature, by examining the phe-nomenon through seven identified themes: clinical decision support; competence; continuity of care; management; secondary uses; patient safety and quality of care; and patient empowerment. Even though increased use of structured health data (depending on context) has the potential to cause ma-jor impacts to healthcare, a middle path represented by the synergistic co-existence of both structured data and unstructured information seems to be the most feasible to follow for healthcare at the time being based on the available literature