Sobia Amjad - Academia.edu (original) (raw)

Papers by Sobia Amjad

Critical Care

Background Natural language processing (NLP) may help evaluate the characteristics, prevalence, t... more Background Natural language processing (NLP) may help evaluate the characteristics, prevalence, trajectory, treatment, and outcomes of behavioural disturbance phenotypes in critically ill patients. Methods We obtained electronic clinical notes, demographic information, outcomes, and treatment data from three medical-surgical ICUs. Using NLP, we screened for behavioural disturbance phenotypes based on words suggestive of an agitated state, a non-agitated state, or a combination of both. Results We studied 2931 patients. Of these, 225 (7.7%) were NLP-Dx-BD positive for the agitated phenotype, 544 (18.6%) for the non-agitated phenotype and 667 (22.7%) for the combined phenotype. Patients with these phenotypes carried multiple clinical baseline differences. On time-dependent multivariable analysis to compensate for immortal time bias and after adjustment for key outcome predictors, agitated phenotype patients were more likely to receive antipsychotic medications (odds ratio [OR] 1.84, 1...

Critical Care and Resuscitation, 2021

Background: There is no gold standard approach for delirium diagnosis, making the assessment of i... more Background: There is no gold standard approach for delirium diagnosis, making the assessment of its epidemiology difficult. Delirium can only be inferred though observation of behavioural disturbance and described with relevant nouns or adjectives. Objective: We aimed to use natural language processing (NLP) and its identification of words descriptive of behavioural disturbance to study the epidemiology of delirium in critically ill patients. Study design: Retrospective study using data collected from the electronic health records of a university-affiliated intensive care unit (ICU) in Melbourne, Australia. Participants: 12 375 patients Intervention: Analysis of electronic progress notes. Identification using NLP of at least one of a list of words describing behavioural disturbance within such notes. Results: We analysed 199 648 progress notes in 12 375 patients. Of these, 5108 patients (41.3%) had NLP-diagnosed behavioural disturbance (NLP-Dx-BD). Compared with those who did not ha...

Research in Computing Science, 2013

Observing the role of traditional medicines in global healthcare, World Health Organization signi... more Observing the role of traditional medicines in global healthcare, World Health Organization signifies the need to preserve knowledge of this valuable intellectual property which is being lost or inaccessible, as it is either undocumented or local in context. Unani medicines, a 2500 years old system, has been practiced in Asia, is facing the same situation. Little computerization effort has been done so far earlier in this domain. To preserve the knowledge of Unani medicines, a formal semantic structure is required that is machine readable and reusable. This paper defines a conceptual structure of Unani medicines using upper domain ontology that includes core philosophy, diseases, diagnosis, symptoms, drugs, and treatment of patients. Developed in Protégé, designers have no past experience in ontology development; information collected from books and expert interviews. The proposed ontology serves as a backbone in upcoming knowledge management framework of Unani medicines.

Internal Medicine Journal, 2019

Background: Delirium is common in hospitalised patients but its epidemiology remains poorly chara... more Background: Delirium is common in hospitalised patients but its epidemiology remains poorly characterised. Aims: To test the hypothesis that patient demographics, clinical phenotype, management and outcomes of patient with delirium in hospital ward patients differ from intensive care unit (ICU) patients. Methods: Retrospective cohort of patients admitted to an Australian universityaffiliated hospital between March 2013 and April 2017 and coded for delirium at discharge using the International Classification of Diseases System, 10th revision, criteria. Results: Among 61 032 hospitalised patients, 2864 (4.7%) were coded for delirium. From these, we studied a random sample of 100 ward patients and 100 ICU patients. Ward patients were older (median age: 84 vs 65 years; P < 0.0001), more likely to have dementia (38% vs 2% for ICU patients; P < 0.0001) and less likely to have had surgery (24% vs 62%; P < 0.0001). Of ward patients, 74% had hypoactive delirium, while 64% of ICU patients had agitated delirium (P < 0.0001). Persistent delirium at hospital discharge was more common among ward patients (66% vs 17%, P < 0.0001). On multivariable analysis, age and dementia predicted persistent delirium, while surgery predicted recovery. Conclusions: Delirium in ward patients is profoundly different from delirium in ICU patients. It has a dominant hypoactive clinical phenotype, is preceded by dementia and is less likely to recover at hospital discharge. Therefore, delirium prevention, detection and goals of care should be adapted to the environment in which it occurs.

Lecture Notes in Computer Science, 2014

Traditional medicines can play a major role in global health care, due to its indigenous nature, ... more Traditional medicines can play a major role in global health care, due to its indigenous nature, easy access, and cost effectiveness. However, knowledge of this intellectual property is in danger of being lost. It is either undocumented or if documented, it is inaccessible and local in context. World Health Organization signifies the necessity to preserve and maintain this knowledge. Unani medicines, a subfield of traditional medicines, have been continuously practiced in Asia for about 2500 years, and it is facing the same situation of knowledge lost. To preserve knowledge of Unani medicines, initial kind of an effort has been done but a formal semantic structure, that is machine readable and reusable, is required to preserve this knowledge efficiently and effectively. This research focuses on conceptual structure of Unani medicines by presenting domain ontology which includes core principles and philosophy of Unani medicines, diseases, symptoms, diagnosis, drugs, and treatment. Knowledge about fundamentals is captured from expert interviews and books and then this knowledge is converted into ontologies using Protege. Although it is not exhaustive domain ontology, however it may serve as a starting point for any knowledge based application of Unani medicines. In this research a semantic queries based case study along with a prototype expert system is also proposed.

Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine, 2019

OBJECTIVE To develop a library of delirium-suggestive words. DESIGN Cross-sectional survey. SETTI... more OBJECTIVE To develop a library of delirium-suggestive words. DESIGN Cross-sectional survey. SETTING Single tertiary referral hospital. PARTICIPANTS Medical, nursing and allied health staff and medical coders. MAIN OUTCOME MEASURES Frequency of graded response on a 5-point Likert scale to individual delirium-suggestive words. RESULTS Two-hundred and three complete responses were received from 227 survey respondents; the majority were medical and nursing staff (42.4% and 43.8% respectively), followed by allied health practitioners and medical coders (10.3% and 3.4%). Words that were "very likely" to suggest delirium were "confused/ confusion", "delirious", "disoriented/disorientation" and "fluctuating conscious state". Differences in word selection were noted based on occupational background, prior knowledge of delirium, and experience in caring for intensive care unit patients. Distractor words included in the survey were rated as &qu...

Intensive Care Medicine, 2022

Purpose: To compare the prevalence, characteristics, drug treatment for delirium, and outcomes of... more Purpose: To compare the prevalence, characteristics, drug treatment for delirium, and outcomes of patients with Natural Language Processing (NLP) diagnosed behavioral disturbance (NLP-Dx-BD) vs Confusion Assessment Method for intensive care unit (CAM-ICU) positivity. Methods: In three combined medical-surgical ICUs, we obtained data on demographics, treatment with antipsychotic medications, and outcomes. We applied NLP to caregiver progress notes to diagnose behavioral disturbance and analyzed simultaneous CAM-ICU. Results: We assessed 2313 patients with a median lowest Richmond Agitation-Sedation Scale (RASS) score of − 2 (− 4.0 to − 1.0) and median highest RASS score of 1 (0 to 1). Overall, 1246 (53.9%) patients were NLP-Dx-BD positive (NLP-Dx-BD pos) and 578 (25%) were CAM-ICU positive (CAM-ICU pos). Among NLP-Dx-BD pos patients, 539 (43.3%) were also CAM-ICU pos. In contrast, among CAM-ICU pos patients, 539 (93.3%) were also NLP-Dx-BD pos. The use of antipsychotic medications was highest in patients in the CAM-ICU pos and NLP-Dx-BD pos group (24.3%) followed by the CAM-ICU neg and NLP-Dx-BD pos group (10.5%). In NLP-Dx-BD neg patients, antipsychotic medication use was lower at 5.1% for CAM-ICU pos and NLP-Dx-BD neg patients and 2.3% for CAM-ICU neg and NLP-Dx-BD neg patients (overall P < 0.001). Regardless of CAM-ICU status, after adjustment and on time-dependent Cox modelling, NLP-Dx-BD was associated with greater antipsychotic medication use. Finally, regardless of CAM-ICU status, NLP-Dx-BD pos patients had longer duration of ICU and hospital stay and greater hospital mortality (all P < 0.001). Conclusion: More patients were NLP-Dx-BD positive than CAM-ICU positive. NLP-Dx-BD and CAM-ICU assessment describe partly overlapping populations. However, NLP-Dx-BD identifies more patients likely to receive antipsychotic medications. In the absence of NLP-Dx-BD, treatment with antipsychotic medications is rare.

Asia-Pacific Journal of Clinical Oncology

We aimed to test the performance of the quick Sequential Organ Failure Assessment score (qSOFA) i... more We aimed to test the performance of the quick Sequential Organ Failure Assessment score (qSOFA) in predicting the outcomes of oncology patients admitted to the emergency department (ED) with suspected infection.