Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis

Deep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide fast and accurate medical diagnoses. However, the high complexity makes deep neural networks and their predictions difficult to understand. Providing model explanations can be a way of increasing the understanding of “black box” models and building trust. In this work, we applied transfer learning to develop a deep neural network to predict sex from electrocardiograms. Using the visual explanation method Grad-CAM, heat maps were generated from the model in order to understand how it makes predictions. To evaluate the usefulness of the heat maps and determine if the heat maps identified electrocardiogram features that could be recognized to discriminate sex, medical doctors provided feedback. Based on the feedback, we concluded that, in our setting, this mode of explainable artificial intelligence does not provide meaningful information to medical doctors and is not useful in the clinic. Our results indicate that improved explanation techniques that are tailored to medical data should be developed before deep neural networks can be applied in the clinic for diagnostic purposes

Contacts With the Health Care System Before Out-of-Hospital Cardiac Arrest

BACKGROUND: It remains challenging to identify patients at risk of out‐of‐hospital cardiac arrest (OHCA). We aimed to examine health care contacts in patients before OHCA compared with the general population that did not experience an OHCA. METHODS AND RESULTS: Patients with OHCA with a presumed cardiac cause were identified from the Danish Cardiac Arrest Registry (2001–2014) and their health care contacts (general practitioner [GP]/hospital) were examined up to 1 year before OHCA. In a case‐control study (1:9), OHCA contacts were compared with an age‐ and sex‐matched background population. Separately, patients with OHCA were examined by the contact type (GP/hospital/both/no contact) within 2 weeks before OHCA. We included 28 955 patients with OHCA. The weekly percentages of patient contacts with GP the year before OHCA were constant (25%) until 1 week before OHCA when they markedly increased (42%). Weekly percentages of patient contacts with hospitals the year before OHCA gradually increased during the last 6 months (3.5%–6.6%), peaking at the second week (6.8%) before OHCA; mostly attributable to cardiovascular diseases (21%). In comparison, there were fewer weekly contacts among controls with 13% for GP and 2% for hospital contacts (P<0.001). Within 2 weeks before OHCA, 57.8% of patients with OHCA had a health care contact, and these patients had more contacts with GP (odds ratio [OR], 3.17; 95% CI, 3.09–3.26) and hospital (OR, 2.32; 95% CI, 2.21–2.43) compared with controls. CONCLUSIONS: The health care contacts of patients with OHCA nearly doubled leading up to the OHCA event, with more than half of patients having health care contacts within 2 weeks before arrest. This could have implications for future preventive strategies

Prodromal complaints and 30-day survival after emergency medical services-witnessed out-of-hospital cardiac arrest

BACKGROUND: Out-of-hospital cardiac arrest (OHCA) is a frequent and lethal condition with a yearly incidence of approximately 5000 in Denmark. Thirty-day survival is associated with the patient's prodromal complaints prior to cardiac arrest. This paper examines the odds of 30-day survival dependent on the reported prodromal complaints among OHCAs witnessed by the emergency medical services (EMS).METHODS: EMS-witnessed OHCAs in the Capital Region of Denmark from 2016-2018 were included. Calls to the emergency number 1-1-2 and the medical helpline for out-of-hours were analyzed according to the Danish Index; data regarding the OHCA was collected from the Danish Cardiac Arrest Registry. We performed multiple logistic regression to calculate the odds ratio (OR) of 30-day survival with adjustment for sex and age.RESULTS: We identified 311 eligible OHCAs of which 79 (25.4%) survived. The most commonly reported complaints were dyspnea (n = 209, OR 0.79 [95% CI 0.46: 1.36]) and 'feeling generally unwell' (n = 185, OR 1.07 [95% CI 0.63: 1.81]). Chest pain (OR 9.16 [95% CI 5.09:16.9]) and heart palpitations (OR 3.15 [95% CI 1.07:9.46]) had the highest ORs, indicating favorable odds for 30-day survival, while unresponsiveness (OR 0.22 [95% CI 0.11:0.43]) and blue skin or lips (OR 0.30, 95% CI 0.09, 0.81) had the lowest, indicating lesser odds of 30-day survival.CONCLUSION: Experiencing chest pain or heart palpitations prior to EMS-witnessed OHCA was associated with higher 30-day survival. Conversely, complaints of unresponsiveness or having blue skin or lips implied reduced odds of 30-day survival.</p

Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis.

Peer reviewed: TrueDeep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide fast and accurate medical diagnoses. However, the high complexity makes deep neural networks and their predictions difficult to understand. Providing model explanations can be a way of increasing the understanding of "black box" models and building trust. In this work, we applied transfer learning to develop a deep neural network to predict sex from electrocardiograms. Using the visual explanation method Grad-CAM, heat maps were generated from the model in order to understand how it makes predictions. To evaluate the usefulness of the heat maps and determine if the heat maps identified electrocardiogram features that could be recognized to discriminate sex, medical doctors provided feedback. Based on the feedback, we concluded that, in our setting, this mode of explainable artificial intelligence does not provide meaningful information to medical doctors and is not useful in the clinic. Our results indicate that improved explanation techniques that are tailored to medical data should be developed before deep neural networks can be applied in the clinic for diagnostic purposes

Symptom presentation of SARS-CoV-2-positive and negative patients: a nested case–control study among patients calling the emergency medical service and medical helpline

Objective Currently effective symptom-based screening of patients suspected of COVID-19 is limited. We aimed to investigate age-related differences in symptom presentations of patients tested positive and negative for SARS-CoV-2.Design Setting Calls to the medical helpline (1-8-1-3) and emergency number (1-1-2) in Copenhagen, Denmark. At both medical services all calls are recorded.Participants We included calls for patients who called for help/guidance at the medical helpline or emergency number prior to receiving a test for SARS-CoV-2 between April 1st and 20th 2020 (8423 patients). Among these calls, we randomly sampled recorded calls from 350 patients who later tested positive and 250 patients tested negative and registered symptoms described in the call.Outcome Results After exclusions, 544 calls (312 SARS-CoV-2 positive and 232 negative) were included in the analysis. Fever and cough remained the two most common of COVID-19 symptoms across all age groups and approximately 42% of SARS-CoV-2 positive and 20% of negative presented with both fever and cough. Symptoms including nasal congestion, irritation/pain in throat, muscle/joint pain, loss of taste and smell, and headache were common symptoms of COVID-19 for patients younger than 60 years; whereas loss of appetite and feeling unwell were more commonly seen among patients over 60 years. Headache and loss of taste and smell were rare symptoms of COVID-19 among patients over 60 years.Conclusion Our study identified age-related differences in symptom presentations of SARS-CoV-2-positive patients calling for help or medical advice. The specific symptoms of loss of smell or taste almost exclusively reported by patients younger than 60 years. Differences in symptom presentation across age groups must be considered when screening for COVID-19