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Article ; Online: A retrospective study on machine learning-assisted stroke recognition for medical helpline calls.

Wenstrup, Jonathan / Havtorn, Jakob Drachmann / Borgholt, Lasse / Blomberg, Stig Nikolaj / Maaloe, Lars / Sayre, Michael R / Christensen, Hanne / Kruuse, Christina / Christensen, Helle Collatz

NPJ digital medicine

2023 Volume 6, Issue 1, Page(s) 235

Abstract: Advanced stroke treatment is time-dependent and, therefore, relies on recognition by call-takers at prehospital telehealth services to ensure fast hospitalisation. This study aims to develop and assess the potential of machine learning in improving ... ...

Abstract	Advanced stroke treatment is time-dependent and, therefore, relies on recognition by call-takers at prehospital telehealth services to ensure fast hospitalisation. This study aims to develop and assess the potential of machine learning in improving prehospital stroke recognition during medical helpline calls. We used calls from 1 January 2015 to 31 December 2020 in Copenhagen to develop a machine learning-based classification pipeline. Calls from 2021 are used for testing. Calls are first transcribed using an automatic speech recognition model and then categorised as stroke or non-stroke using a text classification model. Call-takers achieve a sensitivity of 52.7% (95% confidence interval 49.2-56.4%) with a positive predictive value (PPV) of 17.1% (15.5-18.6%). The machine learning framework performs significantly better (p < 0.0001) with a sensitivity of 63.0% (62.0-64.1%) and a PPV of 24.9% (24.3-25.5%). Thus, a machine learning framework for recognising stroke in prehospital medical helpline calls may become a supportive tool for call-takers, aiding in early and accurate stroke recognition.
Language	English
Publishing date	2023-12-19
Publishing country	England
Document type	Journal Article
ISSN	2398-6352
ISSN (online)	2398-6352
DOI	10.1038/s41746-023-00980-y
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Reply letter to "Machine learning as a supportive tool to recognize cardiac arrest in emergency calls".

Blomberg, Stig Nikolaj / Folke, Fredrik / Ersbøll, Annette Kjær / Christensen, Helle Collatz / Torp-Pedersen, Christian / Sayre, Michael R / Counts, Catherine R / Lippert, Freddy K

Resuscitation

2019 Volume 144, Page(s) 205–206

MeSH term(s)	Emergencies ; Emergency Service, Hospital ; Heart Arrest ; Humans ; Machine Learning
Language	English
Publishing date	2019-09-23
Publishing country	Ireland
Document type	Letter ; Comment
ZDB-ID	189901-6
ISSN	1873-1570 ; 0300-9572
ISSN (online)	1873-1570
ISSN	0300-9572
DOI	10.1016/j.resuscitation.2019.09.013
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Article ; Online: Effect of Machine Learning on Dispatcher Recognition of Out-of-Hospital Cardiac Arrest During Calls to Emergency Medical Services: A Randomized Clinical Trial.

Blomberg, Stig Nikolaj / Christensen, Helle Collatz / Lippert, Freddy / Ersbøll, Annette Kjær / Torp-Petersen, Christian / Sayre, Michael R / Kudenchuk, Peter J / Folke, Fredrik

JAMA network open

2021 Volume 4, Issue 1, Page(s) e2032320

Abstract: Importance: Emergency medical dispatchers fail to identify approximately 25% of cases of out-of-hospital cardiac arrest (OHCA), resulting in lost opportunities to save lives by initiating cardiopulmonary resuscitation.: Objective: To examine how a ... ...

Abstract	Importance: Emergency medical dispatchers fail to identify approximately 25% of cases of out-of-hospital cardiac arrest (OHCA), resulting in lost opportunities to save lives by initiating cardiopulmonary resuscitation. Objective: To examine how a machine learning model trained to identify OHCA and alert dispatchers during emergency calls affected OHCA recognition and response. Design, setting, and participants: This double-masked, 2-group, randomized clinical trial analyzed all calls to emergency number 112 (equivalent to 911) in Denmark. Calls were processed by a machine learning model using speech recognition software. The machine learning model assessed ongoing calls, and calls in which the model identified OHCA were randomized. The trial was performed at Copenhagen Emergency Medical Services, Denmark, between September 1, 2018, and December 31, 2019. Intervention: Dispatchers in the intervention group were alerted when the machine learning model identified out-of-hospital cardiac arrest, and those in the control group followed normal protocols without alert. Main outcomes and measures: The primary end point was the rate of dispatcher recognition of subsequently confirmed OHCA. Results: A total of 169 049 emergency calls were examined, of which the machine learning model identified 5242 as suspected OHCA. Calls were randomized to control (2661 [50.8%]) or intervention (2581 [49.2%]) groups. Of these, 336 (12.6%) and 318 (12.3%), respectively, had confirmed OHCA. The mean (SD) age among of these 654 patients was 70 (16.1) years, and 419 of 627 patients (67.8%) with known gender were men. Dispatchers in the intervention group recognized 296 confirmed OHCA cases (93.1%) with machine learning assistance compared with 304 confirmed OHCA cases (90.5%) using standard protocols without machine learning assistance (P = .15). Machine learning alerts alone had a significantly higher sensitivity than dispatchers without alerts for confirmed OHCA (85.0% vs 77.5%; P < .001) but lower specificity (97.4% vs 99.6%; P < .001) and positive predictive value (17.8% vs 55.8%; P < .001). Conclusions and relevance: This randomized clinical trial did not find any significant improvement in dispatchers' ability to recognize cardiac arrest when supported by machine learning even though artificial intelligence did surpass human recognition. Trial registration: ClinicalTrials.gov Identifier: NCT04219306.
MeSH term(s)	Aged ; Denmark ; Double-Blind Method ; Emergency Medical Dispatch ; Female ; Humans ; Machine Learning ; Male ; Out-of-Hospital Cardiac Arrest/diagnosis
Language	English
Publishing date	2021-01-04
Publishing country	United States
Document type	Journal Article ; Randomized Controlled Trial
ISSN	2574-3805
ISSN (online)	2574-3805
DOI	10.1001/jamanetworkopen.2020.32320
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Article ; Online: PDZ Domains as Drug Targets.

Christensen, Nikolaj R / Čalyševa, Jelena / Fernandes, Eduardo F A / Lüchow, Susanne / Clemmensen, Louise S / Haugaard-Kedström, Linda M / Strømgaard, Kristian

Advanced therapeutics

2019 Volume 2, Issue 7, Page(s) 1800143

Abstract: Protein-protein interactions within protein networks shape the human interactome, which often is promoted by specialized protein interaction modules, such as the postsynaptic density-95 (PSD-95), discs-large, zona occludens 1 (ZO-1) (PDZ) domains. PDZ ... ...

Abstract	Protein-protein interactions within protein networks shape the human interactome, which often is promoted by specialized protein interaction modules, such as the postsynaptic density-95 (PSD-95), discs-large, zona occludens 1 (ZO-1) (PDZ) domains. PDZ domains play a role in several cellular functions, from cell-cell communication and polarization, to regulation of protein transport and protein metabolism. PDZ domain proteins are also crucial in the formation and stability of protein complexes, establishing an important bridge between extracellular stimuli detected by transmembrane receptors and intracellular responses. PDZ domains have been suggested as promising drug targets in several diseases, ranging from neurological and oncological disorders to viral infections. In this review, the authors describe structural and genetic aspects of PDZ-containing proteins and discuss the current status of the development of small-molecule and peptide modulators of PDZ domains. An overview of potential new therapeutic interventions in PDZ-mediated protein networks is also provided.
Keywords	covid19
Language	English
Publishing date	2019-04-24
Publishing country	Germany
Document type	Journal Article ; Review
ISSN	2366-3987
ISSN (online)	2366-3987
DOI	10.1002/adtp.201800143
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Article ; Online: Machine learning as a supportive tool to recognize cardiac arrest in emergency calls.

Blomberg, Stig Nikolaj / Folke, Fredrik / Ersbøll, Annette Kjær / Christensen, Helle Collatz / Torp-Pedersen, Christian / Sayre, Michael R / Counts, Catherine R / Lippert, Freddy K

Resuscitation

2019 Volume 138, Page(s) 322–329

Abstract: Background: Emergency medical dispatchers fail to identify approximately 25% of cases of out of hospital cardiac arrest, thus lose the opportunity to provide the caller instructions in cardiopulmonary resuscitation. We examined whether a machine ... ...

Abstract	Background: Emergency medical dispatchers fail to identify approximately 25% of cases of out of hospital cardiac arrest, thus lose the opportunity to provide the caller instructions in cardiopulmonary resuscitation. We examined whether a machine learning framework could recognize out-of-hospital cardiac arrest from audio files of calls to the emergency medical dispatch center. Methods: For all incidents responded to by Emergency Medical Dispatch Center Copenhagen in 2014, the associated call was retrieved. A machine learning framework was trained to recognize cardiac arrest from the recorded calls. Sensitivity, specificity, and positive predictive value for recognizing out-of-hospital cardiac arrest were calculated. The performance of the machine learning framework was compared to the actual recognition and time-to-recognition of cardiac arrest by medical dispatchers. Results: We examined 108,607 emergency calls, of which 918 (0.8%) were out-of-hospital cardiac arrest calls eligible for analysis. Compared with medical dispatchers, the machine learning framework had a significantly higher sensitivity (72.5% vs. 84.1%, p < 0.001) with lower specificity (98.8% vs. 97.3%, p < 0.001). The machine learning framework had a lower positive predictive value than dispatchers (20.9% vs. 33.0%, p < 0.001). Time-to-recognition was significantly shorter for the machine learning framework compared to the dispatchers (median 44 seconds vs. 54 s, p < 0.001). Conclusions: A machine learning framework performed better than emergency medical dispatchers for identifying out-of-hospital cardiac arrest in emergency phone calls. Machine learning may play an important role as a decision support tool for emergency medical dispatchers.
MeSH term(s)	Aged ; Aged, 80 and over ; Cardiopulmonary Resuscitation/methods ; Emergency Medical Dispatch/organization & administration ; Emergency Medical Service Communication Systems ; Emergency Medical Services/methods ; Female ; Humans ; Machine Learning ; Male ; Middle Aged ; Out-of-Hospital Cardiac Arrest/diagnosis ; Out-of-Hospital Cardiac Arrest/therapy ; ROC Curve ; Retrospective Studies ; Time Factors
Language	English
Publishing date	2019-01-18
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	189901-6
ISSN	1873-1570 ; 0300-9572
ISSN (online)	1873-1570
ISSN	0300-9572
DOI	10.1016/j.resuscitation.2019.01.015
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Article ; Online: Coding variants identified in patients with diabetes alter PICK1 BAR domain function in insulin granule biogenesis.

The Journal of clinical investigation

2022 Volume 132, Issue 5

Abstract: Bin/amphiphysin/Rvs (BAR) domains are positively charged crescent-shaped modules that mediate curvature of negatively charged lipid membranes during remodeling processes. The BAR domain proteins PICK1, ICA69, and the arfaptins have recently been ... ...

Abstract	Bin/amphiphysin/Rvs (BAR) domains are positively charged crescent-shaped modules that mediate curvature of negatively charged lipid membranes during remodeling processes. The BAR domain proteins PICK1, ICA69, and the arfaptins have recently been demonstrated to coordinate the budding and formation of immature secretory granules (ISGs) at the trans-Golgi network. Here, we identify 4 coding variants in the PICK1 gene from a whole-exome screening of Danish patients with diabetes that each involve a change in positively charged residues in the PICK1 BAR domain. All 4 coding variants failed to rescue insulin content in INS-1E cells upon knock down of endogenous PICK1. Moreover, 2 variants showed dominant-negative properties. In vitro assays addressing BAR domain function suggested that the coding variants compromised BAR domain function but increased the capacity to cause fission of liposomes. Live confocal microscopy and super-resolution microscopy further revealed that PICK1 resides transiently on ISGs before egress via vesicular budding events. Interestingly, this egress of PICK1 was accelerated in the coding variants. We propose that PICK1 assists in or complements the removal of excess membrane and generic membrane trafficking proteins, and possibly also insulin, from ISGs during the maturation process; and that the coding variants may cause premature budding, possibly explaining their dominant-negative function.
MeSH term(s)	Adaptor Proteins, Signal Transducing/metabolism ; Carrier Proteins/genetics ; Cell Membrane/metabolism ; Diabetes Mellitus/genetics ; Diabetes Mellitus/metabolism ; Humans ; Insulin/genetics ; Insulin/metabolism ; Nerve Tissue Proteins ; Nuclear Proteins/metabolism ; Protein Binding
Chemical Substances	Adaptor Proteins, Signal Transducing ; Carrier Proteins ; Insulin ; Nerve Tissue Proteins ; Nuclear Proteins ; PICk1 protein, human ; amphiphysin (147954-52-7)
Language	English
Publishing date	2022-03-08
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	3067-3
ISSN	1558-8238 ; 0021-9738
ISSN (online)	1558-8238
ISSN	0021-9738
DOI	10.1172/JCI144904
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Article ; Online: Physical constraints and functional plasticity of cellulases

Jeppe Kari / Gustavo A. Molina / Kay S. Schaller / Corinna Schiano-di-Cola / Stefan J. Christensen / Silke F. Badino / Trine H. Sørensen / Nanna S. Røjel / Malene B. Keller / Nanna Rolsted Sørensen / Bartlomiej Kolaczkowski / Johan P. Olsen / Kristian B. R. M. Krogh / Kenneth Jensen / Ana M. Cavaleiro / Günther H. J. Peters / Nikolaj Spodsberg / Kim Borch / Peter Westh

Nature Communications, Vol 12, Iss 1, Pp 1-

2021 Volume 10

Abstract: Enzyme reactions at interfaces are common in both Nature and industrial applications but no general kinetic framework exists for interfacial enzymes. Here, the authors kinetically characterize 83 cellulases and identify a scaling relationship between ... ...

Abstract	Enzyme reactions at interfaces are common in both Nature and industrial applications but no general kinetic framework exists for interfacial enzymes. Here, the authors kinetically characterize 83 cellulases and identify a scaling relationship between ligand binding strength and maximal turnover, a so-called linear free energy relationship, which may help rationalize cellulolytic mechanisms and guide the selection of technical enzymes.
Keywords	Science ; Q
Language	English
Publishing date	2021-06-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
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Article ; Online: Physical constraints and functional plasticity of cellulases.

Kari, Jeppe / Molina, Gustavo A / Schaller, Kay S / Schiano-di-Cola, Corinna / Christensen, Stefan J / Badino, Silke F / Sørensen, Trine H / Røjel, Nanna S / Keller, Malene B / Sørensen, Nanna Rolsted / Kolaczkowski, Bartlomiej / Olsen, Johan P / Krogh, Kristian B R M / Jensen, Kenneth / Cavaleiro, Ana M / Peters, Günther H J / Spodsberg, Nikolaj / Borch, Kim / Westh, Peter

Nature communications

2021 Volume 12, Issue 1, Page(s) 3847

Abstract: Enzyme reactions, both in Nature and technical applications, commonly occur at the interface of immiscible phases. Nevertheless, stringent descriptions of interfacial enzyme catalysis remain sparse, and this is partly due to a shortage of coherent ... ...

Abstract	Enzyme reactions, both in Nature and technical applications, commonly occur at the interface of immiscible phases. Nevertheless, stringent descriptions of interfacial enzyme catalysis remain sparse, and this is partly due to a shortage of coherent experimental data to guide and assess such work. In this work, we produced and kinetically characterized 83 cellulases, which revealed a conspicuous linear free energy relationship (LFER) between the substrate binding strength and the activation barrier. The scaling occurred despite the investigated enzymes being structurally and mechanistically diverse. We suggest that the scaling reflects basic physical restrictions of the hydrolytic process and that evolutionary selection has condensed cellulase phenotypes near the line. One consequence of the LFER is that the activity of a cellulase can be estimated from its substrate binding strength, irrespectively of structural and mechanistic details, and this appears promising for in silico selection and design within this industrially important group of enzymes.
MeSH term(s)	Algorithms ; Biocatalysis ; Cellulases/chemistry ; Cellulases/metabolism ; Cellulose/metabolism ; Hydrolysis ; Kinetics ; Molecular Dynamics Simulation ; Protein Binding ; Protein Domains ; Substrate Specificity
Chemical Substances	Cellulose (9004-34-6) ; Cellulases (EC 3.2.1.-)
Language	English
Publishing date	2021-06-22
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-021-24075-y
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Article: The Danish National Child Health Register.

Andersen, Mikkel Porsborg / Wiingreen, Rikke / Eroglu, Talip E / Christensen, Helle Collatz / Polcwiartek, Laura Bech / Blomberg, Stig Nikolaj Fasmer / Kragholm, Kristian / Torp-Pedersen, Christian / Sørensen, Kathrine Kold

Clinical epidemiology

2023 Volume 15, Page(s) 1087–1094

Abstract: Aim of the database: The aim of the National Child Health Registry is to provide comprehensive insight into children's health and growth on a national scale by continuously monitoring the health status of Danish children. Through this effort, the ... ...

Abstract	Aim of the database: The aim of the National Child Health Registry is to provide comprehensive insight into children's health and growth on a national scale by continuously monitoring the health status of Danish children. Through this effort, the registry assists the health authorities in prioritizing preventive efforts to promote better child health outcomes. Study population: The registry includes all Danish children, however, incomplete coverage persists. Main variables: The National Child Health Registry contains information on exposure to secondhand smoking, breastfeeding duration, and anthropometric measurements through childhood. The information in the registry is divided into three datasets: Smoking, Breastfeeding, and Measurements. Beside specific information on the three topics, all datasets include information on CPR-number, date of birth, sex, municipality, and region of residence. Database status: The National Child Health Registry was established in 2009 and contains health information on children from all Danish municipalities, collected through routinely performed health examinations conducted by general practitioners and health nurses. Conclusion: The National Child Health Register is an asset to epidemiological and health research with nationwide information on children's health and growth in Denmark. Due to the unique Danish Civil Registration System, it is possible to link data from the National Child Health Register to information from several other national health and social registers which enables longitudinal unambiguous follow-up.
Language	English
Publishing date	2023-11-14
Publishing country	New Zealand
Document type	Journal Article
ZDB-ID	2494772-6
ISSN	1179-1349
ISSN	1179-1349
DOI	10.2147/CLEP.S423587
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Article ; Online: A machine-learning framework for robust and reliable prediction of short- and long-term treatment response in initially antipsychotic-naïve schizophrenia patients based on multimodal neuropsychiatric data.

Ambrosen, Karen S / Skjerbæk, Martin W / Foldager, Jonathan / Axelsen, Martin C / Bak, Nikolaj / Arvastson, Lars / Christensen, Søren R / Johansen, Louise B / Raghava, Jayachandra M / Oranje, Bob / Rostrup, Egill / Nielsen, Mette Ø / Osler, Merete / Fagerlund, Birgitte / Pantelis, Christos / Kinon, Bruce J / Glenthøj, Birte Y / Hansen, Lars K / Ebdrup, Bjørn H

Translational psychiatry

2020 Volume 10, Issue 1, Page(s) 276

Abstract: The reproducibility of machine-learning analyses in computational psychiatry is a growing concern. In a multimodal neuropsychiatric dataset of antipsychotic-naïve, first-episode schizophrenia patients, we discuss a workflow aimed at reducing bias and ... ...

Abstract	The reproducibility of machine-learning analyses in computational psychiatry is a growing concern. In a multimodal neuropsychiatric dataset of antipsychotic-naïve, first-episode schizophrenia patients, we discuss a workflow aimed at reducing bias and overfitting by invoking simulated data in the design process and analysis in two independent machine-learning approaches, one based on a single algorithm and the other incorporating an ensemble of algorithms. We aimed to (1) classify patients from controls to establish the framework, (2) predict short- and long-term treatment response, and (3) validate the methodological framework. We included 138 antipsychotic-naïve, first-episode schizophrenia patients with data on psychopathology, cognition, electrophysiology, and structural magnetic resonance imaging (MRI). Perinatal data and long-term outcome measures were obtained from Danish registers. Short-term treatment response was defined as change in Positive And Negative Syndrome Score (PANSS) after the initial antipsychotic treatment period. Baseline diagnostic classification algorithms also included data from 151 matched controls. Both approaches significantly classified patients from healthy controls with a balanced accuracy of 63.8% and 64.2%, respectively. Post-hoc analyses showed that the classification primarily was driven by the cognitive data. Neither approach predicted short- nor long-term treatment response. Validation of the framework showed that choice of algorithm and parameter settings in the real data was successfully guided by results from the simulated data. In conclusion, this novel approach holds promise as an important step to minimize bias and obtain reliable results with modest sample sizes when independent replication samples are not available.
MeSH term(s)	Antipsychotic Agents/therapeutic use ; Humans ; Machine Learning ; Magnetic Resonance Imaging ; Reproducibility of Results ; Schizophrenia/drug therapy ; Schizophrenic Psychology
Chemical Substances	Antipsychotic Agents
Language	English
Publishing date	2020-08-10
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2609311-X
ISSN	2158-3188 ; 2158-3188
ISSN (online)	2158-3188
ISSN	2158-3188
DOI	10.1038/s41398-020-00962-8
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