Development of medical applications based on AI models and register data – regulatory considerations

Artificial intelligence based methods, especially machine learning (ML), are increasingly used in healthcare for automatic medical image analysis and clinical decision support systems. Development and validation of ML models involve processing of large volumes of personal data. We analysed regulatory impacts on ML based application development especially from the perspective of privacy protection and usage of ML models as a basis for software under medical device regulation (MDR). We present best practices for ML application development and personal data usage in a use case of predicting elderly individuals’ future need for healthcare and social welfare services.publishedVersionPeer reviewe

Predicting Daytime Sleepiness from Electrocardiography Based Respiratory Rate Using Deep Learning

Daytime sleepiness impairs the activities of daily living, especially in chronic disease patients. Typically, daytime sleepiness is measured with subjective patient reported outcomes (PROs), which could be prone to recall bias. Objective measures of daytime sleepiness, which are sensitive to change, would benefit disease state assessment and novel therapies that impact the quality of life. The presented study aimed to predict daytime sleepiness from two hours of continuously measured respiratory rate using a 1-dimensional convolutional neural network. A wearable biosensor was used to continuously measure electrocardiography (ECG) based respiratory rate, while the participants (N=82) were asked to fill in Karolinska Sleepiness Scale three times a day. Considering the need for a sleepiness measure for chronic diseases, neurodegenerative disease (NDD, N=14) patients, immune-mediated inflammatory disease (IMID, N=42) patients, as well as healthy participants (N=26) were included in the study. The diseaseagnostic model achieved an accuracy of 63% between nonsleepy and sleepy states. The result demonstrates the potential of using respiratory rate with deep learning for an objective measure of daytime sleepiness.acceptedVersionPeer reviewe

Transformers for cardiac patient mortality risk prediction from heterogeneous electronic health records

With over 17 million annual deaths, cardiovascular diseases (CVDs) dominate the cause of death statistics. CVDs can deteriorate the quality of life drastically and even cause sudden death, all the while inducing massive healthcare costs. This work studied state-of-the-art deep learning techniques to predict increased risk of death in CVD patients, building on the electronic health records (EHR) of over 23,000 cardiac patients. Taking into account the usefulness of the prediction for chronic disease patients, a prediction period of six months was selected. Two major transformer models that rely on learning bidirectional dependencies in sequential data, BERT and XLNet, were trained and compared. To our knowledge, the presented work is the first to apply XLNet on EHR data to predict mortality. The patient histories were formulated as time series consisting of varying types of clinical events, thus enabling the model to learn increasingly complex temporal dependencies. BERT and XLNet achieved an average area under the receiver operating characteristic curve (AUC) of 75.5% and 76.0%, respectively. XLNet surpassed BERT in recall by 9.8%, suggesting that it captures more positive cases than BERT, which is the main focus of recent research on EHRs and transformers.publishedVersionPeer reviewe

Pohjavesien suojelun ja kiviaineshuollon yhteensovittaminen – Keski-Pohjanmaan loppuraportti

Pohjavesien suojelun ja kiviaineshuollon yhteensovittamista käsittelevä POSKI-projekti oli käynnissä Keski-Pohjanmaan alueella vuosina 2007-2009. Projektin tavoitteena on turvata hyvälaatuisen kiviaineksen saatavuus yhdyskuntarakentamisessa, hyvä laatuisen ja riittävän pohjaveden saatavuus yhdyskuntien vesihuollon käyttöön sekä geologisen luonnon ympäristöarvot. Projektin tuloksena koottua aineistoa sovelletaan Keski-Pohjanmaan maakuntakaavan 3. vaihekaavassa sekä maa-aineslupia koskevasa päätöksenteossa. Lopullinen alueiden käytön yhteensovittaminen tapahtuu maakuntakaavassa sekä kuntien yleiskaavoituksessa. Alueella aiemmin tehtyjä selvityksiä täydennettiin kartoittamalla maa- ja kallioperän kiviainesten määrää ja laatua sekä tarkentamalla pohjavesialueiden luokitustietoja maaperä- ja pohjavesitutkimuksin. Lisäksi täydennettiin arvokkaiden harjualueiden selvitystä sekä inventoitiin kallioalueiden luontoarvoja. Keski-Pohjanmaalla on yhteensä 57 vedenhankintaa varten tärkeää pohjavesialuetta (I luokka), joilla muodostuu vuorokaudessa arviolta noin 73 400 m3 vettä. Vedenhankintaan soveltuvia pohjavesialueita (II luokka) on 13 kpl ja niiden antoisuus noin 10 400 m3/d. Vedenkulutus vuonna 2030 on ennusteen mukaisesti 19 930 m3/d, jolloin n. 24 prosenttia pohjavesivaroista on käytössä. Keski-Pohjanmaan alueella on hiekka- ja soravaroja yhteensä noin 640 milj. kuutiota, josta kiviainesten ottoon soveltuvilla alueilla noin 140 milj. k-m3. Tästä soraa tai murskeeksi soveltuvaa ainesta on noin 15 prosenttia eli noin 21 milj. k-m3. Tutkituista kalliokiviainesalueista 314 osoittautui kiviaineksen ottoon soveltuviksi. Näissä kohteissa on arvion mukaan kalliokiviainesta yhteensä noin 153 milj. k-m3. Kiviainestestien perusteella 7 kohdetta testatuista 23 kohteesta osoittautui I-luokan kiviainekseksi (TIEL 1995). Keski- Pohjanmaan alueella on jäljellä varsin niukasti hiekan ja soran ottamiseen soveltuvia alueita. Tärkeiden ja vedenhankintaan soveltuvien pohjavesialueiden ulkopuolelle sijoittuvat muodostumat ovat jo pitkälti ottotoiminnan piirissä tai niiden aines on raekooltaan liian hienoa. Pohjavesialueilla kiviainesten oton esteenä on usein pohjaveden pinnan yläpuolella olevien kerrostumien pienet kerrospaksuudet.Todennäköisesti kiviainesten otto tulee jatkossa siirtymään kasvavassa määrin kalliokiviainekseen ja korvaaviin kiviaineksiin. Koska murskauskelpoinen harjusora on kuitenkin tärkeä raaka-aine mm. betoniteollisuudelle, tulisi hyvälaatuisen soran käyttöä jatkossa ohjata ainoastaan sellaisiin tarkoituksiin, joissa sen saatavuus on keskeistä

Assessing fatigue and sleep in chronic diseases using physiological signals from wearables : A pilot study

Problems with fatigue and sleep are highly prevalent in patients with chronic diseases and often rated among the most disabling symptoms, impairing their activities of daily living and the health-related quality of life (HRQoL). Currently, they are evaluated primarily via Patient Reported Outcomes (PROs), which can suffer from recall biases and have limited sensitivity to temporal variations. Objective measurements from wearable sensors allow to reliably quantify disease state, changes in the HRQoL, and evaluate therapeutic outcomes. This work investigates the feasibility of capturing continuous physiological signals from an electrocardiography-based wearable device for remote monitoring of fatigue and sleep and quantifies the relationship of objective digital measures to self-reported fatigue and sleep disturbances. 136 individuals were followed for a total of 1,297 recording days in a longitudinal multi-site study conducted in free-living settings and registered with the German Clinical Trial Registry (DRKS00021693). Participants comprised healthy individuals (N = 39) and patients with neurodegenerative disorders (NDD, N = 31) and immune mediated inflammatory diseases (IMID, N = 66). Objective physiological measures correlated with fatigue and sleep PROs, while demonstrating reasonable signal quality. Furthermore, analysis of heart rate recovery estimated during activities of daily living showed significant differences between healthy and patient groups. This work underscores the promise and sensitivity of novel digital measures from multimodal sensor time-series to differentiate chronic patients from healthy individuals and monitor their HRQoL. The presented work provides clinicians with realistic insights of continuous at home patient monitoring and its practical value in quantitative assessment of fatigue and sleep, an area of unmet need.publishedVersionPeer reviewe

Development of medical applications based on AI models and register data – regulatory considerations

Artificial intelligence based methods, especially machine learning (ML), are increasingly used in healthcare for automatic medical image analysis and clinical decision support systems. Development and validation of ML models involve processing of large volumes of personal data. We analysed regulatory impacts on ML based application development especially from the perspective of privacy protection and usage of ML models as a basis for software under medical device regulation (MDR). We present best practices for ML application development and personal data usage in a use case of predicting elderly individuals’ future need for healthcare and social welfare services.publishedVersionPeer reviewe

SPECT Image Features for Early Detection of Parkinson’s Disease using Machine Learning Methods

Millions of people around the world suffer from Parkinson's disease, a neurodegenerative disorder with no remedy. Currently, the best response to interventions is achieved when the disease is diagnosed at an early stage. Supervised machine learning models are a common approach to assist early diagnosis from clinical data, but their performance is highly dependent on available example data and selected input features. In this study, we explore 23 single photon emission computed tomography (SPECT) image features for the early diagnosis of Parkinson's disease on 646 subjects. We achieve 94 % balanced classification accuracy in independent test data using the full feature space and show that matching accuracy can be achieved with only eight features, including original features introduced in this study. All the presented features can be generated using a routinely available clinical software and are therefore straightforward to extract and apply.acceptedVersionPeer reviewe