This paper introduces Lynx, an intelligent system for personal safety at home environments, oriented to elderly people living independently, which encompasses a decision support machine for automatic home risk prevention, tested in real-life environments to respond to real time situations. The automatic system described in this paper prevents such risks by an advanced analytic methods supported by an expert knowledge system. It is minimally intrusive, using plug-and-play sensors and machine learning algorithms to learn the elder’s daily activity taking into account even his health records. If the system detects that something unusual happens (in a wide sense) or if something is wrong relative to the user’s health habits or medical recommendations, it sends at real-time alarm to the family, care center, or medical agents, without human intervention. The system feeds on information from sensors deployed in the home and knowledge of subject physical activities, which can be collected by mobile applications and enriched by personalized health information from clinical reports encoded in the system. The system usability and reliability have been tested in real-life conditions, with an accuracy larger than 81%.
Ambient Assisted Living (AAL) [
Health knowledge about the elder’s state is the starting point to identify behavior patterns and to assess his/her status like taking medication, activity recommendations, social interactions, early memory loss, disorientation, falls at home, and symptoms of weakness, tiredness, or fatigue. It can be stated that we are looking for an Intention Detection System. Various experiments on AAL environments have demonstrated the possibilities and the complexities of intention detection [
The main objectives of Lynx, the system described in this paper, are as follows: Develop a knowledge management system able to store and understand the user clinical status, activity, context, and situation aware, allowing to integrate this semantic information in the intelligent system, in order to detect health abnormal events. Create intelligent monitoring services of an elder and his medical issues, so that the system adapts to him, creating automatically rules that determine the usual values for each individual and evolve with the subject under monitoring, so that they are always up to date. These rules allow launching fully customized alerts without human intervention. Create a telecare third-party system based on an expert system and an inference engine that can automatically detect dangerous situations decreasing false positives, firing events only at abnormal circumstances.
The critical task of the system is the automatic profiling of user behaviors. Several systems were introduced in recent years to address elder-care issues, principally fall detection systems [
The remainder of the paper is organized as follows. Section
Context Awareness (CA) [
The Lynx system is designed to fulfill two main requirements. First, the extraction, transformation, and load of sensor information are to be carried out in a simply way: the sensor is plugged on the network, and its raw data is automatically integrated in the platform. Second, the platform must be able to measure the elderly’s habits in order to track his behavior to find deviations from their daily tasks (wake-up times, sleep habits, diary strolls, etc.) or regarding their health situation and provide a detailed summary to the caregivers and the family about their evolution.
Figure Data Capture from Heterogeneous Sensors. Relevant Electronic Health Record Evidences. Central Ontology and Expert Rules System. Intention Detection System. Anomaly Detection System.
The overall operation of the system represented in Figure
Lynx artificial intelligence platform over real housing with care systems.
The main component of this platform at the gateway level is the so-called Home Box Service (HSB), a structured software system, flexible and hardware platform independent, which is the system kernel processing the remote data received. Regarding the technologies involved, we use the UPnP [
Lynx has been admitted in universAAL [
Caring for elderly requires a multidisciplinary approach and may include monitoring of the health status of elderly and the management of chronic conditions and the inclusion of one or more medications prescribed for regular use. This complex context must be taken into account in a telecare system [
Semantic networks implemented in ontologies allow for concept disambiguation [
Nowadays, electronic health records (EHR) store most of the patient evolution information in natural language reports (80% of relevant information), written by the medical personnel, so that the percentage of structured information containing quantitative data is minimal. EHR systems can be combined with Diagnostic Support Systems (DSS) to allow order entry and provide alerts on errors of drug dosage, allergies, and contraindications, guided selection of diagnostic tests, reminders for procedures, tests, visits, computer-assisted diagnosis, and trend analysis. The extraction of the information is not an easy task and requires the development of correlation, noise reduction, and inference algorithms. The standardization of EHR is not a trivial problem. The coding of data within EHR and clinical databases is essential for sharing and exchanging across heterogeneous systems. Challenges to adoption of terminology standards [
It is not easy to identify the principal diagnosis or disease in natural language over EHR fields. There are descriptions about the family history, secondary diagnosis, and a constellation of comorbid diseases. We have developed a preclassifier to detect the main disease and diagnosis, the principal medical procedures, treatments, and medications impacting directly in the user’s life, embedding it into the teleassistance platform as a new indicator. Normal age-related changes may be accompanied by chronic health problems such as diabetes or heart disease. Managing many such chronic conditions may include one or more medications prescribed for regular or daily use. Combined, these factors increase the complexity of telecare systems [
Data capture from clinical records.
There are mathematical techniques that are used to capture the semantic structure of documents based on correlations among textual elements within them [ To extract information from natural language medical reports in EHR that comprise a broad number of medical context and a great variability among the different medical fields, countries, and even hospitals. To define a new model that is able to comprise, normalize, and structure the information contained in the different clinical reports generating a unique structured descriptor in a standard format. To enrich the information from other sources, both external and internal, of heterogeneous data. To compress the generated models and descriptors to support compactness and real time response and to improve inference strength in semantic summaries. To develop a semantic ontology model that is able to structure, normalize, enrich, and compact the written clinical histories, test reports, and clinical records regardless of the medical discipline, country, language, hospital, and practitioner. This task will focus on the modeling of relevant information for clinical practice applications, for instance, the integration with other clinical collected data, and use this information for search new medical researches. Our key involvement focuses on linking to clinical IT and to the knowledge collected in clinical practice.
The system takes into account the correlation of all the concepts in the clinical records about the primary diagnosis of the patient, by calculating the different concepts vectors with the “tf/idf” frequency algorithm. Later, we run a “map” process which will analyze the direct correlation of secondaries variables with the principal concepts, extracting their relationships and recoding them under the Unified Medical Language System (UMLS) Metathesaurus. The UMLS Metathesaurus is a large, multipurpose, and multilingual vocabulary database that is organized by concepts. The current release comprises more than 1.5 million biomedical terms from over 100 sources. Synonymous terms are clustered together to form a unique concept or cluster. Concepts are linked to other concepts by means of various types of relationships, resulting in a rich graph. The Semantic Network provides a consistent categorization of all concepts represented in the UMLS Metathesaurus as well as information about the set of basic semantic types, or categories, which may be assigned to those concepts. Lynx Semantic Network contains 133 semantic types and 54 relationships.
Sharing common understanding of the structure of information among people or software agents is one of the most common objectives developing ontologies [
First, we built a large general medical ontology (illustrated in Figure
Medical observations in the summaries ontology.
Figure
The general algorithm to create summaries has the following steps: Perform the text preprocessing steps: stemming, stop-list, and spell-checking, either correcting or removing strings that are not recognized. Use MetaMap [ Use UMLS relations to create first-order classes, adding only those types of relations that lead to improvement of classification results. Annotate the concepts on the summaries ontology with a set of rules joining automatically pair concepts with their relationships or properties.
Table
(a) Diagnostics. (b) Treatment and diagnostics.
D | 4755 | 4270 | C0007124 | 1 | dcis | neop | Noninfiltrating intraductal carcinoma |
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D | 4755 | 4279 | C0337354 | 1 | Quadrantectomy | topp | Quadrantectomy of breast |
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|||||||
D | 4755 | 4280 | C0222600 | 1 | Right breast | bpoc | Right breast |
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D | 4756 | 4298 | C1176475 | 1 | Ductal carcinoma | neop | Ductal carcinoma |
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|||||||
D | 4756 | 4299 | C1318216 | 1 | 4 | qnco | 4 |
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D | 4756 | 4300 | C0237753 | 1 | 4 mm | GATE_length | 4.0 mm |
D | 4757 | 4303 | C0007097 | 1 | Carcinoma | neop | Carcinoma |
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|||||||
D | 4757 | 4308 | C0450367 | 1 | 4.5 | qnco | 04-may |
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|||||||
D | 4757 | 4309 | C0237753 | 1 | 4.5 cm | GATE_length | 4.5 cm |
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T | 4757 | 4311 | C0543467 | 1 | Surgical | diap, topp | Operative surgical procedures |
The use of a No-SQL database to store Resource Description Framework (RDF) and Web Ontology Language (OWL) descriptions makes it possible to query, manipulate, and reason about the data with standard tools, such as OWL reasoners and languages (e.g., the SPARQL Query Language for RDF).
The EHR typically contain description of different episodes written in natural language by the medical staff; often they are multilingual (English, Spanish, etc.) with their diagnostic impressions, treatments, procedures, and so forth [ For indexing, there are public tables with “stop-words” (not relevant words) and some dictionaries to help in the automatic translation between concepts (one or more words regarding a medical disease, procedure, treatment, drug or observation) and codes, scripts errors, abbreviations, or names of very local concepts (e.g., Rt is radiotherapy). However, these codes are plain; that is, their importance is based on the frequency, assigned to a group, and basically, it is proportional to the way of occurrence of the terms in each document or group and inversely proportional to the appearance of such terms where they have the whole information set. Figure Each UMLS concept belongs to a hierarchical class, that is, the key of the Lynx system. There is a statistical model that calculates the weight of the episodes analyzing the “td/idf” frequency of each concept into an episode, taking into account the weight of the class to which each concept belongs. A diagnosis does not have the same weight compared to a treatment, a part of the body, a drug or compared to different combinations of different concepts. With our weighting algorithm the Lynx system selects all entries and chooses the most relevant episodes, and, within them, it selects the main and secondary diseases for each episode (see Figure
Absolute frequency of terms found in clinical records.
Grouping by principal diseases.
So, the annotation process is as follows: Extract relevant tokens from sentences along different episodes in the whole health record (stop-words, dictionaries, translates, UMLS codes, etc.). Assign the UMLS concepts to their principal classes. This research requires a proper mechanism to select them in order to support further tasks like adding semantic rules by means of Semantic Web Rule Language (SWRL) and launching inference, setting bindings to terminologies, validating archetypes, and so forth. Thus, it should be noted that describing constraints as human-readable comments should be avoided as far as possible given that they cannot be used by semantic reasoners. Calculate the weight of each episode of the health records and the weight of each concept into the episode. Calculate, from each set of sentences, when the text is talking about the same diagnosis, treatment, or procedure and grouping sentences by principal or secondary diseases. annotate only the relevant information, present to the medical staff only the relevant relationships between concepts, analyze unknown relationships between diseases, procedures, treatments, and personal patient data as familiar issues, gender, age, demographic or economical situations, personal context, drugs, and so forth, detect unknown relationships between different episodes along the time; the statistical models, mainly on clinic records, lose knowledge if they do not take into account the evolution over time. Time evolution is a variable which has not been deeply studied within repetitive sets of data in the medical domain. the Lynx system uses machine learning with sliding windows to solve this issue [
With this plain information, we are able to transform the concepts into a hierarchical (semantic) format, in order to
Once the concepts are grouped by primary and secondary diseases, treatments, or procedures and extracted from a relational database, some semantic rules are executed to convert in triplets (sets of subject-predicate-object) the relationships found in the different principal groups. In addition, each triplet is assigned to a principal disease, if it exits in the group, or to a secondary disease, depending on its content. Each concept in the clinical record belongs to a UMLS class (see examples of hierarchies in the table of Figure
Minimal relevant gain between principal UMLS classes.
Each unique class or pair of semantic types is manually assigned to an annotation rule. Figure
Annotation rule system.
So the system is able to recognize the semantic types and to infer new semantic instances that are the real annotations in the ontology. The system uses a reasoner engine (Pellet), so once all the semantic instances relating to the same group of diagnosis, treatment, or procedures are recorded in a temporal memory, the system runs several rules to determine if the triplet within that group belongs to an earlier principal disease, that is, a new main diagnosis or a secondary diagnosis. Finally, all the triples processed are stored in the no-SQL structure and can be used directly for consultations or queries by medical practitioners, almost in a natural language format in the style of queries that can be similar and linked to other platforms like “LinkedLifeData” [
Reasons for sharing and reusing semantic (SWRL) rules include the ones listed below: Interoperable decision support: the ability of systems to reliably communicate with each other regarding clinical decision support, to encourage the development of interoperable mechanisms for triggering critical aids to decision making like alerts, reminders and monitoring tasks that improve effectiveness and reduce clinical risks. Inheritable compatibility: given the archetypes’ capability of being defined as specialization of more general archetypes, a SWRL rule originally designed according to the OWL version of a parent archetype is also applicable to derived archetypes. Fostering semantics for clinical guidelines: the introduction of SWRL rules and inferential mechanisms together with the archetypes expands the boundaries of the declarative knowledge that can be migrated from clinical guidelines to healthcare information systems. In this way, a means for standardized representation, reuse, and execution of the essential fragments of declarative knowledge contained in clinical guidelines is provided. Specialists’ empowerment: to enable domain rules and guidelines to be modeled in a formal way by domain experts. By defining the declarative knowledge they work with, they can gain direct control over their information systems. Consistency checking: rules integration can offer consistency checks to help guaranteeing data correctness in EHR fragments. Archetype validation: to support archetype validation and inconsistent restrictions detection. Full semantic interoperability: for all abovementioned reasons, integrating rules with clinical archetypes and EHR is an essential step towards extracting relevant information into summaries.
The advantage of the system is that the rules for concept weight calculation and for creation of relationships between concepts, joined to the rules that determine whether a disease, treatment, or procedure is the principal or not in the context of the patient, are fully configurable by the medical staff, without computer, statistical, or semantic knowledge, therefore, in a very dynamic, elastic way, allowing a variation of different medical contexts. Figure
Clinical record in a semantic store (semantic tool view).
The ultimate goal of automatic summaries is to feed the telecare platform with the most relevant clinical data obtained by an unassisted way from medical summaries and move the therapeutics procedures, treatments, or medical recommendations from medical summaries to universAAL ontology. So, the anomaly engine and the predicting intentions engine are capable of learning about personal living habits of the patients, highly correlated with their clinical conditions and prescriptions. To build this integration, thanks to semantic annotation process, we need only to join the most relevant concepts (principal diseases, diagnosis, treatment, and procedures) in the control platform (see Figure
Joining summaries and central ontologies.
Expert systems represent formal knowledge to solve human problems. This type of systems is applicable to any domain and is present today in nearly any application that requires high computational cost to automatize processes with some reasoning. Expert systems are suited to specific tasks which require a lot of knowledge derived from a particular domain experience as diagnostics, instructions, predictions, or advice to real situations that arise and can also serve as training tools, mimicking the human behavior.
By their nature, the Semantic (Knowledge-Based) Management Decision Support Systems (MDSS) work over structured data representation (schema). The knowledge is persistent in data-storages, and the expert knowledge (system rules) is heuristic evidence based rules, with reasoning capacity using an inference engine. This means that the rules are well-known and always are true (an uncertainty factor in the MDSS systems by their definition is not possible, in principle). The use of the Resource Description Framework (RDF) standard (and thus its associated representation machinery such as RDF schema and OWL) offers the possibility of making inferences when retrieving and querying information, in a way very similar to human natural language, being the advantage in query-answer systems. Although OWL automated reasoning does not scale up for use in large knowledge bases, researchers and practitioners have just begun to explore the problems and technical solutions which must be addressed in order to build a distributed system. On the other hand, there are nonknowledge-based MDSS, which learn from raw data (semi/unstructured) and are based on probabilistic techniques: patterns are taken as examples or cases in the past and the system has learning and probabilistic prediction capability. The direction of the last researches [
“Semantic smoothing for language modeling” emerged recently as an important technique to improve probability estimations using document collections or ontologies, and this was the way followed to design this system. This is the technical way in this project. In Figure Fill the ontology with the sensor and clinical records raw data by means of “process rules.” Since these rules are easy to adapt, alter, and maintain, this feature makes them an attractive solution for nonexpert caregivers. The caregiver is able to directly define and modify the rules that specify the behavior of a system in a given situation. For example, context-aware behaviors could be specified by a rich set of rules. In addition, the use of rules on top of ontologies can enable adaptive functionality that is both transparent and controllable for users [ Transformation of raw data into complex concepts, for example, “at noon in the kitchen over half an hour, the patient is eating.” Avoid data processing outside certain limits or thresholds set by the caregiver. Simple warning rules: more than 2 hours in the bathroom throws an alert. Medical inferences: “if cholesterol is more than a threshold, the patient is suffering from hypercholesterolemia.” Execute expert rules (see Figure
Ontologies and rules in the expert system.
Both set of rules are running over the ontologies with a semantic reasoner (Pellet), and the main advantage of the system is that it is open to future development: the functions are out of the kernel code, and the program flow or the alarms system can be modified changing the rules; that is, it is not necessary to change the code of the system kernel. So, this can be done by an expert who does not necessarily have to be a programmer. Defining personalized and adaptive elder interaction/behavior models is a key challenge when considering the issue of analyzing or predicting elder intentions. The intention aware elderly care application can predict the activities of the elder based on historic usual activity, profiting automatically the elder usual activity information. So, the system has a hybrid “inference engine” that joins heuristics rules (first conditions to check without elder data) mainly with a supervised system, in order to discover new rules, adapting the heuristic rules to the personal behavior changing, with an unsupervised system, with two objectives: Detect unknown alerts (not registered in heuristics nor supervised system). Join similar users’ behaviors in homogeneous groups (segment usual activity, customizing automatically the user usual activity, state, or profile), in order to send new extracted knowledge to the medical and care agents about the general behavior of their patients. Step Step Step Step Finally, back to Step The system infers whether the elderly is eating at a specific time of day. The indoor localization system is aware of the time of day and the amount of time that the elderly stays in the kitchen. Electrical appliance usage is also taken into account to infer if the elderly has been preparing his/her meal. Contact doors on kitchen furniture is also used to track this situation. During the day, the system infers whether the elderly is doing the housework. The indoor localization system is aware of the time of day and the amount of time that the elderly spends tracking his/her position. Vacuum cleaning can also be detected. Physical exercise like hiking or strolling can be detected through the GPS-enabled smart-phone of the elderly. The position, velocity, and path will be analyzed to measure the amount of kilometers of each day. Current activity: it is the activity that the elderly is performing at the present time. Last 24 hour activities: they are the activities performed during the last 24 hours split into 15-minute time slots. Next 15-minute forecast activity: the next activity that will perform the elderly based on the historic data of the elderly intention is inferred. This horizon was determined in a heuristic way, since 15 minutes is much more than enough for the purpose of the system, and the higher the horizon, the lower the accuracy (in this and in all prediction systems).
In general, after the home installation, the inference machine runs the following tasks:
Human actions are influenced by context, knowledge, or experience of dependencies between actions and by expectations of how the situation is going to develop [
The “Activity Tracker” system provides three services for the elderly activities intention aware API:
This service provides the likelihood of the tracked activities to happen and takes the activity whose probability of happening is higher than the other ones.
The main task of the Intention Detection System is the development of decision trees that are running on central server and, based on information collected by different sensors deployed in the home, make decisions about the status of the patients, depending on their health situation (extracted from clinical records), treatment procedures, level of stress, behaviors, risk of exposure to harsh circumstances, and so forth. To do this, as exposed in Section
As each home user could adapt differently to his environment and could have a recent history of actions that involve a danger due to accumulation of negative elements that can affect him, it is important that the general rules generated by experts adapt automatically to each user inside the platform with implicit knowledge, so the system will personalize the information to each patient, their current parameters, and their last actions, allowing elimination of the false positives. These supervised models exploit the reasoning power of expert system to derive new knowledge and facts. The Intention Detection System reasons over the base knowledge to infer new facts, resolves context conflicts, and maintains the consistence. The situation of a user is derived from his personal context, but the context is derived from the aggregation of all the user’s situations plus the environment situation, too.
Through these techniques of intelligent information processing, we are given special emphasis on the detection and prediction of anomalies (trend analysis, deviations in the data, etc.), such as lifestyle changes and poorly executed exercises.
One easy way to detect changes and behavior anomalies is to compare the actual situation or state of the elderly with one prediction of his state. In this paper, is proposed a 24-hour sliding window that is analyzed with a decision tree (a supervised machine learning algorithm), in order to predict the next user action over the time. For each time window, the decision tree takes into account a multivariate set of values generating a predictive model (decision tree) and extracts the next status of the user, with confidence and probability levels.
Compared to the current technology for evaluating context-aware systems, we focus in particular on the quantitative evaluation of each one of the rules of our rule-based system with a temporal data set. The challenge of this proposal is a novel and distinctive base technology repository that has been developed in the treatment of time series and another algorithm repository for rule generation based on probabilistic rules directly from RDF semantic systems, without human knowledge, and automatic insertion of these rules in the central ontology again, assigning weights to the semantic confidence (triple-stores), in order to customize the personal behaviors of each user. The described system is proposed as a new development and addressing mode of the current telecare systems taking into account the characteristics and preferences of each person in such a way that a personal behavior is built. For example, Figure
Personal behavior model per user.
Usually, interest driven analysis tends to overlook unexpected patterns in data. To avoid this inconvenience the system contains unsupervised algorithms (clustering and association models). Data Mining deals with applications such as Anomaly Detection to prevent excessive consumption, pollution, escapes, and, in general, abnormal patterns of what an expected profile for each segment is.
To detect anomalies, we use the Local Outlier Factor (LOF) algorithm [ Statistical models: they are based on the field of statistics, given the premise of knowing the distribution of data. Based primarily on measurements of distances between objects, the greater distance of the object relative to others is considered an anomaly. Region density distances: based on the estimation of density of objects, the objects located in regions of low density and relatively distant from their neighbors are considered anomalous. The main feature is that generally it is considered unsupervised learning and a score is assigned to each instance that reflects the degree to which the instance is anomalous.
One of the tools necessary for the Anomaly Detection is clustering, which is to group a set of data, without predefined classes based on the similarity of the values of the attributes of different data. This grouping, unlike classification, is performed in an unsupervised manner, since it is not known beforehand of classes of the training data set.
The algorithm used to carry out the segmentation process takes as an input the data set (sensors data and clinical history) and the cluster model that was generated by a clustering algorithm (
Anomaly Detection System.
Since the beginning of 2015, 60 homes have installed the system, checking online the users daily living over three different customer: dependent elderly people, elderly whose habits are worsening due to aging, and elderly people who are suffering the first symptoms of dementia. As previously exposed, the expert rules are running since the moment of the installation, but after a month capturing raw data, the system begins to obtain the behavior of the elderly. The steps through which the system gets their behavior are the following: At a first stage, the sensor raw data are processed by the expert system to determine which is the elder’s context at every time, formatting the data into a structured table with the information about person, date, hour, and the stage in that moment regarding the user: Sleeping (S), Cooking (C), Eating (E), Doing Housework (D), Outdoor (O), Outdoor Sport (U), Using Tablet (T), Using Mobile (M), or Spare Time (P). At the same time, the system checks the physiological status of the users, as temperature, heart pulse, blood pressure, and so forth. In this way the data will be managed more compactly and only the relevant information to the alerts is managed (e.g., “the elderly has a fever unusual at 08:02 more than 15 minutes”). Initially the thresholds that are in the table are defined manually by the doctors (e.g., a temperature of 36,5°C), leaving the detection of statistical thresholds for later, where the average temperature threshold is modified by historical and statistical processes and unsupervised algorithms. These thresholds are able to modify the medical rules directly regarding the user personal historical set. The system also checks environmental sensors, such as smoke, temperature, and humidity, throwing alerts when activated. At every moment, the system is taking external data to integrate them into users data, with a number of values: Haze (C), Fog (N), Low Fog (N), Fog (I), Precipitation (P), Drizzle (L), Rain (U), Torn Rain (V), Tornado Sight (R), Rain Shower (H), Rain (E), Snow (E), Shower Hail (T), and Freezing Rain (T).
The first conclusion, as expected, is that the outdoor weather conditions are the most relevant in order to predict which will be the user behavior, in second correlation place (by a Chi Squared statistical test), after the hour of the stage, but before other indicators such as the day week, or even the month of action. With the temporal sliding window method, including the last action in order to predict the new stage of the user, the accuracy trust by the system using cross-validation is about 81,80%, as shown in Table
Confusion matrix with previous stages.
Accuracy: 81.80% | ||||||||||
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True S | True P | True E | True T | True O | True C | True D | True U | True X | Class precision | |
Pred. S | 7404 | 270 | 9 | 2 | 74 | 23 | 0 | 0 | 3 | 95.11% |
Pred. P | 313 | 2318 | 136 | 51 | 66 | 207 | 23 | 2 | 0 | 74.39% |
Pred. E | 16 | 175 | 851 | 84 | 6 | 68 | 124 | 0 | 0 | 64.27% |
Pred. T | 0 | 73 | 17 | 994 | 126 | 27 | 6 | 38 | 1 | 77.54% |
Pred. O | 0 | 87 | 44 | 6 | 843 | 119 | 1 | 72 | 14 | 71.08% |
Pred. C | 36 | 197 | 244 | 42 | 3 | 1321 | 56 | 0 | 0 | 69.56% |
Pred. D | 2 | 25 | 0 | 83 | 8 | 75 | 345 | 0 | 0 | 64.13% |
Pred. U | 0 | 28 | 2 | 0 | 59 | 33 | 2 | 242 | 5 | 65.23% |
Pred. X | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 2 | 33.33% |
|
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Class recall | 95.28% | 73.05% | 65.31% | 78.76% | 71.08% | 70.53% | 61.94% | 67.79% | 8.00% |
Thus, it is shown that external data on local weather and data from past actions are representative to make predictions about the future status of the elderly immediately. Thus, if the prediction state does not match with the actual state and this situation has a significant score, the system sends an alert to remote care services to immediately launch protocols. On the other hand, the anomaly clustering method used is a complement to the supervised system, in order to detect not so usual behaviors.
In this paper, we have introduced a behavior prediction system to be used in an home telecare platform. Such platform is an assisted living system for monitoring elderly people at their homes, with the final aim of providing a robust, easily deployable, and cost-contained solution to ensure the safety of the elderly. It obtains both physiological and environmental data through a multisensor infrastructure, connecting the home with both the carer and the family, being aware of the state of the elderly.
Our next steps in the closed future are two: on one hand, to transform this validated system into a reference product in the market of home telecare platforms and, on the other hand, reinforce the intelligent systems for analysis of anomalies and behaviors taking advantage of the health information to detect how the clinical records, medical diagnoses, and treatments are affecting the usual behavior of different patient profiles in their daily lives, specially, at home, and if these treatments are appropriate or not.
Finally, as a medium term step, we are researching on the challenge of creating a new method for automatic big data analysis over the medical summaries (obtained from clinical records, usually written in plain text natural language), in order to discover new relationships between diseases, medical procedures, treatments, and their correlation with home personalized medicine.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The research was supported by the REAAL Project (CIP ICT PSP – 2012 - 325189), the ASK-MED Project (Aggregated Semantic Knowledge Medical), a Gaitek Program by The Basque Country Government. Thanks are due to the Onkologikoa (