Could the time it takes for you to water your houseplants say something about your health? Or might the amount you’re moving around your neighborhood reflect your mental health status?

您給植物澆水所需的時間能否說明您的健康狀況？ 還是您在附近移動的金額反映出您的心理健康狀況？

When translated into data and analyzed, these measures could indeed provide insights into the health of people living with chronic physical or mental illness or disabilities.

當轉換為數據并進行分析時，這些措施的確可以提供對患有慢性身體或精神疾病或殘疾的人的健康的見解。

Data science has presented new possibilities for greater independence, improved care and better outcomes. Whether it’s healthcare organizations using data to deliver care more effectively, developers creating data-driven apps that help identify early warning signs of illness, or inventors creating new sensors and devices to detect health issues, all these tools rely on data science techniques to help people living with disabilities or mental illness. Here are some examples of this kind of innovation that I found especially intriguing.

數據科學為增強獨立性，改善護理和改善結局提供了新的可能性。 無論是醫療機構使用數據來更有效地提供護理，開發人員創建數據驅動的應用程序以幫助識別疾病的早期征兆，還是發明家創建新的傳感器和設備來檢測健康問題，所有這些工具都依賴于數據科學技術來幫助人們患有殘疾或精神疾病的人。 以下是一些我特別感興趣的創新示例。

在家中異常檢測 (Anomaly Detection at Home)

People who are able to live mostly independently could benefit from advances in smart home technologies and sensors embedded in their living environments. Anomaly detection is a key data science technique used in many different contexts, such as detecting credit card fraud and monitoring manufacturing processes. Researchers are also working on using anomaly detection to identify deviations from typical behavior for people living in “smart” homes equipped with sensors, such as cameras and computerized pill dispensers that report on medication habits.

能夠獨立生活的人們可以從智能家居技術和生活環境中嵌入的傳感器中受益。 異常檢測是在許多不同情況下使用的關鍵數據科學技術，例如檢測信用卡欺詐和監視制造過程。 研究人員還致力于使用異常檢測來識別居住在配備傳感器的“智能”房屋中的人們與典型行為的偏差，例如攝像機和報告用藥習慣的計算機化藥丸分配器。

In one study, researchers established a baseline for movement and behavior at home by having volunteers complete “Instrumental Activities of Daily Living,” or routine home activities like watering plants or microwaving food. The researchers constructed an activity graph representing how that behavior looked in time and physical space for each volunteer.

在一項研究中 ，研究人員通過讓志愿者完成“日常生活中的工具性活動”或日常的家庭活動(例如澆水或微波烹飪食物)來建立家庭活動和行為的基準。 研究人員構建了一個活動圖，表示每個志愿者在時間和身體空間上如何看待這種行為。

Knowing that baseline, the researchers’ anomaly detection algorithms could identify deviations that could represent cognitive or physical concerns. These researchers plan to integrate this capability into a real-time monitoring application that could show both sudden or longer-term deviations from a person’s baseline behavior patterns at home.

知道了基線之后，研究人員的異常檢測算法可以識別出可能代表認知或生理問題的偏差。 這些研究人員計劃將該功能集成到實時監視應用程序中，該應用程序可以顯示與一個人的基準行為模式在家中突然或長期的偏差。

帕金森氏病的聚類分析 (Cluster Analysis for Parkinson’s Disease)

It’s hard for people with Parkinson’s disease and their caregivers to identify, track and respond to the many different ways the disease can manifest and progress. Wearable sensors and the data they generate could help address this challenge, however.

對于帕金森氏病及其護理人員來說，很難識別，跟蹤和應對這種疾病表現和發展的許多不同方式。 但是，可穿戴式傳感器及其生成的數據可以幫助應對這一挑戰。

For example, this research study explained how a wearable device could gather data for patients and clinicians, including tracking of activity, sleeping, falls, gait characteristics and “freezing” (a temporary inability to move that is experienced by those with Parkinson’s). That data could offer more comprehensive insights than those available in a brief office visit with a medical provider.

例如，這項研究解釋了可穿戴設備如何收集患者和臨床醫生的數據，包括跟蹤活動，睡眠，跌倒，步態特征和“凍結”(帕金森氏癥患者暫時無法移動)。 與在與醫療服務人員的簡短辦公室訪問中所獲得的見解相比，這些數據可以提供更全面的見解。

However, the researchers also point out that better analytic techniques are needed to make full use of this kind of data. They highlight the potential for unsupervised clustering techniques that could make sense of the large quantity of data gathered by a constantly worn device. Their research used t-Distributed Stochastic Neighbor Embedding (t-SNE), which is a way to visualize high-dimensional datasets in two dimensions. The image below shows an example.

但是，研究人員還指出，需要更好的分析技術來充分利用此類數據。 他們強調了無監督群集技術的潛力，這些技術可以理解不斷磨損的設備收集的大量數據。 他們的研究使用t-分布隨機鄰居嵌入 (t-SNE)，這是一種可視化二維高維數據集的方法。 下圖顯示了一個示例。

The image shows the clustering of movement data from a wearable sensor worn by a patient with Parkinson’s. The red Xs represent movement during an “off state” (the term used to describe a period when a patient doesn’t respond well to levodopa medication); the blue dots represent movement during an “on state,” when medication was working well. The clustering approach shows the difference between those times and could help identify patterns in a patient’s movement that would be useful in refining a personalized treatment strategy.

該圖像顯示了帕金森氏癥患者佩戴的可穿戴傳感器的運動數據聚類。 紅色的X代表“關閉狀態”(該狀態用于描述患者對左旋多巴藥物React不良的時期)的運動； 藍點表示藥物正常運作時“開啟狀態”下的運動。 聚類方法顯示了這些時間之間的時差，可以幫助確定患者運動的模式，這將有助于完善個性化治療策略。

精神健康的地理位置 (Geolocation for Mental Health)

Do your movements in your local area reflect your mental health status? What about the number of phone calls you make or text messages you send?

您在當地的活動是否反映出您的心理健康狀況？ 您撥打的電話或發送的短信數目如何？

Researchers have been studying the potential of smartphone data for treating mental illness for some time. Mobility, social interaction and survey responses could all be useful in helping people with mental illness manage and assist caregivers in knowing when an intervention might be timely.

一段時間以來，研究人員一直在研究智能手機數據在治療精神疾病方面的潛力。 流動性，社交互動和調查回復都可以幫助精神疾病患者管理和協助看護者了解何時應該及時進行干預。

One pilot study of an app designed for people with schizophrenia used both active data collection (asking users about their symptoms) and passive data (e.g., geolocation, communication frequency). The data were integrated into a multivariate time series model and could be monitored for days when multiple features were “simultaneously and sufficiently anomalous.” Those occurrences could predict relapse and hospitalization for app users, potentially providing “early warning signs” for caregivers.

一個試驗性研究設計用于精神分裂癥患者一個應用程序的使用主動收集數據(要求用戶對他們的癥狀)和無源數據(例如，地理位置，通信頻率)。 數據被集成到一個多元時間序列模型中，并且可以在多個特征“同時且充分地異常”的情況下監控幾天。 這些事件可能會預測應用程序用戶復發和住院，從而可能為護理人員提供“早期預警信號”。

The researchers noted that mental illness is extremely varied and complex. They suggest that data-driven approaches may be best used in creating a personalized model for relapse, reflecting one individual’s unique patterns, versus trying to create a generalizable model that applies to everyone contending with the same disease.

研究人員指出，精神疾病極為多樣化和復雜。 他們建議，以數據驅動的方法最好用于創建個性化的復發模型，以反映一個人的獨特模式，而不是嘗試創建適用于所有患有相同疾病的人的通用模型。

重要注意事項 (Important Considerations)

If you’ve been reading this and thinking, “But … privacy!” — yes, indeed. There’s certainly a great deal of personal information that is shared to use smart home sensors, wearable devices or smartphone apps as indicators of physical and/or mental health.

如果您一直在閱讀此書并在思考，“但是……隱私！” - 確實是的。 當然，可以共享大量的個人信息以使用智能家居傳感器，可穿戴設備或智能手機應用程序來指示身體和/或心理健康。

One study showed that older adults would be willing to share this kind of data with their primary medical professionals and “most trusted people,” and would be OK with the data being stored short term (defined in the study as up to one week). With regard to mental health, a worrisome 2017 study showed that fewer than half of the 116 available mental health smartphone apps had a privacy policy. A 2018 survey of people with a history of mental illness showed they had strong concerns about the external monitoring of their health, the potential for undesired sharing to social networks, and the risk of exposure of sensitive information to third parties or hackers.

一項研究表明，老年人愿意與他們的主要醫療專業人員和“最受信任的人”共享此類數據，并且可以將數據短期存儲(在研究中定義為長達一周)。 關于心理健康，一項令人擔憂的2017年研究表明，在116種可用的心理健康智能手機應用程序中，只有不到一半具有隱私政策。 2018年對有精神病史的人的調查顯示，他們對外部監控自己的健康狀況，社交網絡中不希望有的共享的可能性以及敏感信息暴露給第三方或黑客的風險感到非常擔憂。

Data science techniques will undoubtedly help address many health challenges, but the public may need a little reassurance about how their data will be used in this effort. Still, the potential for both personalized treatment and widespread insights into a wide variety of diseases is clear and exciting.

數據科學技術無疑將幫助解決許多健康挑戰，但是公眾可能需要在此方面將如何使用其數據方面有所放心。 盡管如此，個性化治療和對多種疾病的廣泛見解的潛力是顯而易見而令人興奮的。

This article was originally published on the Alteryx Community.

本文最初在 Alteryx社區上發布 。